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The Power of Consumer to Business (C2B) Matching
Episode 10 ›
Sales and Employee Segmentation
Episode 17 ›
Improving Match Accuracy and Confidence
Episode 5 ›
Master Data and Governance: It’s Not Scary
Episode 9 ›
The Critical Importance of Data Context
Episode 19 ›
Navigating Data Technology Migrations
Episode 20 ›
D-U-N-S® Number Recertification
Episode 11 ›
What COVID-19 Taught Us About Matching
Episode 18 ›
Dealing With Duplicates
Episode 6 ›
How Referential Sources Reduce Data Bias
Episode 7 ›
Domain (URL and Email Address) Matching
Episode 22 ›
Leveraging the Match Data Profile
Episode 23 ›
The Life and Death of a Business
Episode 1 ›
The Importance of a Name
Episode 2 ›
The D-U-N-S® Number Speaks Out
Episode 3 ›
Why Match Accuracy Is Critical
Episode 4 ›
Identity Resolution
Episode 8 ›
The Impact of Hierarchies
Episode 12 ›
Extended Linkage Insights (ELI)
Episode 13 ›
Getting the Most From Your Data Partner
Episode 15 ›
Finding Your Organization’s Data Truth
Episode 16 ›
Assessing MDM Software Choices
Episode 21 ›
Improving Match Rates With Multi-pass Matching
Episode 14 ›
The Power of Consumer-to-Business (C2B) Matching
One of the greatest assets we have at Dun & Bradstreet is our Data Advisory Team. This team of experts advises clients on data, technology, and analytics best practices, and with an average of 20 years’ experience advising customers at Dun & Bradstreet — plus previous experiences — these seasoned professionals add tremendous value for our clients. One of my favorite aspects of being a part of this incredible team was our weekly meetups to catch up and review the different projects getting worked on and the challenges they posed. Although we work on different use cases across industries, we noticed that we were having many of the same conversations with our clients as we explained the value of the Dun & Bradstreet data and services. We started to think about how valuable those conversations end up being to our clients. Then one of us had a brilliant idea: “If we enjoy these conversations so much, why not bring them out into the open and make them an asset for everyone to benefit from?” That’s how the idea for the conversation series known as “Data Talks” was born. From there we compiled a list — an inventory of sorts — of the types of conversations we were having. With so many of our experts being able to speak to these topics, one of the toughest parts was figuring out whom to feature and how to limit our conversation to a modest 15-ish-minute discussion. The result? Hours of content explaining some of the most relevant topics we encounter, sharing best practices and thought-provoking concepts. Anyone who manages or consumes data should find something of interest here to improve their skills, advance their data journey, and grow their business. I hope you enjoy the content!
Sr. Director of Product Management for Data Management and Data Talks Program Host Have an idea for a topic? Shoot me an email.
George L’Heureux
Data related to the life cycle of a business can guide how that business interacts with its customers, suppliers, and prospects. For example, you may want to work differently with a startup than you would with an established multinational company or a company that’s teetering on the brink of closure. But life cycle data isn’t necessarily easy to come by; one needs to understand the right signals and make the relevant connections.
“Understanding the data and signals that a business generates and relating that data to where that business is in its life cycle can help your team use resources more efficiently, reducing costs and risks and maximizing opportunities. Ultimately it can help you avoid risk, be more effective, and perhaps be more profitable.”
“I help our customers maximize the value of Dun & Bradstreet data in conjunction with the data assets they have within their organization. By piecing together these items, we can help a company be more efficient and productive.”
The business name we see on the sign over the front door isn’t necessarily the business’s legal name. In fact, a company can conduct business under multiple names: its legal name and multiple trade styles. Therefore, it’s important to understand with whom you’re working and with which business name you’re working based on your use case in order to aggregate it all under the same record and reap the benefits.
“Using a company name may not be the best way to identify a record. A better way is to use a unique identifier such as the D-U-N-S® Number, which can let you identify a record and get all of the names for that company, whether it’s a trade style or a legal name, so that when you’re conducting business and making decisions, you can have the appropriate information, and the appropriate name, for your use case.”
“I work with our strategic customers in the insurance vertical to help them ingest Dun & Bradstreet data, understand it, consume it, and use it to make better decisions. Whether they’re small organizations or Fortune 500, I help them mitigate risks within their accounts receivable portfolios, streamline workflows, and use data to improve their sales and marketing strategy. I also help customers understand suppliers, analyzing risk and spend to create efficiencies within their vendor portfolios.”
Businesses and governmental agencies around the world rely on the D-U-N-S Number, the unique nine-digit number assigned to a business entity. This seemingly simple number is actually quite powerful: It is the primary data key for businesses around the world, and it plays a significant role in their master data architecture by serving as a unique identifier in the capture and storage of information relating to their customers, partners, and suppliers.
“The D-U-N-S Number is the most unique and powerful identifier in the business world, and it’s your key to unlocking the potential of the Dun & Bradstreet Data Cloud! The D-U-N-S Number, and all its bounty, can empower your own master data program and is your entryway to a better understanding of companies, including their linkages and corporate hierarchy, i.e., their family tree.”
“I manage a portfolio of strategic accounts in the financial services and insurance industry, working with clients in both pre- and post-sales, supporting them from strategic to tactical solutions by providing expert levels of data knowledge and best demonstrated practices. I work holistically with our Technical Advisory and Analytics Advisory teams to ensure our clients receive the most comprehensive subject matter support for their data solutions to make sure they’re getting the most value out of their Dun & Bradstreet solutions. I also assist customers with match optimization efforts to ensure my customers can maximize their automated match acceptance rates based on their specific use case.”
Tim discusses the basics of matching — what it is and why it’s important — and how Dun & Bradstreet helps our customers understand the connection between their data and Dun & Bradstreet data, which contains information on hundreds of millions of global businesses in the Data Cloud.
“Matching is the start of an organization’s data journey. During the matching process, it’s important for our customers to feel confident that they’re being presented with the best record we have. For that to happen, we need to work collaboratively to ensure they’re providing the highest-quality input data to maximize their results.”
“I work with our clients in the financial services vertical, specifically insurance. Collaborating with our sales representatives and customers, I support their use cases through education, working through match files and hierarchies, and explaining our data best uses to support their business.”
Match confidence is our way of expressing how confident we are that we found the right match, to help customers understand that the match project is going in the right direction. In this episode, our experts discuss some of the things you can do to get the best results from your matching exercise and increase your confidence.
“We often see data that includes test records or invalid data elements, so it’s important for an organization to review its data before submitting it for matching. Clients can talk to their Dun & Bradstreet Client Director about the services we offer to help review the quality of their input data!”
“My role with Dun & Bradstreet is to ensure our customers recognize the full value of the data they license from us and to help make that data actionable. Mapping their business data to the Dun & Bradstreet Data Cloud (i.e., “matching”) is the critical first step in this process, and educating our customers on the matching process is essential for them to be able to leverage the full value of their Dun & Bradstreet services.”
Just like incomplete and inaccurate data, duplicate data can have negative implications for sales, marketing, and finance teams — really for everyone within the organization who uses data. There are many causes of duplicate entries, including human error, subjectivity in entry, and collection from multiple sources — it’s even the result of a well-intentioned program. But reasons aside, there are things that we can do to fix duplicates once they occur and prevent them from happening in the first place.
“Understanding the root cause of and creating an approach to solve for duplicate records should be a priority of a data management program. One solution that an organization can use is the D-U-N-S Number. It can play an important role as a unique identifier to address the duplicate dilemma.”
“I’ve progressed through a lot of positions in my tenure. Starting in our delivery organization, I understood how clients asked for our data and actually used our data. Then I shifted into our data content organization, which gave me new perspectives on how we ingest data to drive quality and completeness of the Dun & Bradstreet Data Cloud. I consider myself to have a holistic view and understanding of our data from both the customers’ and the vendors’ perspectives.”
While it’s possible for an organization to function with its internal data alone, it may not be the best strategy. Relying on internal data alone can be shortsighted, and even worse, it can lead to bias. Think of it this way: There’s what you know, what you don’t know, and what you don’t know you don’t know — that’s what can really hurt you, and that’s where referential data comes in.
“Look at an ad hoc request for data from within the organization as a red flag. It’s usually a symptom of a larger problem. Businesses need to really look into the benefits of bringing referential data into their data ecosystem on a consistent basis. Not only does this strengthen our internal data practice, but it may also widen our view of the world outside our organization’s walls.”
“I was a Dun & Bradstreet customer for about 10 years before joining the team as a Principal Data Advisor. One way that I see our role is to share our thought leadership inside and outside of Dun & Bradstreet, whether through articles or client consultations. The most important thing that I’ve come to know about our role is to think about what else is possible for data, to elevate not just the role of data, but the usefulness of data for decision-making.”
Matching, aka identity resolution, is the exercise of connecting your organization’s data to a trusted set of commercial reference data. Matching is a critical and worthwhile step, whether you’re doing basic data cleansing, cleaning up your CRM, or augmenting your data with the richness of third-party data assets. In this episode, we discuss how we help clients match their data to the Dun & Bradstreet Data Cloud and provide a D-U-N-S Number — the unique business identifier — to bring the richness of the Data Cloud into the customer environment, including business demographics, firmographics, predictive indicators, and more.
“The Dun & Bradstreet Data Advisory Team has a deep understanding of our match reference data and metadata. Our experts have been involved in many, many engagements from a data stewardship standpoint, so our approach can really help guide customers with this process and bring added value to their organization’s data.”
“I’ve spent my entire career at Dun & Bradstreet in positions ranging from financial and credit analyst to participating on our product teams. However, I’ve spent the most significant amount of time in an implementation role working directly with our customers, helping them with integration and consumption of Dun & Bradstreet data into their own environment.”
Why is there so much concern, frustration, and worry within organizations about terms like “governance” and “master data”? Perhaps it’s because these terms connote authority, policy, protection, rules, and regulation. It’s time to think about these terms in a more positive light. Governance doesn’t have to feel restrictive; rather, it’s an enabler of consumption. Master data is about connections. Like the Rosetta stone, it’s a capability that enables languages and countries to speak and engage with one another. Similarly, when you’re bringing data together as a collective whole within a business, you want to consider how to make the data connect with some kind of common focus — a common key that can help drive that enablement and have that power of connection.
“Rather than worry about terms, we should be staying focused on our journeys, particularly the who, what, and why. Make sure that your company has a reason, a need to look at its data and use it in a way that’s going to help progress the business. Then think about the strategy and how you are going to get there. Last, determine who is going to own it.”
“I lead a team of incredibly experienced data experts. Our role is to work with customers who are trying to turn their data into power and make sure that Dun & Bradstreet is adding significant value along the way.”
Many businesses get stuck in a rut, mining the same lead list for years and experiencing flat or declining campaign results. One strategy to help break free is consumer-to-business (C2B) cross-selling, a catchy name for a cross-selling approach where a company can look for business prospects within its consumer portfolio. It’s commonly used by financial institutions, but any B2B organization with a robust consumer base that’s trying to expand its business portfolio can benefit from a C2B strategy, too.
“Utilize the relationships you have and build upon them! Consumer-to-business matching and cross-selling can be an easy way to expand your lead pool in a variety of use cases for financial institutions, tech companies, and more. For those businesses that have both consumer and commercial accounts, matching is the first step to identify businesses associated with the individuals in the consumer portfolio. The match reference database contains information about individuals (the consumers) who own and lead businesses. Since you already have a consumer relationship with these organizations, it makes sense (and pays) to take advantage of it.”
“As a Data Strategy Consultant, I’m a subject matter expert in Dun & Bradstreet’s match technology and the Dun & Bradstreet Data Cloud. I work with both new and existing clients to optimize their match results, provide guidance on “how” and “why” a match was made — our match metadata — and provide recommendations for improvements based on client use cases and strategies.”
The D-U-N-S® Number, our unique nine-digit identifier for businesses, identifies a company as being unique from any other in the Dun & Bradstreet Data Cloud. D-U-N-S Numbers are used to identify and maintain up-to-date and timely information on hundreds of millions of businesses worldwide and help identify relationships between corporate entities. Every D-U-N-S Number is unique; they are never reused. However, businesses are constantly in flux, and therefore data is dynamic. When we identify changes, whether it’s due to mergers, acquisitions, or organizational restructuring, for example, we provide an avenue to help our customers stay in sync with our data: D-U-N-S recertification, a refresh process to ensure our customers’ D-U-N-S records represent the current D-U-N-S Numbers associated with their company information.
“You have to understand that there are data dynamics happening every day, and Dun & Bradstreet has processes in place to help you keep up with those dynamics so that you can have the most valid and up-to-date information on customers, partners, and suppliers.”
“My role as a Data Strategy Consultant is working with clients to help them consume our data in the best demonstrated practice; helping them identify what they can use in terms of ingesting our data, whether from flat files or API calls; and making the data best suited for whatever their business or use case is.”
Hierarches, also known as corporate family trees, are key elements of a company’s master data efforts. But hierarchies can be complex, and many data integrators recommend simplifying, a recommendation that may result in missing out on the very insights that could be valuable for the use case you’re trying to solve for and the outcomes you’re trying to achieve. Every department — credit and risk, sales and marketing, supply and compliance — has different use cases for hierarchies. Thus, creating a one-size-fits-all approach that accommodates all their views is a mighty task not easily achieved.
“Organizations need to understand and embrace the complexity of hierarchies to get the full picture of a business. If one of your data consumers feels like they’re missing something, they probably are! Your hierarchy needs have to be driven by use case and an understanding of the outcome you’re looking for.”
“I’ve been with Dun & Bradstreet for most of my business career. I’ve spent about half that time in data and analytics roles and the other half in business development and sales leadership. In addition to gaining the comprehensive knowledge that I have around our data and analytics, I’ve also been able to see the impact of how customers benefit from the services and solutions that we offer. My role as Data Advisor is comprised of assessing, educating, and making recommendations on master data management for enterprise areas such as credit and risk, sales and marketing, and supply and comply for a portfolio of customers in retail, manufacturing, and transportation.”
Businesses require a variety of insights on their customer, partner, and vendor relationships. They often require specialized views mapped to specific business challenges and opportunities. Extended Linkage Insights (ELI) is a predictive linkage solution from Dun & Bradstreet that utilizes machine learning to link businesses just as a linkage expert would. ELI brings together entities that may not be legally linked but have similarities or affiliations.
“Leveraging ELI content can shed light on a wide spectrum of potential relationships, including majority ownership, minority ownership, franchise, dealership, and more. It brings real efficiencies and cost savings. Instead of you doing this work manually, Dun & Bradstreet can do it for you.”
“I work closely with Fortune 1000 companies, bringing together their information with Dun & Bradstreet information to drive business efficiencies and using information to drive knowledge and actions in the marketplace.”
Identity resolution is the foundation that connects a business’s data to Dun & Bradstreet data. Through matching, all the disparate identifiers and fields of a business record that exists on multiple platforms can be mapped to a single record. Multi-pass matching takes this a step further by putting different input permutations of client data — i.e., additional match data points — through our identity resolution process, not simply to return more matches but to better match candidates.
“Think outside the box, even when it comes to matching! Think about the data points that you have today and what else might be available, and valuable. Then talk to your Dun & Bradstreet team, as we may be able to grow your insight, improve your performance, and improve your ability to connect more customers or prospects to Dun & Bradstreet data.”
“As Data Strategy Consultants, we’re here to be subject matter experts for our customers around the different use cases of their data, whether it’s identity resolution, master data initiatives, hierarchy assignment, or even various global data points. We help ensure our customers understand their data and are maximizing its value in the most effective and efficient ways possible.”
Every customer of a business has a different experience and a different story to tell. This applies whether we’re talking about our favorite coffee shops or our data providers. But why? It often comes down to how much the customer is willing to share with the business. For example, if you just order a coffee and don’t tell your barista that you prefer a dark roast, you’re likely to be disappointed in your purchase. Similarly, if you hold back on sharing your specific needs with your data provider, you’re likely not going to receive all the value that your provider can deliver.
“I want our customers to recognize that we’re only successful when you’re successful. We want to be an active partner in helping you solve for your use case, so trust us with the challenges you’re facing and know that we’ll do our best to help you succeed.”
“The Data Advisory Team helps our clients get maximum value from their investment in Dun & Bradstreet data and tools. As a Principal Consultant, I get to really amplify that mission by helping spotlight the team’s expertise through thought leadership pieces and by bringing our own experience to help clients solve for their use cases in direct engagements.”
Scott shares insights from his own master data management journey and the process he went through to define “truth” for his organization’s customer data. Should you rely solely on your internal data or solely on a third-party data source — or is there a sweet spot that harnesses the goodness of both? Scott talks about his experience and the critical role that Dun & Bradstreet played in helping his organization define truth in its customer data.
“The struggle you’re facing — the need and desire to find an accurate and reliable source of truth — is real. Solving for your truth isn’t easy, but it’s absolutely doable, and leveraging a third-party data provider is an important piece of the puzzle. But remember, your truth isn’t necessarily the same truth for other organizations. Your puzzle is unique, and as such, you likely won’t find any vendor that has all the exact pieces that you’re looking for. Some of the pieces will come from your systems, and some will come from a third-party vendor. But this is your puzzle, so be prepared to fill in the rest.”
“I started my career as a sales rep at Dell Technologies and then held different operational roles supporting sales and marketing. It was during that time that I found my passion for data, so much so that I spent the next 10 years in the business intelligence space, leveraging data to inform key sales initiatives. After leaving Dell I joined an enterprise data and analytics team at a smaller high-tech company, which provided visibility into the nuances of managing an entire data supply chain.”
Sales figures and employee counts are two frequently used attributes that marketers rely on for B2B campaigns. But aside from publicly traded companies, that information is often difficult, if not impossible, to capture. It gets even more challenging when you try to allocate where those revenue dollars or employees belong inside a large enterprise. In this episode, we learn how an industry-leading analytic model allows Dun & Bradstreet to provide this information.
“The reality of global customer data is that it’s extremely challenging to obtain, and marketers can spend a ton of time trying to chase down actual values to base their segmentation processes off of. I encourage clients to try out our model and let the data speak for itself.”
“I believe my background in sales operations, finance, services, and IT provides me with a deeper understanding of our customers’ challenges so that I can help them maximize the value of their investment in Dun & Bradstreet data and services.”
When the COVID-19 pandemic descended on the world in early 2020, some businesses were more prepared than others to weather the ups and downs that followed. Among the advantages the more prepared enterprises had was that they were already regularly checking their business processes to make sure they were finding marketing targets, evaluating partners, and assessing risk properly. The ability to do that well is rooted in matching rules that make sense for those companies’ business goals, and that are regularly reevaluated and updated as needed.
“Utilizing learnings from the recent pandemic can be a path to develop a stronger data foundation with risk elements to support ongoing identity, change management, risk mitigation, and a best-in-class sales pipeline. Companies must identify risk and increases in risk across their business. Knowing your suppliers and knowing your customers are crucial, but it’s an ongoing process — it’s not ‘one and done.’ Maintaining strong identity resolution and that ongoing data hygiene process is crucial at all times to make sure that your business is performing optimally and going back to its data governance policy.”
“As a Data Strategy Consultant, I support Dun & Bradstreet’s clients to help them understand, organize, deploy, and grow in their data journey. My role is to understand the client’s goals and challenges, and to create a realistic expectation for maximizing data and technology assets to make sense of their data across their organizations.”
When we talk about data context, we’re describing the data’s definition — that is, what the data elements actually mean. Are the sales figures in millions or billions? Is everyone in the organization defining the data in the same way? Often, without realizing it, we have at least two completely different definitions. Understanding the full context of the data you’re working with is a necessity if you’re going to be successful using that data to accomplish your business goals.
“Know your data! Failing to consider the context of the data can have disastrous impacts on your downstream analysis. Based on your use case, based on what goal you’re trying to accomplish, make sure you have a good understanding of that data.”
“I focus on helping our strategic customers optimize their match efforts to get an accurate match so they get the accurate data that they need.”
Data technology migrations pose a risk even to the most seasoned organizations. There are changes to data, changes to processes, and even changes to the user experience that need to be considered. Failing to properly think through the transition for any of these can be problematic. In this episode, we get recommendations on how to approach a migration, whom to involve, how much planning time is required — and even how long to run systems in parallel.
“Don’t be afraid of migrations. Having a plan can help you avoid the pitfalls and make the experience easier on everyone involved. Yes, migrations can be challenging, but at the end of the day, you can overcome the challenge by working together: client, vendor, and technology.”
“As a Solution Design Consultant, I work closely with our sales teams and our clients, supporting pre- and post-sales engagements and providing best practices through a consultative approach to address their requirements. My primary focus is on defining the solutions that optimally solve client problems while enhancing the sales process with our technical expertise.”
There are many benefits that technology can bring to organizations pursuing a master data management (MDM) strategy, but as the space has expanded, so too have the options that companies need to sift through in order to make an intelligent choice to meet their needs. It is easy for a company starting its data management journey to get overwhelmed. Dun & Bradstreet Solution Design Consultant Howard Poppel has worked through and consulted on many MDM journeys with companies around the world. He shares his expertise on which factors have the greatest influence on companies’ choices of MDM software packages, which options to consider, and how to prioritize and staff properly to maximize the value of such an investment.
“Nobody does a master data exercise just for the sake of doing it. There has to be an end game, and that’s to make the data useful and actionable, regardless of what area of the organization is asking for it. Enterprise master data management needs to be thought of as a process and a journey, but not as a project.”
“The Solution Architecture Team is made up of technical advisors. We do pre- and post-sales technical consulting, mostly specializing in integration and automation of Dun & Bradstreet tools and data within our customers’ ecosystems.”
Domain matching is a technique that allows us to further resolve a client’s incomplete or imperfect data when undertaking a match exercise. There are two primary use cases for which matching by domain is helpful: first, when we can’t get a match to the company name and address that were provided; and second, when the company information that is provided is incomplete, which prevents getting an acceptable matched record. In these cases, when a match might have been impossible, URL or domain matching can help us identify a match candidate.
“Domain matching overcomes a common challenge of the matching exercise, providing results that you might not have gotten using a standard name and address match. In addition, you often get a more certain answer with the domain match because it is more binary.”
“I believe data is a corporate asset. I understand the power and value of having clean, standardized, and consistent data across systems and applications. I help customers understand the benefit of Dun & Bradstreet Data Cloud and how to integrate it with their own data to achieve their objectives.”
The pace of business change is robust, and companies have a hard time keeping up with that change in their core supplier and customer data. Company names, locations, phone numbers, and people can all change rapidly, and the information an organization possesses may not be valid or valuable in a matching exercise. Therefore, alternate match points can add tremendous value. Alternate match points are data elements that Dun & Bradstreet maintains in our match reference file that enable our clients to find the entities they are looking for, especially when their data is outdated and incomplete. Examples include data elements such as former names, former addresses, past CEO names, and additional phone numbers.
“Keeping data up to date is no easy task. Using Dun & Bradstreet’s match insights capabilities — alternate match points, confidence code, match grade string, and match data profile — can give businesses the information they need to understand why and how a match took place and the quality of the match.”
“Before joining Dun & Bradstreet, I worked with Dun & Bradstreet data for 18+ years. Needless to say, I’m very familiar with deploying solutions using Dun & Bradstreet data, and I really came to depend on Dun & Bradstreet data very heavily, both in maintaining our CRM and in developing various analytical models.”
George L’Heureux: Hello everyone. This is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux, I'm a Principal Consultant for Data Strategy here in the Advisory Services team at Dun & Bradstreet. In Advisory Services, our team is dedicated to helping our clients maximize the value of the relationship with Dun & Bradstreet, through expert advice and consultation. On Data Talks, I chat each episode with one of the expert advisors at Dun & Bradstreet about a topic that can help consumers of our data and services to get more value. Today's guest expert is Rick Venezia, a Data Strategy Consultant at Dun & Bradstreet. Now, Rick, how long have you been with the company? Rick Venezia: Hello, George. I joined Dun & Bradstreet just under a year ago. However, prior to that, I had worked with D&B Data for going on 18 years. And so needless to say, I'm very familiar with deploying solutions using D&B Data, and really came to depend on D&B Data very heavily, both in maintaining our CRM as well as in developing various analytical models. George L’Heureux: I think one of the great assets that we have inside Dun & Bradstreet is that we have a lot of the people who used to be clients like you who've come into the company who understand what it is to be a client, and who use that to inform the way that we work with clients. And I think that that talks to the topic that we're discussing today, which is a really important aspect of one of the benefits that Dun & Bradstreet matching can bring to our clients. And that's this idea of having alternate match points. Let's start with the basics though. What are alternate match points? Rick Venezia: Well, alternate match points are alternate data that we maintain within our match reference file, that enable our clients to find the entities that they're looking for. So these alternate match points would include things like former names, former addresses, CEO names, additional phone numbers. George L’Heureux: How does having a robust match reference file like that, that includes all these alternate match points, actually end up helping our clients? Rick Venezia: Great question. Naturally data does not exist within a vacuum and let's be honest, depending on the age of an account, the complexity of an organization and various other factors, it may have been a long time since the client initially collected the information about the particular entity that they're looking for. And as we've all seen, things can change, company names change, physical locations, phone numbers, people. So the information that the client may possess, may no longer be valid or even certain ports of it may no longer be valid, yet they still have a need to find the right D-U-N-S Number and so maintaining these alternate match points makes that possible. George L’Heureux: Rick, do you think it would be fair to say that the more of these match points that we have, the better chance that we share a common view of an entity with that of our clients? Rick Venezia: Probably a bit of an over simplification. There is always the more is better rule. No, it's really the true power comes from the structured collection of the data, the depth of the records, and the refresh of the data to even make this comparison possible, that being the comparison between the client's data and our own. And what we find sometimes is that the client's data is, it's widely different from what we believe as the correct and current information. But again, only by having that former data available, are we able to make the matches. George L’Heureux: And so the idea Rick then, is that used together these different parts of match insight, the confidence code, the match grade string, the match data profile, they give clients the information that they need in order to understand why and how a match took place and how high we really think the quality of the match is, right? Rick Venezia: That's right. And our clients are going to present us with the best information that they have available, so it's on us and our processes. Our processes have to anticipate imperfect input, and then we need to leverage the deep knowledge base that we possess to get to an answer. And we want to make sure that we're transparent in how we got to the match and that's what the match insight allows us to do. George L’Heureux: You and I both know that the conversations that we often have inside of advisory services with clients frequently involve discussions around match insight, and especially when an alternate match point has been used to find a particular match. Could you share what some of those conversations might be like? What are some of the things that get brought up that need to be explained? Rick Venezia: Yeah, sure. When customers get back a match based on an alternate match point, at least initially, they'll sometimes look at it, and frankly, question why it was matched. They'll say, "This isn't even close to the name that I put in, but yet you're returning it a confidence code of a nine on it." Or they'll say, "How did I get an A on this street name?" And the answer is that they're providing us information that represents one dimension of the data for that entity. And we are in turn returning to them the current information that we possess. So for example, let's say a given client has all trade style names in their data. And let's say that we match on this trade style and give it a rating of an A. But the business name or the legal name that we return to the client could be very different. And so at least initially, that could cause some confusion. But again, remember the match was a perfect match. It was an A, but it was on a different dimension of the data than the true business name. And so in this case, it was on that trade style. George L’Heureux: So aside from trade style, you mentioned earlier that some of the match points that we have, represent former data, like former names or former addresses, former executives. It seems to me that, that could represent potentially useful information to the client in and of itself. Rick Venezia: Right, yeah. Absolutely. Let's take an example where you submit an executive name and we return a match, and we inform you via that match data profile that we matched on the former executive. Well, that should set off the alerts on your side to say, "Wait a second. I thought this was a current executive," and in this case, you might want to get your stewardship team in there to verify the information that you have is accurate and current. Another thing that we see on a fairly regular basis is the client will submit more than one record that they believe are distinct and not related to one another, but we will in turn end up matching them back to the same D-U-N-S Number. While everyone wants to have perfect data, this type of input is very helpful. It's showing you that you potentially have some overlap within your data set and gives you the awareness so that you can address that. George L’Heureux: We've talked a lot about the benefits of having that deep reference file. And you just mentioned a couple of those additional sort of almost hidden benefits around alternate match points, but can we compare what's happening inside Dun & Bradstreet against the rest of the industry? What does that comparison look like when you take a look at it? Rick Venezia: Well, simply put, the data that we collect in the Data Cloud is unrivaled vis-a-vis business data. It's truly this bank of business data that sets us apart. George L’Heureux: That's what feeds into the match reference file as well as what we're able to return back to the client. Do customers need to do anything special to prep their data in order to take advantage of the match insight that you were talking about or these alternate match points that can get them there? Rick Venezia: Well, so D&B is set up to ingest whatever information you have. The depth of our records is really intended to prepare for a variety of characterizations of a given account. And so the preparation, if you will, would be, once we return the MDP to you, you could have your own processes to act upon the information as is appropriate. So in this sense, the preparation becomes a question really of governance and stewardship. What are the rules? How do you implement them upon receiving this new information? Another way of saying this is, how do you optimize the experience of your internal stakeholders based on what you get back? George L’Heureux: Rick, as we get close to ending our conversation here today, I want to ask, really, what's the bottom line? What would you want to make sure that someone who's watching our conversation today or listening to it, that, that person would take away from this conversation? Rick Venezia: All right, well, first let me say that having worked closely with data stewardship teams for years, I have an enormous respect for their role in the impact that they provide. And I know that any opportunity to improve the efficiencies of their processes saves the company money. Now, I'm sure I'm not telling you anything new, when I say that most people are going to achieve the vast majority of their matches by way of the confidence codes in most use cases, eight or greater, but it shouldn't end there. This additional information provided via the match data profile will, again, help you to better understand the way that we arrived at the match and help you prepare and maintain your data, which leading to more predictable outcomes. So also leveraging the insights from the match data profile in combination with certain match grade string patterns, will allow you to fine tune your matching and extend your match acceptance. And let's face it, the more data that you can confidently match via an automated process, the greater velocity that you'll recognize within your operations. So this is the long way of getting to my bottom line, but the bottom line I would say, is that learning to harness the full power of the information in the match insight, means you can optimize your matching and stewardship processes, to allow you to achieve the velocity that your stakeholders demand. George L’Heureux: Rick, I really appreciate you sitting down and taking the time to share some of your perspective and your expertise, both from having been a client of Dun & Bradstreet, and now inside the Advisory Services Team here at Dun & Bradstreet. Thank you so much for your time. Rick Venezia: Thank you, George. I enjoyed it. George L’Heureux: Our guest expert today has been Rick Venezia, a Data Strategy Consultant at Dun & Bradstreet and this has been Data Talks. I do hope you've enjoyed today's discussion. And if you have, please let a friend or a colleague know, and if you'd like more information about what we've discussed on today's episode, please visit www.dnb.com or talk to your company's Dun & Bradstreet representative today. I'm George L'Heureux, thanks for joining us, until next time.
George L'Heureux: Hello everyone, this is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a Principal Consultant for Data Strategy and the Advisory Services team here at Dun & Bradstreet. And in Advisory Services, our team is dedicated to helping our clients to maximize the value of the relationship with Dun & Bradstreet through expert advice and consultation. On Data Talks, I chat every episode with one of the expert advisors at Dun & Bradstreet about a topic that can help our consumers of our data and services to get more value. Today's guest expert is Ron Stam, a Data Strategy Consultant at Dun & Bradstreet. And Ron, how long have you been with the company? Ron Stam: Hey, George, I've been with the company about 25 years. George L'Heureux: And Ron, we wanted to chat a little bit today about domain and email matching. Now, I think most people are familiar with the idea of trying to identify an entity by say its name or its address or phone number. What are cases where you'd want to go beyond that and search by domain or an email address? Ron Stam: There's two primary cases that domain matching is especially helpful. Number one is when we don't get a match to the name and address that's provided, and the second one is where the company information that comes into us does not have very complete information or it's heavily dependent on domain level data itself. George L'Heureux: So when we do have situations like that, what sort of advantage can we gain by going ahead and doing a match on that URL domain or that email domain? Ron Stam: Oftentimes we can get a match where we didn't have a match previously. We can reinforce the match that we had and we can get to a better record at times with the domain match than we could with data that might not be presented perfectly with name and address matching. George L'Heureux: Now, is there a difference between maybe what you're able to get out of, say, a URL domain, a website address that someone gives you versus someone handing you an email address? Ron Stam: There are differences. And actually, you have to be a little bit careful. While both of the domains themselves are often similar, so dnb.com is used both for the corporate domain as well as for our email convention. If a customer record shows a corporate domain, that data is more likely to be reflective of the company. An email domain has a challenge that sometimes people don't use their corporate or work email address as a source point that is being provided. And there are certain instances where you have to be a little bit more careful and do a little bit more filtering up front with email domains than you might have to with a corporate domain. George L'Heureux: Can you go into that a little bit deeper? What are some of those examples? What are situations where that email domain question might come into play and you have to exercise an additional little bit of caution when you're looking at your match results? Ron Stam: Sure. The first one that's very visible is all of the ISP addresses, the Gmails, the Yahoos, the AOLs. If somebody registers with those kind of emails, they're not reflective necessarily of a company that they work for. A couple other things that you do have to be careful though of are for example, .edu, which is colleges and universities. And a college or university might extend those email addresses to alumni or other people who don't necessarily work for the organization. And as it relates to matching to Dun & Bradstreet's data, oftentimes what the goal is is to get the information on the business itself, where somebody works. And if I'm an alumni of a university and I'm using that email address but I work at a company not related at all to that university, that domain match on abcuniversity.edu would not be reflective of where I actually work. I'm just using that email address for professional or convenience reasons. George L'Heureux: So it strikes me that compared to some other places where you can match, for example, address in particular, a name, where there's going to be a lot of fuzziness around is this name spelled correctly? Am I, spelling this name the way I think it's pronounced? Or is this street in avenue or a Boulevard or a circle? It feels like URLs, domains, would be a lot more binary. That it's either yes or no. Is that the case or is there more to it than that? Ron Stam: It is. Almost always the case. There's chances that a domain might be typed in incorrectly, but a domain such as dnb.com exists and it points to Dun & Bradstreet. The challenge is that if somebody did type in DMB instead of DNB, the domain for DMB could very well be valid and could point to a different company. But most of the time, we have to assume that the domains themselves are accurate as provided and we can match to that domain on a one-to-one basis, or at least get to the right organization because the domain itself could be reflective of all of the different locations of a business, but a primary goal of our customers when they match to our domain is to get to the right corporate entity as opposed to necessarily getting to the exact particular address site for a record. Even though that's beneficial, it's not always a requirement. George L'Heureux: So obviously domain match, we're talking about it and it is a capability that we offer here at Dun & Bradstreet. When it comes time to actually perform a domain match, is it as simple as just supplying that URL or that email address and hitting go, or is that more complex? Ron Stam: It is kind of that simple, but it's beneficial for you if you are a customer who has domain to provide us with additional information, in particular, geographic or location-based information because that might point to a better record in the world. So if ibm.com is valid and relevant for all of the IBM locations worldwide and you are dealing with IBM in South Africa, it would be good to know that you're trying to get to an IBM record in South Africa as opposed to just any IBM record or go to the top IBM record in New York. George L'Heureux: And we've talked about domain matching versus traditional name and address type matching. Are there different ways that customers when they go to do this with Dun & Bradstreet can integrate the two in a way that makes sense for them? Is that variable or is it all kind of only one way? Ron Stam: There are a couple different ways and you could do it in a kind of integrated way or you could do it in a waterfall way or you can do it in a kind of a validation way. So for example, a customer might try to match on a name and address, and especially if they have questionable or mid-level match results, they might want to also match on a domain that's provided and they might make a better decision if they feel that the domain match and the address match was the same. If they see that there's differences, they might choose one path versus another based on the type of information provided in either the address or domain and the likelihood that that is more correct. So the domain match is at a minimum, a value add second step, but it can be used as a more reinforcement of an existing match or identification of a record. Ron Stam: And outside of Dun & Bradstreet, a domain match can be used internally by a customer or a company as well to help link records that are of the same organization. So using the ibm.com example again, if they have records around the world that are ibm.com, they can manage or link those records together as part of their data management, but it should be noted that some companies, and I'll use in this case, Microsoft and LinkedIn. LinkedIn is now a subsidiary of Microsoft, but their linkedin.com URL is in fact fully operational and used all the time, but it's different than Microsoft. So there is a benefit in matching to the URLs, but the information on a corporate hierarchy or family tree is not always one consistent URL. It can vary. George L'Heureux: And you kind of hit on another aspect of data management that at Dun & Bradstreet we talk about quite a bit, and that is hierarchies. This idea that LinkedIn is a wholly owned subsidiary of Microsoft and they have different domains and they still all, however, are part of Microsoft. That goes for things like brands, right? If you're a CPG company, if you've got a lot of different consumer brands that each have their own URL, we're going to be able to resolve those to the correct company that actually manufactures those brands, right? Ron Stam: Often. Yeah, I won't say 100% of the time. Some of the brands themselves have their own unique URLs and some of them are going to point or redirect to a corporate site. But if that's what a customer has, that's what they should submit. And many times we'll be able to resolve or get a match to that record. George L'Heureux: I want to go back to something that you were talking about a few minutes ago, Ron, and that is this idea of these shared or ISP type domains that particularly for email matching, we have to take additional caution with. I imagine that this is a data stewardship, data governance type question, but can you talk about the additional steps that a consumer of that type of matching might want to take after getting results back to ensure that they're really putting the right high quality results into their database? Ron Stam: Yeah. And they might actually want to do it before they submit it as well. So what they could do is they could take a list of ISP and other email domains, marketplaces like Facebook or Etsy or Amazon that people also use, and they could actually suppress those records from even being attempted to match if there's an awareness that the match might come up with an inconsistent answer. So, as an example, if ronstamplumbing@facebook.com was either my email address or my marketplace address, if we isolate that facebook.com, that would not get to the right organization related to my plumbing business. It's just a site that I might be using via a third party to have a presence on the web. So if I suppress that out front knowing that I don't really want to match on facebook.com or gmail.com or Etsy or a bunch of other domains, you won't get records that you have to figure out and filter later. So it's usually better to do it up front. George L'Heureux: So, Ron, as we wrap up here, do you have something that you are hoping that people who are watching this or listening to this will walk away with? What's the bottom line message that you'd like them to make sure that they learn from this discussion today? Ron Stam: Yeah, the primary benefit is that you will get matches that you might not have gotten previously using a standard name and address match that is common at Dun & Bradstreet. In addition, you will often get, I'll say a more certain answer with the domain match because it is a little bit more binary, as you mentioned, where a domain is a domain and that reports and reflects the particular company with those exceptions of some of the marketplaces, with some of the franchises, with some of the other kind of known shared domains. And again, with the email domains, you have to be a little bit more careful still. George L'Heureux: Ron, I really appreciate you taking the time to sit down and talk about this aspect of matching with me and sharing your expertise here. Ron Stam: Thank you, George. George L'Heureux: Our guest expert today has been Ron Stam, a Data Strategy Consultant at Dun & Bradstreet, and this has been Data Talks. I do hope you've enjoyed today's discussion. And if you have, I encourage you to please share it with a friend or a colleague. And for more information about what we discussed on today's episode, please visit www.dnb.com or talk to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L’Heureux: Hello everyone. This is Data Talks, presented by Dun & Bradstreet. I'm your host George L'Heureux. I'm a Principal Consultant for Data Strategy in the Advisory Services group here at Dun & Bradstreet. In Advisory Services, our team is dedicated to helping our clients to maximize the value of their relationship with Dun & Bradstreet, through expert advice and consultation. And on Data Talks, I chat every episode with one of the expert advisors at Dun & Bradstreet, about a topic that can help consumers of our data and services to get more value. Today's guest expert is Howard Poppel, a Solution Design Consultant at Dun & Bradstreet. Howard, how long have you been with the company? Howard Poppel: I've been here 21 years and 13 of them in Technical Advisory Services. George L’Heureux: And can you tell me a little bit about what it is that you do in your role as a Solution Design Consultant? Howard Poppel: Well, the solution design consulting team, we're technical advisors, and we do a post-sale technical consulting, mostly specializing in integration and automation of D&B tools and data within our customers’ ecosystems. George L’Heureux: Thanks, Howard. I think our topic today is really relevant for a lot of the data practitioners that are out there and that's how to choose the right MDM application. Let's face it, right? There's a ton of different software packages out there. How is one supposed to figure out what's right for them? Howard Poppel: Well, I think they first have to figure out what they're trying to accomplish. What is their point of arrival? Because every package is different, and once they understand what that point of arrival is, then they need to start then breaking that down into the different components of what's important to them. So whether it's alternative hierarchies, operational hierarchies, multilingual functionality, global access, in-product identity identification, they need to understand what, what really are the needs. And then that'll help them to determine what's the right package for them. George L’Heureux: So just then, you mentioned a few of them like alternative hierarchies, multilingual functionality. Are those the most common functional needs that we hear clients saying that they need? Are there others? Howard Poppel: Yeah, there's a few others. But the most important, and one of the main reasons why we see our customers purchasing MDM packages, are for those operational hierarchies. So when we're talking about that, we mean like sales hierarchies, geography hierarchies, territory hierarchies. D&B can provide the standard levels of hierarchy. So we can do standard corporate hierarchy, alternative linkage, minority linkage. But a lot of these customers need operational views. And that's really where MDM comes into play, because they can then separate and slice and dice the data and provide it into those operational views, taking our data. Also, a lot of customers are looking to be able to rationalize data from disparate sources. So whether you've got two, a hundred different sources, what they're trying to do is find out what data is across the organization and where is it duplicative. And then MDM helps to solve that. George L’Heureux: So when we're looking at how to choose, I imagine that these types of functional factors, the ones that you've mentioned just now, are important. But also its ability to really handle the volume. If you've got 200 sources and they're all small, that might be one thing. But if you've got five sources and they're all massive, it might be a completely different calculation. Is volume something that comes into play when you're looking to evaluate MDM offerings? Howard Poppel: Absolutely. The two things in that aspect are volume and throughput, because it's not just how many records or how much data you want to push through the system, but what kind of speed do you need? With some customers, just the ability to rationalize data in days, is sufficient. In others, they want to do subsecond response. They want to know immediately, and they need to clean their data for that. So that's your throughput, is how much data can we consume in what period of time, but then the amount of data and also the number of different source systems. Because keep in mind, no two data sources are ever alike. So when you're looking at disparate data sources across an organization, the more you have, obviously the more technology you're going to need, and the more complex the process is going to be, because it has to account for all of those different nuances from each of those systems. George L’Heureux: So do the distinctions between sort of on-premises software and cloud based software, do those play into it as well or mobile accessible, and like you were talking about before, multilingual capabilities. How should people be assessing each of those in terms of their importance to the overall decision? Howard Poppel: Well, we're starting to see going it across the spectrum that on-premises is minimizing. It's a lot more cloud based solutions. A lot of our customers are really ... Everything's moving to the cloud, but there is that need for on-premise. And we see that a lot more in the financial services industry where security really becomes a priority. They need to keep everything behind their firewall. So we're seeing them lean towards more of those on-prem. But for the most part, a lot of our customers are really going cloud based and they really need the security of working with cloud-based systems, but also they need to know that they have connectivity. Whether again, it's mobile, and from any region across the globe. Being an international or global corporation like Dun & Bradstreet, and we're delivering data across the globe, our clients could be anywhere. And I mean that not just from their position as a company, but as their position as their employees, I've done work personally in multiple countries around the world, reaching out to the D&B database to get that information, so that has to be accessible. George L’Heureux: Talking about Dun & Bradstreet, here we often talk about the importance of really understanding your data before you try and put together a technology stack. Now, are there particular considerations around data that you feel you need to consider when you're assessing your software options? Is data the first thing that you need to think about, or is there something even before that? Howard Poppel: Well, right before you decide what data you need, it goes back to what I had said previously. You really have to focus on what is the use case. A lot of times, our customers come to us with an MDM problem or opportunity, and it's really based in the sales and marketing space. But keep in mind today, master data management really goes across all spectrums of data. Whether you're doing finance analytics, whether you're looking at your supply chain across the globe, or whether you're using it for sales and marketing purposes. The first thing you need to do is what is that use case. Then once you've got the use case, now you start looking at the data, because what data do I need? What data do I have and what is the integrity of that information? Because again, as I mentioned previously, when you've got disparate data sources and different levels of completeness or accuracy of information, your MDM software has to be able to manage that. Then the last piece and again, I was saying just previously, was when you start looking at data enrichment, do you need finance data? Do you need diversity information or compliance information? Do you need sales and marketing? Do you need URLs, IP addresses? All of that different information all correlates back to what was that original use case, and then quality of the input and then quality of the enrichment. George L’Heureux: And you mentioned that there too, kind of that integrating the different sources and that's what a lot of the MDM software is really trying to get you to, is this idea of a golden record. Are there different ways that these MDM providers are building these golden records? Or allowing you to sort of integrate toward a golden record, that you need to take into consideration that might be different? It might get you different results in the end. Howard Poppel: Well, a lot of them are using the same type of processes to get you to that point of arrival. A golden record should be the same across every organization, but it's not, because again, it depends on the input data, and then what data are you matching it up against. A lot of these, keep in mind, MDM is really a tool to get you to a point of arrival. But these companies don't offer the in information that D&B is offering. So when you're trying to get to that golden record, you have to look at first party data with which is the customer's data. You have to look at second party data, and then you've got to look at that third party data and again us, when you bring them all together, then you can get yourself to that point of arrival, which is finding out what is that best record. Now, a lot of different MDMs do it slightly different. But at the end of the day, they're trying to get you to the same point of arrival. George L’Heureux: Now, a lot of the providers have packages that come pre-loaded with, like, pre-defined logical models. They cover a variety of different domains. In what you've seen, in your experience, do these end up being helpful? Is that something that can help differentiate between different products, different packages? Howard Poppel: They're helpful as a template. You have to start somewhere and it's much easier to start with that template, and then bring that use case in and then make those configuration changes. Doing this for 20 years, I've never seen two customers’ data look exactly the same. So there shouldn't be just one process for everybody, and that makes sense, and a lot of these vendors know that. So what they do is they'll provide you with the template, get you started, but give you a toolkit or a toolbox so that you can do configurations based on your needs. Again, you brought it up just a minute ago, multilingual. Are we dealing with diacritic characters? Are we dealing with double byte characters? How is the data provided? Is it name first, address second? All of that needs to come into play. Are you using ISO standards? Are you using FIPS standards? So when you look at all of the information that's available, you've got to look at what customizations are available, but those templates will get us the starting ground. But they should never be looked at as the end all be all to get you to your point of arrival. George L’Heureux: Well, I'll tell you, Howard, as a guy who has an apostrophe in his last name, you're talking about diacritic characters, I'm just thinking, can they handle apostrophes right? Another thing that you need to think about though, anytime you're bringing on a new software package is resources. And are these packages that these MDM providers offer, are they going to require specialized resources generally? Howard Poppel: A lot of the MDM providers have a package that comes with it, where they'll at least get you implemented. But yes, absolutely. Every MDM’s process that I've ever worked on, you need to have resources really in three different areas. Number one, you've got to have it at the customer base. They need to be able to provide a project manager. They need to be able to provide DBAs. They need to be able to provide reporting people. In the old days, it was easy. We would deliver a flat file of data. You'd have a DBA massage it in Excel or some other simple program and they'd spit out, "Here's the answers." But today things have so much more complex and it's so much better because we can really dig into the data. So at the customer level, you need all of those different resources to really get you to your point of arrival. Then second, you need the software provider to have their provision. They need to be able to provide the training and technologies and support at the application level. And then last but not least is where D&B comes into play at the data level. We're going to work with the hardware and software providers, but we also need to be able to explain how the data fits into those structures. What should the canonical models be? Where does the data update structures? All of that gets really important, and that's why it's important to have resources on all three levels. George L’Heureux: So we've been talking for a while now, and we've gone over a lot. Now there's probably some people out there watching this or listening to this and they're asking the question, "Can you do this without enterprise data management software?" Howard Poppel: That's a great question, George. And to be honest, it depends on the two things. Number one is what's your point of arrival? So what are you trying to accomplish? And in what I found in my experience and working with my team, is it also depends on the size of the organization and the size of the engagement. I've seen very successful engagements without using master data management software, but bringing it into a data lake, a data warehouse, or even a CRM. But keep in mind, those are kind of more outliers. A lot of the big companies, they have to have this software because it really does the job for them. But when you're a smaller organization or you have a smaller footprint and you really just need to be able to, again, dedupe across two different data sources, and then provide external data onto it, that's not overly technically complex. So that's where you might want to look at again, a data warehouse or a data lake to manage that kind of process. So the short answer is, yeah it does, it's necessary to have it. But the longer answer is that it really depends on the use case and the size of the organization to make that decision. George L’Heureux: So, Howard, as we start to wrap up, what's one thing that you'd want someone who's watching this or listening to this, to walk away with having learned? Howard Poppel: They have to understand one thing. And I think if you're going to get anything out of this video and out of this conversation that we're having, is that enterprise data and master data management itself, it's not a project. It's a process. It's a journey. You've got to have that starting ground. You're going to have then the implementation. But what a lot of companies fail on, and that's why we as consultants work with them closely, is also understanding the most important part, and that's the maintenance or monitoring strategy. Once you get the data in there, if you don't do anything with it, then all you have is an expensive Rolodex. But if you have that maintenance strategy in there, then you've got the technology in place that will get you guys well through, keep the data clean and keep it actionable, because that's also important. And I know we haven't touched on that, but I want to finish with that. And that is at the end of the day, master data management is really, nobody does master data management just to master their data. There has to be an end game and that's make the data actionable and useful, regardless of what area of the organization is asking for it. George L’Heureux: Well, thank you Howard so much for joining me and sharing some of your expertise on this. Howard Poppel: Thanks, George. I appreciate being here today. George L’Heureux: Our guest expert today has been Howard Popper, a Solution Design Consultant at Dun & Bradstreet, and this is Data Talks. I hope you've enjoyed today's discussion, and if you haven't, I encourage you to share it with a friend or a colleague. If you would like more information about what we discussed on today's episode, visit www.dnb.com or talk to your company’s Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L’Heureux: Hello, everyone. This is Data Talks, presented by Dun & Bradstreet. I'm your host George L'Heureux. I'm a Principal Consultant for Data Strategy here in the Advisory Services team at Dun & Bradstreet. In Advisory Services, our team is dedicated to helping our clients to maximize the value of their relationship with Dun & Bradstreet through expert advice and consultation. On Data Talks, I chat every episode with one of the expert advisors at Dun & Bradstreet about a topic that can help consumers of our data and services to get more value. Today's guest expert is Sita Nathan, a Senior Solution Design Consultant at Dun & Bradstreet. Now Sita, how long have you been with the company? Sita Nathan: Approximately 14 years. George L’Heureux: Could you tell me a little bit about what it is that you do in your current role as a Solution Design Consultant? Sita Nathan: Sure. A solution design consultant supports both the pre- and the post-sales engagements. We are close partnership with both our sales teams as well as our clients, where we provide them with best practices, leveraging a consultative approach in order to address their needs or their requirements that they have. Our focus primarily being on defining the solutions that optimally solve client problems as well as being able to enhance the sales process using a consultative technical expertise on the D&B integrative products and offerings. George L’Heureux: Thanks so much for that explanation, Sita. Today's topic is really an important one for us and it's one that just about every organization is going to face at some point or another, and that is a technology migration. And so let's start really with the basics. Why are these such a big deal? What types of challenges are our customers going to face when they go to do a migration? Sita Nathan: When everybody talks about migration, everyone gets a lot nervous about it because everyone has a different view of or a perspective about migrations, right? Today, there are three critical perspectives that one needs to think about: Data, technology and the user, because users are the people who get primarily impacted when we do these migrations. When we talk about the user, there are multiple things that one needs to be thoughtful about and to understand what are some of the things that changes that would impact their day to day life. This becomes a really critical, important play, which we really do not take into consideration and we need to bring it very upfront in the process as we go through this. George L’Heureux: Let's make sure that we do that. Let's take a little bit of a deep dive in each of those perspectives, the data, the technology, and as you point out the user. For each of them, what are some of the things that we need to be considering? Sita Nathan: Yeah. So let's start with, I like to call them the three legs of the stool, right? The data part. To understand the data that is being impacted by or will be impacted for the client, this is a real critical partnership that has to happen between the client as well as the provider in order for us to be able to get together and assess the needs of them. Understanding what are they getting today and what they will be getting tomorrow. What are the differences in the data? What are some of the rules they have to build in as they go through the process? A technology perspective. A technology is usually many clients are now migrating from an older technology to more of the modern technology, which is like a REST-based API. With this shift in technology, there are critical aspects that one needs to think about like the resources and how to implement this because the older technologies have different type of resource type of criterias required versus the newer technologies. The user interface, this is where one needs to think about how this new user interface. Do you want to keep the same interface or do you want to change it? There will be naming convention differences because of the way the industry has moved so we need to say many clients want to move towards the best practices in there. How the placement of these output data is going to be? What are some of the changes, because there may be updates to the models that may have happened? That could cause changes within what the data the user is going to see. We need to bring them early into this journey and help these users to be able to understand not just the data, but also the technology that we are moving towards. These three really play a big deal as we move towards migration. George L’Heureux: So something that you just mentioned about bringing the users on the journey earlier. When you're thinking about so many different aspects, these three legs of the stool like you're talking about, when is the right time to start having these sorts of discussions? Sita Nathan: This is a very... This topic becomes very controversial and it also depends. If this is being driven by the provider due to a platform change, then you might need as much of a advanced notice like a year, okay? Because everybody needs to plan it out, make sure that they are part of this, the changes are part of a major release because this will require budgeting, resource planning and all the different criterias that go with any technology improvements that happen. We need to work with the partner as well as the vendor to ensure that everybody understands these changes and give enough notification for them. If it's a customer is making the change, then they may be lesser time because some of these things may already have been taken into consideration, right? Like they would've already got the resources, the funding. They would have a high level architecture plan. They would know where this is going to fit within their release schedule. It could vary anywhere between eight to 12 weeks or even longer, but it all is driven by a combination of the client as well as the partner and who is driving this migration. George L’Heureux: That seems like a big difference, right? The difference between say maybe a quarter's worth of time and a year's worth of time. It makes me believe that maybe the actual migration to the new technology really isn't the biggest part of the equation, isn't the biggest driver. Would you say that that's correct? Sita Nathan: Yes. I would say yes. Integrating an API, for example, is not the real issue, right? The bigger part of this is understanding the content, and that is very critical. Understanding what the new rules are, getting enough context and understanding to ensure that the data that is being delivered into this new system becomes actionable and becomes something that the user can be able to understand and drive towards their end goal that they're trying to achieve. That is the critical piece of it. George L’Heureux: It seems like all of this planning is really designed to try and avoid getting tripped up, stumbling over some unexpected difference that exist between the old system and the new one that didn't get detected earlier. What types of changes might these be? What might a user see that's different and that you need to be on the lookout for? Sita Nathan: There could be some that is as small as a difference in saying an indicator, right? Today, we may be giving a flag that says yes or no. Tomorrow, that same flag would've been changed to true or false. We assume that this is a very minor change. It could be, but you have to think about not just the system that you're migrating, but also the downstream system, because it could have a big impact in there. Another one is also talking about the sales data, because this I've seen a lot, right? Many clients, you need to firstly understand the use case and say, "What is that sales data? Is it actual that they're getting today or is it something... What is the use case? What is the purpose?" If it's a marketing use case, they could have been getting estimated or modeled, right? Because the sources that we have been pulling from a migration perspective may have changed. Understanding those kinds of subtlety is very important. And the third one that I'm seeing also is the small business indicator. What use case is this for? How are the clients leveraging this? Is this for a supplier use case where you need the data from a supplier database, right? Because that way, it's a certified small business indicator versus a small business indicator, which is used from a marketing perspective. All these may seem like small changes, but it's really not. It causes a lot of churn in the system so you need to be aware of it, make the user aware of this and sometimes this is more painstaking if you don't do some of the due diligence processes. George L’Heureux: Sita, can you share with me some of the best practices that you've observed or even used yourself when performing a technology migration or some sort of integration project? What are those? Sita Nathan: A technology migration in general is driven by the technology teams, okay? While an integration migration can start from a business and bring the technology teams along the ride, right? They’re definitely separate migration efforts from a testing efforts. The first thing is always include business users in testing. That's why the beta testing that we call once it's developed is very important. Make sure that you gather all the documentation, whether it's an API documentation or how you can create a user documentation. The next thing is all the three teams have to work together in partnership. The technology, the business and the users. Each one will have a different role to play and will be brought in different points of the project. But it is very important that you listen to all three of them and make sure that you define what that documentation is required and provide them as early as possible. The clients also will have this as something to be left behind after the migration is done, right? Finally, the last one is having a project plan. This project plan should be twofold. One is a high level summary plan and the second one is a detailed plan to ensure that we are capturing all the critical components that need to make this into a successful migration. George L’Heureux: Digging a little bit deeper, are there any big differences between migrating from one vendor to another versus say migrating from one platform to another within the same vendor? Do you approach those differently? Sita Nathan: Absolutely. When you migrate from one vendor to another vendor, there's a lot of differences. One could be from a technology perspective of the migration, of how that is. What tech stacks each of the vendors have. The second one is the data differences. The third one is how, especially from a D&B perspective, how we do entity identification. Our entity identification has a method of how we do that, right? Whereas, other vendors have a different methodology. Having all these different types of components, you will see there is a variation and it definitely causes a lot of discussion that we need to work through during this process. George L’Heureux: Any migration is going to be a collaborative effort as you've already alluded to. But in the end, who's ultimately responsible when you've got a client and vendor and even maybe a separate technology team all working together like this? Sita Nathan: At the end of the day, the client is the point of contact. They are responsible. But the vendor needs to be very consultative and make sure the client understands and provides the client with enough documentation so that they can be able to drive that migration, okay? The vendor as such needs to say what are the constraints and what are the assumptions that we are working on from the vendor's perspective. The client also needs to be very articulate to say, "What is this migration use case about and what are the goals and objectives they're trying to achieve?" Having this three-way partnership between the business, the vendor and the technology team is very critical in the component because sometimes the technology team is in-house. Sometimes it's a third party vendor. But all three of them working together is the critical part when you're doing a migration. George L’Heureux: Finally, I think one thing that we've all seen and done and that we know is pretty good practice is to run things in parallel. Run your old technology stack and your new technology stack in parallel for a while. But how long should we be doing that? What do you recommend? Sita Nathan: This will vary anywhere from six to 12 months, right? It all depends on the project and the resources a client have. If it's a smaller project, I would say it could be done within six months. But if it's larger project, which involves just not one component of migration, because they may have opened up the hood, say it's an MDM project, they've opened up the hood, but there is a small piece of migration that's part of it, right? Then it could take a longer time. But in general, I think everybody needs to be aware it's not just the migration piece of it, but the post migration. Once everything is deployed, then run this for the next two, three months so that you are very comfortable in the thought that any issues that come up post migration can be resolved while you're not disrupting the current processes that users are working through. George L’Heureux: Sita, as we get set to wrap up here, what's one thing that you would want someone who's watching this or listening to this today to walk away remembering, to have learned? Sita Nathan: Yeah, don't be afraid of migrations. It's something that everybody needs to go through this journey in order it to be able to get more information and insight and be able to come up to with the better technologies that are available. Because all these are something that, yes, it is challenging, but at the end of the day, you can overcome it by working together between the client, the vendor and the technology to team working together as a team, we can be able to drive it. Will there be issues? Absolutely. But we can all work together to solve it. See what options are there and then have a successful migration. George L’Heureux: Our guest expert today has been Sita Nathan, a Senior Solution Design Consultant at Dun & Bradstreet, and this has been Data Talks. If you've enjoyed today's discussion and we hope you have, please let a friend or a colleague know about it. If you'd like more information about what we discussed on today's episode, please visit www.dnb.com or reach out to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L’Heureux: Hello, everyone. This is Data Talks, presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a Principal Consultant for Data Strategy in the Advisory Services team here at Dun & Bradstreet. In Advisory Services, our team is dedicated to helping our clients maximize the value of their relationship with Dun & Bradstreet through expert advice and consultation. On Data Talks I chat every episode with one of the expert advisors at Dun & Bradstreet about a topic that can help our consumers of data and services to get more value. Today's guest expert is Matt Schroeder, a Data Advisor at Dun & Bradstreet. Matt, how long have you been with Dun & Bradstreet? Matt Schroeder: Just over 22 years. George L’Heureux: And you've recently moved into this role on the Advisory Services team. Can you tell me a little bit about what you're doing in this new role for you? Matt Schroeder: Really I'm focused in helping our strategic customers really on optimizing the match. Because we know that when they come to D&B with their customers, suppliers, prospects, it's important to get an accurate match so that they get the data that they need, and get accurate data. George L’Heureux: So Matt, we wanted to chat a little bit today about how understanding the full context of data that you're working with really helps you to be successful at accomplishing what your business goals are. Before we get a whole lot deeper than that, though, let's talk about what we mean when we're talking about this data context. Matt Schroeder: With data context it's really, what's the definition, what's the shape? What does the data elements that you're receiving actually mean? For example, if you're looking at sales figures or any type of revenue number, is that summarized by millions, billions, or is it the exact number? If you're looking at other data elements such as employee figures, any type of thing to size a business, is it modeled or are they actual numbers? George L’Heureux: That type of numeric precision, definitely one example of context, but it's far from the only one, right? Matt Schroeder: Exactly. I mean, sometimes it could be simple as a flag, whether you're looking at ... D&B has a public-private indicator or an out-of-business flag. And knowing what that definition is, is key to utilizing that data. I mean, just by listening to the flag you might think, "Oh, I know what it means. I'm 100% right." But let's say the true definition of that public-private flag is really, is that particular company, that site, traded on a stock exchange? If your definition is different, that's putting risk in any decisioning that you're making. Or if you look at the out-of-business indicator, is it the whole company's out of business or are they just not operating at that particular address? Two completely different definitions that sometimes people don't think about. George L’Heureux: Great points. And when I think about it, I'm also thinking about things like how complete is the coverage, globally, nationally, just regionally. Or are there special values that really are the equivalent of null or something else? That's the type of stuff that I also think about too. Matt Schroeder: Oh, exactly. It's really important to understand the scope of your project when you're looking at those data elements. Is it a global project? Is it just a certain region of the world? Is it just the US? Is it on customers, suppliers, or including prospects as well? Because depending on what that scope is, where the geography, are those elements populated? I mean an element that's in the US might not be in Europe for example, or in Canada, or it may be just in part of the world. And what are you going to do with those null values, as you mentioned. George L’Heureux: We've kind of talked through a couple of really good examples, numeric precision, those indicator definitions, coverage, completeness. But what if that doesn't happen? What if you don't have that context, what sort of problems can you run into? Matt Schroeder: It could throw off your entire analysis or decision making. If you're looking at revenue or sales amount for businesses, for example, trying to define your own sales people's sales territory, so trying to balance it out. If that information's off in any way, you could have more customers, larger opportunities heavily weighted in one area and not the other. And then you're overworking employees. If you want to have things well-balanced or whatever your goal is, it's important to know the context of that data because it could just throw off your project or what you're trying to accomplish. George L’Heureux: You've stated good reasons why we need to have a good grip on data context, but how do we go about getting there? How do we go about getting to a point where not only do we have data context, but we've got enough, and we know that it's enough in order to make those types of good decisions? Matt Schroeder: It's really talking to the teams that are providing the data. Whether it's internal data or external third party data, talking to the team that's gathering or providing that. They're going to be the experts, in this case data advisors on that case. Usually in those situations there's some sort of data dictionary, some sort of documentation that can be provided that walks through, hey, what does this particular data element mean? What's the scope of it? And utilize those. Rely on experts, rely on your ... If it's D&B data, rely on your data advisory team to help you with this. Rely on the delivery people that are providing the information and that data dictionary. There's really no dumb questions. It really comes down to asking so that you understand so that you can properly use that data for the use case and accomplish your goal. George L’Heureux: I mean, we regularly deal with customers who have hundreds, if not thousands, of data elements that they're working with. And in order to really get anywhere, you have to be able to prioritize. Are there types of fields that maybe in general are more susceptible to these types of problems, with lack of context? Matt Schroeder: That is a great question. I really think every data element has some level of susceptibility. It really comes down to what’s your use case and what you're trying to accomplish. When you're defining that and the data elements you're gathering, you'll be able to parse out. And really, that's where our expertise comes in or whoever's providing the data. That's where their expertise comes in to help you define what's more important. Some will be elements that are just coming along as reference. Others are going into decisioning and modeling, which will be key on what you're trying to accomplish. George L’Heureux: It's interesting, the more and more of these conversations that we have here on Data Talks, the more and more we realize that things really just continue to come down to what's your use case. And it sounds like that's what you're saying here again. Matt Schroeder: Oh, definitely. George L’Heureux: We've been talking a lot about how to use context information to really make more effective use of data, but let's approach it from the other side now. How do we make sure that if we have data, that we can convey that type of context to downstream users who have to try and make use of it? Matt Schroeder: It really comes down to documentation. I mean, if you are having high level reporting, having some sort of document or context in there. Or if you're presenting it, explaining that as you're going through there. Documentation's also important, along with those data dictionaries, to help represent those values, depending on how often you do these types of projects or reporting. You might not be the one that's doing the project the next time or the next year or the next quarter. So having that documentation, whether it's you or another party at your business, it just, it's going to make your job easier. You're not going to have to start from scratch. You're going to have that fundamental of documentation to be able to run your report and make your decisions. George L’Heureux: Yeah, you help others to avoid having to reinvent the wheel from scratch every time. And we know that Dun & Bradstreet already does a lot of this. We have a full data dictionary that goes into that sort of detail like you were talking about. George L’Heureux: In your experience, are there any stories that jump out where you've seen the power of data context really take a front seat? Really show how important it is and why it made a difference in a project? Matt Schroeder: Yeah, actually just recently, you'd mentioned I just recently moved into this Data Advisor role. And I was brought in to help a particular customer after the sale. They were using a lot of our data, diversity data and other information for reporting and compliance on their suppliers. And they were getting some feedback that they didn't trust the data. There were some questions that had came up. We rolled up our sleeves, went in, what were the issues? What were the questions? And it came down, I know I mentioned it earlier in our discussion, that public-private flag. You think it's simple. And as we dove into it I found out that, we were matching their suppliers, we were appending the data to it, but the particular customer didn't understand, first of all, it's site specific. And when they were doing the reporting, they would provide information at the parent or the ultimate level. And so they were getting some questions that came back to say, "Hey, you've got this flagged as a private company and it's public." And what we discovered is, when I went through the definition of the data element them and, "Hey, this is site specific. And is this particular company traded on a stock exchange?" And what we discovered is a lot of their suppliers were owned by larger public companies. And with the definition of this flag, it wasn't. This particular company was not traded on the stock exchange. It was a wholly owned subsidiary by a large public company. So that created some confusion with them and the reporting. We also discovered, with their suppliers, they were global. In that case, this particular element is only in the US at this point. Just because when you go outside the US, different countries have different definitions of what public and private are. On all their global suppliers it was just coming back blank. Once we rolled up our sleeves and basically help them define what this field was, they were able to adjust their reports for the US. And then globally, we were able to work with them and provide additional data elements to really help them understand which global customers were truly public and which ones were private. George L’Heureux: I think, in that answer I found something really interesting, which is that you'd said, "You'd think it'd be simple." And it's not that it's not simple, it's just that your simple understanding may not be the same as my simple understanding, which may not be the same as the real simple understanding. Matt Schroeder: Exactly. And that's why it's important to understand the definition and ask the questions. And it may not come down to an element by element level, but whatever project, whatever use case you've got in mind, utilize your teams. If it's internal data, utilize them, tell them what you're trying to accomplish. Work with your sales and consulting teams at D&B. Explain to them, "Hey, this is what we're trying to accomplish," so that you can get the data elements. And that way they know your scope. They're the experts on their data elements, and they'll be able to appropriately say, "Hey, this is what you need and this is the definition." George L’Heureux: Matt, as we prepare to wrap up here, what's the bottom line takeaway that you would want someone who's watching this or listening to this to walk away with? Matt Schroeder: It's really just, know your data. Based on your use case, based on what goal you're trying to accomplish, make sure you have a good understanding of that data. There's no dumb question. I've been here 22 years. I learn something every day. Every day that I work with the data, I'm learning as I go. And so that there's never a dumb question. Make sure to ask it and then document it. By making assumptions you're just adding risk to your project. George L’Heureux: Well, thank you, Matt. I appreciate you taking the time today to sit down and share some of your perspective on this. I think it's been a really valuable conversation. Matt Schroeder: Oh, thank you. George L’Heureux: Our guest expert today has been Matt Schroeder, a Data Advisor at Dun & Bradstreet, and has been Data Talks. I hope you've enjoyed today's conversation. And if you have, please let a friend or a colleague know. And if you'd like more information about what we discussed on today's episode, you can visit www.dnb.com, or you can talk to your company's Dun & Bradstreet specialist today. I'm George L'Heureux, thanks for joining us. Until next time.
George L’Heureux: Hello everyone. This is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a Principal Consultant for Data Strategy in the Advisory Services team here at Dun & Bradstreet. In Advisory Services, our team is dedicated to helping our clients maximize the value of their relationship with Dun & Bradstreet, through expert advice and consultation. On Data Talks, I chat every episode with one of the expert advisors at Dun & Bradstreet about a topic that can help consumers of our data and services to get more value. Today's guest expert is Andrew Merkel, a Data Strategy Consultant at Dun & Bradstreet. Andrew, how long have you been here with the company? Andrew Merkel: Yeah. Hi George. Thanks for having me. So I started my career with D&B back in August of 2008, working out of the Atlanta area with one of our D&B multinational clients. And recently I marked my 13 years with the organization. George L’Heureux: Congratulations. Andrew Merkel: Thank you. George L’Heureux: Tell me a little bit about what you're doing these days as a Data Strategy Consultant. Andrew Merkel: Absolutely. So today as a D&B data strategist, I am supporting Dun & Bradstreet's clients to help them understand, organize, deploy, and grow in their data journey. My role is to understand the client's goals and challenges, and to create a realistic expectation of maximizing data and technology assets to make sense of data and how to deploy those across their organizations. Andrew Merkel: I would say the most impactful aspect of what the D&B data advisory service provides is optimization around identity resolution that helps our clients with their overall business performances. George L’Heureux: Wonderful. Thank you for that. I'm sure I don't have to remind you or anyone who's watching about the way in which the COVID-19 pandemic has caused disruptions and changes to businesses around the country and across the world, which is why I thought that the topic that you wanted to discuss today was so important. This idea of what the pandemic experience can teach us and has taught us about how to approach master data and specifically matching like you were talking about. Andrew Merkel: Yeah. The reaction to COVID back in March of 2020 was felt almost immediately and almost non-discriminatory amongst any size or industry, and that was due to forced shutdowns, globally in many cases, and we saw significant economic retraction as a result of this. And it's not over either. The impacts are still here, we're still living through this and learning from it, and it's changing. It's interesting to try and examine, generate learnings that can really help impact an organization's data governance and other business policy. George L’Heureux: So you mentioned impacts that are still ongoing. Let's talk about some of those. What have we seen happening? Andrew Merkel: One of the biggest things that we've seen happening is on the credit side. So creditors, they didn't understand the true risk going into the pandemic situation. They were overexposed on their credit based on the positive selling environment leading into March of 2020. So some still haven't addressed these risks. Some creditors are dealing with different parts of the same enterprise and they don't have visibility into that, and that's just compounding this risk over time by not knowing the full exposure of a corporate family, and this is creating a credit failure nightmare during the pandemic. Andrew Merkel: But we also see a similar incident occurring on the vendor side of the house, not understanding the vendor risk of failure, complexities from a corporate family exposure, or at risk vendors supplying multiple services or products to an organization. And at the same time, you have to adapt to survive, and so lots of companies are rushing to expand or pivot, moving outside of their comfort zone, misidentifying a critical vendor, misestimating these risk levels that are out there and not acting fast enough on overexposed credit limits. And it's creating a huge problem on multiple fronts of an organization. George L’Heureux: So a lot of companies are encountering these problems. They're having to deal with these impacts. And I'm sure a lot of them are saying, how could we have prepared for this? Now, COVID-19 seems like it would be once in a lifetime, let's hope. Can you really prepare for something of this scale? And if so, how do you do that? How can Dun & Bradstreet help you do that? Andrew Merkel: You can absolutely prepare for it, and it starts out foundationally by having a sound data governance policy in place. And you also have to limit the overriding of rules in that policy to maintain compliance and due diligence across your various divisions. And whether it’s customers, your prospects or suppliers, you need to be taking the right level of due diligence to make sure that you know who they are, the risk levels associated, and the supply chain exposure or the credit exposure that you could be facing on a multinational family. And let's face it George, even when the economy's great, companies that stick to the rules are going to have a better outcome and have the policies in place to prevent this from happening in the future with other pandemics or unforeseen events. George L’Heureux: That's true. But to stay in the world of reality, not every place has that sort of preparatory stuff in place. They don't have those data governance committees in place. And so when COVID hit, what did companies like that do? Were they doing nothing, or did they do something that just wasn't effective? Andrew Merkel: Well, I think it came as a shock to a lot of these organizations, because if we remember the economy prior to March 2020, it was in overdrive. It was a seller's dream. You have this hyper-growth going on. Lots of expanding, opening of new branches, and the focus of most businesses in that economy was, how do we sell more? Organizations were looking at taking on more clients, extending more and higher credit to draw that business in, and in an economy like that, you can almost afford a few missteps, be looser with your credit policy, you can even misidentify clients or vendors. But to support that growth, on the other hand, organizations were onboarding new vendors to keep up with that increased demand. So you're brewing the perfect storm. And doing so quickly, many organizations, they were sourcing the same commodities and services from new suppliers. So you partner that up with the increased credit limits, and you brewing that perfect storm, and it's not something that you can do in a COVID like economy. George L’Heureux: So let's go back to something you mentioned a moment ago in that last response, one of the components of that perfect storm, that sort of misidentifying who you are doing business with. How does that sort of high impact misidentification occur? Andrew Merkel: That occurs, honestly, by not having a trusted source of information. You're either not pulling enough, or the right information back on your customer or your vendor, and maybe you're moving at a faster pace because of the improved economy, or you've got a smaller, nimble team, but you lose sight of the ongoing identity management and the continuous data hygiene, and you're digging yourself a giant hole. Andrew Merkel: A big driver is that sometimes you're also dealing with two parts of the same company, because you don't realize they're related, and that ties back to our identity resolution. And so your issues, whether it be vendor risk or customer risk, begins to magnify the effect of those risks during a heightened pandemic or other unforeseen events. George L’Heureux: I'm glad that you tied it back to identity resolution. Obviously that's one of our strengths here at Dun & Bradstreet. How long did it take into this pandemic before companies started realizing that they needed to make changes? That they needed to basically adapt or die? Andrew Merkel: Honestly, it took most people, I would say, just a little bit too late. You look at certain geographies, like New York City, locked down. While that doesn't have a huge impact maybe on the rest of an organization, as those lockdowns began to expand and extend, entire organizations and even industries were becoming very vulnerable to the pandemic. And that starts to impact your supply chain, and rapidly increasing days past due on credit where people can't pay you, and that absolutely crushes your organization's cash flows. Andrew Merkel: And, so, suddenly these companies found out that they needed to better understand and predict risk, but it was almost too late. They'd already began to have people default on payments and already began to see their vendors starting to fail on them. D&B, I would say, was stepping that up. We brought in the COVID Index Score, and that was a measurement of a company's likelihood of being impacted by the pandemic. So our customers were receiving that benefit during the beginning of the pandemic and all the way through to today. But I guess George, to tie it back, the fact is companies that struggled early tend to struggle late and are still possibly struggling today. George L’Heureux: So Andrew, you mentioned three, four, maybe five different things that companies who are looking to try and make their way through this pandemic era could do, and you and I both know that those are all areas in which Dun & Bradstreet could help, but could you go into a little bit of detail around how? Andrew Merkel: I would say that the first thing that we would recommend companies out there do is use match to tie to the right entity. It's really the most crucial step, is that first right off the bat identity resolution. Find out about their corporate family tree, their structure, their risk across that tree and how it could potentially impact your organization. And then using things like industry, revenue, employee figures, to find similar companies, healthier suppliers, and prospects to continue those operations is crucial. And then, you need to get predictive. You need to use things like the COVID Index or D&B's Predictive Risk Scores and other traditional elements to show you where the risk is truly located, and always be monitoring this process in an ongoing effort to maintain data hygiene as part of your or strategy. George L’Heureux: So this may be difficult to quantify, but perhaps you can do this really quickly. As a result of the pandemic, is it possible to really assess how companies’ approaches today to identity resolution compare to what they were say at the end of 2019? Andrew Merkel: Versus the end of 2019, today, you need to have a more extensive due diligence policy into your identity resolution. It's as simple as that. It's become such a higher prioritization to know who you're doing business with, whether it be a prospect, a customer or a vendor. You need to take a deep look into the family and the global risk, not just the local, but the global risk that could be impacting those organizations, and making sure that your credit limits being allocated are in line with the businesses' projected bad debt. You may want to reassess things like bad debt and your overall risk policies that you should be tolerating during a time of pandemic or not. Andrew Merkel: A big question is understanding who are our vendors? How do we check the compliance and understand how we'll be supported should the unexpected occur? Those are some great questions that organizations should be asking themselves. And then some companies are going to survive and thrive during a pandemic, and those are the ones who are likely using Dun & Bradstreet to look for expanded insights and opportunities, even during what's perceived as a global pandemic. George L’Heureux: So let's break it down before we head out and finish this up. Andrew, what did we learn when the pandemic hit? How did the way that companies are looking at matching and data management change? Andrew Merkel: So what did we learn about match? I would say match rules may need to change. Many organizations may need to get very, very tighter on their match rules up front to identify risk and increases in risk across their business. Knowing your suppliers, knowing your customers is crucial, and it's an ongoing process. It's not a one and done process. Being prepared with backup vendors and a secondary sales target market based on who your best vendors, your least risky vendors, and your best customers are that are going to survive in tough times. Being able to mirror them is crucial for having that mitigation plan. But I would say, lastly and most importantly, is maintaining that strong identity resolution and that ongoing data hygiene process. That's really going to be crucial at all times to make sure that your business is performing optimally and going back to its data governance policy. George L’Heureux: And it's something that we preach every day here at Dun & Bradstreet. Andrew, thanks so much for joining me today and sharing your expertise on this topic. Andrew Merkel: Absolutely. Thank you, George. George L’Heureux: Our guest expert today has been Andrew Merkel, a data strategy consultant at Dun & Bradstreet. This has been Data Talks. Hope you've enjoyed today's discussion, and if you have, I encourage you to share it with a friend or a colleague. And for more information about what we discussed on today's episode, you can visit www.dnb.com or talk to your company's Dun & Bradstreet representative today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L'Heureux: Hello everyone. This is Data Talks, presented by Dun & Bradstreet. I'm your host, George L'Heureux. Today's guest is Jason Simmons, a Data Strategy Consultant at Dun & Bradstreet. Jason, we wanted to talk a bit today about employee counts and revenue figures and how Dun & Bradstreet helps our customers with that. First off though, why are employee counts and revenue figures typically so important to clients? Jason Simmons: Well, the big thing is that many of our clients want to effectively “t-shirt size” their customers or their prospects. They want to t-shirt size their marketplace, so that they have a general sense of how big their clients are, or the entities that they're trying to sell to, or do business with, in some way, shape, form, or fashion. George L'Heureux: So, I think I got it. Why is knowing that general sizing so important? Jason Simmons: It's really about aligning the appropriate sort of treatment and operations to those different sized businesses. Some clients, large or small, require certain treatment strategy. And it's also... where do you want to spend your limited resources? No one has unlimited amounts of employees. Certainly no one has unlimited amount of budget to spend on marketing. So, it's like trying to figure out where you can get the best bang for your buck. And the first thing is knowing, who are your big fish, your medium size, your small and so on and so forth. So, you can align your function and your processes against those. George L'Heureux: So like you said, we're basically talking here about a method of prioritizing, segmenting, right? Jason Simmons: Yes. We see that time and time again. I think the biggest use case that we see for that is sales planning cases. So, few people are working with an unlimited amount of sales resources. And so, figuring out where your big opportunities, who are your big customers or big prospects, and then aligning your sales makers against that, is a pretty typical use case that we see. George L'Heureux: And to do that, employee counts and revenue size. Those are two of the primary metrics that our clients are using to try and create those t-shirt sizes to do that alignment, right? They're not the only ones, but they're kind of the biggest ones. Jason Simmons: They are the biggest ones. I mean, everyone imagines that every entity out there, has those two measures inherently. And that they correlate to the size of operation that you're dealing with. There are other measures and some of our more complicated customers, or more mature customers on this journey use other factors like, the amount of square footage that's leased. For example, for warehousing, if they focus on products that would lend themselves to wanting to understand the size of a warehouse or size of square footage rented, then they might use those other sort of extraneous values. But for the most part, I think pretty commonly we see revenue and employees being leveraged. George L'Heureux: And while those are probably figures that are relatively easy to come by, when you're talking about publicly traded companies, companies that really have to do a lot of reporting and get those numbers out there, in order to get government funding and things like that. When you start talking about smaller companies, companies outside of the United States, they become a lot harder to come by. What's Dun & Bradstreet doing to help customers get an idea of employee counts and revenue sizing for those types of entities? Jason Simmons: Well, D&B does our absolute best at acquiring as much publicly and even privately available information that's out there. We're trying to put as much of a detailed profile around every business that we possibly can in the world. And in our data cloud, we have roughly 450 million businesses that we uniquely identify with our D U N S number. And we try to put as many accurate profile around each of those businesses we possibly can, including revenue and employees. But it's not an easy task. On the publicly available information, like companies that are traded publicly, they regularly advertise that information. You can go get it in annual reports, you see it on websites, those sorts of things. But in privately owned businesses that information's not as easy to come by. Some companies do advertise that regularly on their webpage or in financial documents that we can get a hold of. Jason Simmons: But in many cases, this is not available. And in some cases, businesses don't want that information be available in their setting, in their country or what have you. They may not want that information to be known, how much money they're making, how many employees work for them. So, we end up with data that... In some respects, in some parts of our database, we have very reliable and accurate information. In other places we have... we're missing values. We're not able to collect that information. And that can be very difficult then to try and do t-shirt sizing off of a value that's effectively not there. George L'Heureux: Well, that's where modeling comes in. Right? And, and we've got a model, the Global Sales Employee Model that helps to fill some of those gaps. Can you talk about that a little bit? Jason Simmons: Absolutely. So, I've been at D&B now, almost going on 10 years here, and prior to coming to D&B and being a customer of D&B. There's been a couple of iterations of an answer to this from D&B, the latest of which is called the Global Sales Employee Model. And so, to overcome these gaps, statistics is typically what folks use, and we've hired some really smart data scientists to take and ingest the information that we know is good, that comes from publicly available sources. And we've verified that information. And we use that to model accurately for the cases where we don't have that information. And then we're actually able to test, what does the model say? It would come up with from a value perspective for the cases where we actually have an actual value, and we're able to measure essentially accuracy against that. And they've refined this over a number of years. And effectively, now we're able to put an employee and a revenue value consistently across all 450 million records in our database. George L'Heureux: So Jason, as we wrap up, having talked about GSEM and sort of the importance of employee counts and revenue numbers, what's the one thing that you want people to walk away with as they listen to this conversation? Jason Simmons: I think the big thing is just understanding the reality around global customer data is that, no one's going to have actual values on every business in the world. And there's a lot of suppliers that provide data like D&B. You can spend a ton of time trying to chase down actual values to base your segmentation processes off, of your t-shirt sizing processes off of, or you can just let us do the hard work and just use the data that we've got. Use our model, definitely encourage clients to try it out... proof of concept and let the data speak for itself. I've seen it used time and time again and clients to be very successful with it. So that's, that'd be my encouragement to clients. George L'Heureux: Thanks, Jason. I really appreciate you sitting down and talking a little bit about sales, and employee figures and how clients can make use of them. Jason Simmons: Awesome. Thank you. George L'Heureux: Our guest expert today has been Jason Simmons, a Data Strategy Consultant here at Dun & Bradstreet. This has been Data Talks. If you've enjoyed today's discussion, please let a friend or a colleague know about it. And for more information on Dun & Bradstreet, or about today's discussion visit www.dnb.com or talk to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L’Heureux: Hello, everyone. This is Data Talks presented by Dun & Bradstreet, and I'm your host, George L'Heureux. I'm a Principal Consultant for Data Strategy in the Advisory Services team here at Dun & Bradstreet, and in Advisory Services, our team is dedicated to helping our clients to maximize the value of their Dun & Bradstreet relationship through expert advice and consultation. On Data Talks I chat every episode with one of the expert advisors at Dun & Bradstreet about a topic that can help the consumers of our data to get more value. Today's guest expert is Scott Garner, a Data Strategy Consultant at Dun & Bradstreet. Scott, how long have you been with the company? Scott Garner: Thanks for having me, George. I've been with D&B six months now. George L’Heureux: Can you tell me a little bit about your career before you joined D&B? Scott Garner: Sure, absolutely. So right after college, I joined Dell, and spent 18 years at Dell Technologies. I spent the first five years as a sales rep, supporting state and local government accounts for the State of Texas. When I moved out of sales, I moved into several different operational roles, all still very much supporting sales and marketing as well. Over the next 10 years, I had the opportunity to serve in a couple of different leadership roles for business intelligence, and it was during that time that, of course, I already had a passion for business, but I also developed a new passion for all things data. In 2014, I decided to spread my wings a little bit, left Dell, and joined another high tech company here in Austin, a much, much smaller company, but still in the high tech industry. I served that company in a slightly different role, in that I was still doing data, and still very much informing the business, but since it was such a smaller company, I got visibility to the full data supply chain. So instead of just doing reporting and providing it to stakeholders, I got to see a little bit of behind the curtain, a lot more behind the curtain, to be honest, a little bit about how the sausage is made, and all the complexities that go into that. George L’Heureux: And all of us who've been in data for a long time, myself included, and I call myself a data geek proudly, we all have areas of the industry that we find really very interesting and very close to ourselves. And before we got on today, you were telling me that what we're going to discuss today is actually one of the reasons that you decided to join Dun & Bradstreet. Can you tell me a little bit about that? Scott Garner: The implications of not having clear ownership of data and managing data can be very, very large. There's certainly areas of the business where directionally correct information is fine, but when you find yourself at a crossroads where the company, the whole company, is going to measure themselves based on a specific data attribute, it's going to be really important that you have a clearly defined version of truth. For my journey, D&B was a critical part of that, but one of the big lessons that I learned, the mistake that I personally made, was I went into that process thinking D&B is going to solve this problem. They have the truth that I'm looking for, and I can't wait for them to solve this problem. George L’Heureux: So let's dig into that. You talk about the truth that you're looking for, and at least in my experience, that's always somewhere in between what I have, and what somebody else can give me. Is that what you mean when you talk about the truth that you're looking for? Scott Garner: You nailed it. That's absolutely it. I think probably the best way to explain it might be with a fictitious example, and I'll just throw one out there. Hopefully it resonates with you. So there's a ABC corporation. They've decided that they'll run the business based on maybe industry segmentation. Right? Let's put all of our customers into specific industries. For this example, we'll just say half the company is going to be assigned to mining. Right? A mining industry, and then the other half construction. You can see where this is going. It won't take more than 30 seconds for the company to realize some of our customers, in fact, some of our big ones, they actually fall into both. Right? George L’Heureux: Sure. Scott Garner: At any given time they could be a mining industry or a construction, and so that dilemma is very real, and that's when companies realize, "I need a source of truth," and you're exactly right. They're faced with, well, I can look at my internal systems, and that tells me a story. I'm so anxious to go get a third party, because they'll tell me what I think is truth, but that's not the reality, right? Companies, they find that what they have isn't what they're after, and then sometimes they find what we found, which is a third party provider has goodness, but it's not necessarily the truth that we're after. George L’Heureux: So what's happening in these cases, right? I mean, at Dun & Bradstreet we're very proud of the data that we've collected, and that we have in the data cloud. We believe it's the best that's out there. Is that data really wrong, or is there something else at work when you start looking at that and saying, "That's not what we're looking for as a consumer of the data"? Scott Garner: Well, the word “wrong” there is the key word, and that's really the thing about what we're talking about. It's human nature to want to define “right”. It's just in our human nature, and every one of us can likely articulate what we mean when we say, "That is right," or, "That is correct. That is accurate." But the truth of the matter is what you see when you look at your internal data, it may be right. It may be wrong. What you're really saying is, "It's not what I need." And then when you go get data from a third party provider, if it's not what you need, the reaction is most often, "Well, the data is wrong." And the reality is it's not wrong. It's not wrong at all, and in fact, the experience we had with D&B is rarely did we find data that was wrong. What we found was in some of our most important use cases, the data we have, and the data D&B brought to the table, isn't always exactly what we have in mind for our truth. George L’Heureux: So when you get to that point, when you realize that you're still a few steps away from what you're calling your truth, what you feel like you need, how did you as a practitioner, how do our clients, how are they supposed to resolve that type of apparent disconnect? Scott Garner: Yeah, and that's really where the meat of this multi-year journey is, the journey that I've been telling you about is if the truth that we're after isn't in our internal systems, and if we don't see it specifically in the third party data provider, then what's necessary? And what we learned is we have to create and maintain a third data source. Right? That doesn't mean you get rid of either the left or the right, but you pursue something that's in the middle, and that thing in the middle, it doesn't exist at the beginning of the journey. It's something that has to be created, cultivated, groomed, and nurtured over time to become your source of truth. Scott Garner: That third entity, that thing in the middle, is a combination of what is the goodness that we have in our internal systems, what is the goodness we can get from our third party data partner, and then ultimately, what do we as a company want our truth to look like? In some cases, it may come directly from the third party. In other cases, you may say, "I need to adjust this specifically for my specific use case." George L’Heureux: And so that's where a lot of the ideas around data governance, data stewardship start to take over. You have a group of people who are dedicated to kind of defining that end goal, and figuring out how to take a little bit from A, a little bit from B, and consolidate then to get as close to that idea of truth that you're looking for as you can. Scott Garner: That's right. That's truly what ownership means. You nailed it, George. It's like I've got data elements, and I've got data elements, but the truth, the ownership that is needed, is investment in people and resources. George L’Heureux: So what would be some examples, lets kind of speak specifically here, where you could start to build up that truth, that version of truth that you're looking for? If you're a company, if you're that ABC company, and you've got mining, and you've got construction, like you were talking about before, and neither what's internal, neither what you've gotten from an external third party source is exactly what you're looking for. Do you start setting up rules, heuristics? How are you determining what to take from A, and what to take from B, and who does that? Scott Garner: Yeah. Well, one of the things we applied, which is fairly common, is kind of the 80/20 rule. And if resources are scarce, and you don't have a lot of investment in this area, you can still make tremendous progress specifically on the 80% with some very basic rules. And an example would be we're not going to apply any human effort on any company, or any order for that matter, that's below a certain threshold. Right? And you set a threshold, and you may need to tweak it over time, but you set a threshold, and anything below that, it just goes in based on whatever the data system tells you, but anything above that threshold, then you start to apply resources. Right? You apply internal governance, oversight, whatever it may take to get it "correct." George L’Heureux: And so these rules might be based on revenue. They might be based on industry classification. They might be based on the number of employees or the size of the prospect, the prospective account, right? Do you... Or I suppose the better question is, how do you build up that rule set over time, and make sure that they evolve to keep up with what you're doing? Scott Garner: Well, the whole topic we're talking about is your defining your truth, and the "your" part of it is exactly what you're defining. Each company is going to have a different answer for any specific customer attribute. That doesn't mean one company is right, and one company is wrong, it's just different based on the use case. So the beauty of the governance and the stewardship we're talking about is it's living. Right? Scott Garner: And you can start with an existing set of two or three rules, and you can watch how those rules impact your information, and I mentioned that this was a journey for my experience. The journey was that evolution, right? Let's start with these three rules, and let's see what that would look like. And after a month or two months, we tweaked the rules and adjusted it, and a multi-year journey is you're tweaking those rules to really hone in on and get closer to what you define as your truth. George L’Heureux: So what sparks tweaking those rules? Is it encountering an exception? Is it encountering a case where it doesn't work out right? Scott Garner: That's absolutely correct. Whenever the rules that you set in place didn't work for a specific situation, you adjust it, and you change the dial slightly to see if it captures a majority of what you were trying to capture. George L’Heureux: Do you have suggestions on who inside the company should be involved in making these sorts of decisions around the rules and when to adjust them? Is it just a data team? Is it IT? Is it broader than that? Scott Garner: It gets back to the whole ‘your’ part of the equation. If you think about the meme, "Who here is responsible for clean data," if all the hands go down, then you're back to square one. So there's not a one size fits all answer to your question. It will absolutely involve IT and a stewardship team, whether it be in operations or otherwise, because that's where reality is happening, but you also have to have investment from other key areas in the company, whether it be finance, sales, marketing, whatever. Whatever the attribute is that matters to those organizations, they must play a role in helping make some key decisions. George L’Heureux: So I kind of want to circle our conversation back to much earlier in it, and where you said that this is one of the reasons that made you really excited to join Dun & Bradstreet. Obviously, you joined Dun & Bradstreet because you feel like as part of the organization, you can help people solve these problems that you had encountered. So let's ask the question, how can you help? How can Dun & Bradstreet help? Scott Garner: Well, one of the things that was most exciting for me when I got this opportunity to join D&B is when I look back at my journey, the ingredients, the data that we had, and the data we got from D&B, the ingredients were critical. We could not have gotten to where we got to without D&B's data. Having that third party perspective was crucial, but the other thing I learned is there's more to it than just ingredients. Right? There's the ingredients that we have, ingredients that we purchase, but then in the middle there's this blender, so to speak, and that blender is human insight, and human governance, and human stewardship all again geared towards what the company is trying to achieve. Scott Garner: And one of the reasons I was so excited to join D&B is D&B recognizes that this journey is bigger than data. Right? It's bigger than data ingredients. Yes, we're the industry leader, and the best possible option you have when it comes time to partner with a third party vendor, but we also have multiple resources that are here with experience and expertise, that can help guide and consult companies as they begin or proceed through their own journey. And so for me being a business guy and a data guy, this is a dream come true, helping other companies through this journey. George L’Heureux: So as we wrap up, Scott, if people are watching or listening to this, is there one takeaway that you hope people will walk away from our conversation having had as an insight? Scott Garner: Yeah, it may not be one, but I'll wrap it up very quickly. So I'll tell you, guys, if you're listening to this, the struggle that you're facing is real. Right? The need and desire for truth is absolutely real, and it can be achieved. Third party providers are an incredible part of the process. D&B happens to be the best, so that would be a good thing, but ultimately, your version of truth is in your control. You own it. George L’Heureux: Well, hey, Scott, I appreciate you joining me today and talking through a lot of this. We're glad to have you here at Dun & Bradstreet consulting with us, and helping our clients to achieve success on their data management journeys, like you've talked about. Scott Garner: Thanks, George. I appreciate it very much. George L’Heureux: Our guest expert today has been Scott Garner, a Data Strategy Consultant at Dun & Bradstreet, and this has been Data Talks. We hope you've enjoyed today's discussion, and if you have, please let a friend or a colleague know about it, and for more information about what we discussed on today's episode, visit www.dnb.com or talk to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us, until next time.
George L’Heureux: Hello everyone. This is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a principal consultant for data strategy in the advisory services team here at D&B. In advisory services, our team is dedicated to helping our clients maximize the value of their relationship with Dun & Bradstreet, through expert advice and consultation. And on Data Talks, I chat every episode with one of the expert advisors at Dun & Bradstreet about a topic that can help consumers of our data and services to get more value. Today's guest expert is Cecilia Petit. Cecilia, like me, is a principal consultant for data strategy. Cecilia, how long have you been with Dun & Bradstreet? Cecilia Petit: Hi, George. It's coming up on six years, actually two weeks from now. Pretty unbelievable. Wow. George L’Heureux: Tell me a little bit about how you view this role that we share. Cecilia Petit: Yeah, so, I think of it as DAS first. So as you said, in your introduction in data advisory, we help our clients get value from their investment in D&B data and tools. And as principal consultants, we get to really amplify that mission by helping spotlight the team's expertise through thought leadership pieces and bring some of our own experience to help clients solve for their use cases in direct engagements. George L’Heureux: I think one of the really interesting things I've learned about our team is that we've got folks who have been here for decades. They've got decades of experience at Dun & Bradstreet, but we've also got team members who once were customers for a long time, and that includes you. I've found that it really provides an interesting perspective on how this partner relationship works best. I had hoped we could maybe explore that a little bit. Can you tell me a little bit about your experience when you were a customer of Dun & Bradstreet? Cecilia Petit: Yeah, right. Yeah. So much tenure on the team. So for context, when I was a customer, I was leading a marketing analytics team. So we had analysts, modelers, developers, well, one developer and the D&B data was a key ingredient to demand propensity models that fed everything from ROI estimates on large capital investments, to territory planning, to prioritizing leads. So my experience, this is going to be the most boring answer ever, but it was great. Everybody was really good to work with. The relationship manager was efficient. The market inside consultant, shout out to Nancy Hemperly, always took our call. She was always helpful, knowledgeable. So, really not much of a story here because it's not until I joined that I started to realize from talking with other former customers and working with my own clients every day, that I could have gotten a lot more from the relationship with D&B. George L’Heureux: Why do you say that? What do you think it was that was maybe keeping you from getting as much as you could have from your relationship with Dun & Bradstreet? Cecilia Petit: So there were a couple factors, but what I've seen is that, in talking with some of our colleagues, that they got a lot more attention. They got workshops, they got one-on-one help on their use cases. And I didn't see that type of help. I was only seeing the data specific or the tool specific help. So now I'm seeing that there's a lot more. George L’Heureux: Why do you think there was that difference between what you were seeing and you see now that was really available to a lot of our customers? Cecilia Petit: I think a lot of it has to do with how much information I shared or didn't share. A lot of our customers, I see this, the ones who give us more information, ask more questions, they get more information back and they get better at leveraging the products. So it's kind of a case of the squeaky wheel gets the grease, right? And I remember the first time I asked the customer "So what are you trying to do with this?" And I wasn't planning on asking that question because if I had, I probably would've censored it because I don't think I would've answered it when I was a customer, but I was just trying to get my mind in the right space so that I could solve the right problem. So I thought they weren't going to answer, because like I said, I wouldn't have, but then the craziest thing happened: they actually answered. And I think because they got it, they got what I didn't get, that the more I understand, the more relevant the advice that I can give them. George L’Heureux: It's really interesting. So now having had that experience inside the company, do you look back and think, well, if I had shared more, when I was a customer, I would've had an even better experience than the one that I already had that I thought was really good. Cecilia Petit: We will never know. Right? But yeah. I mean, it, it stands to reason. So we, most likely we could have gotten more information, more guidance, more best practices. Yeah. George L’Heureux: It's interesting. Now that you're on the other side and you're asking these questions, do you find that there's a mix of customers, so who are eager and willing to share about their use cases or what their goals are and others who might have been more like what you expected yourself would've been, who are more reticent or slow to share that type of information. Cecilia Petit: Yeah. You know, it's funny, I hadn't really thought about that until you just asked this particular question, but maybe it's, some of it might be cultural, right? Some functions tend to be a little bit more open and engaging and some functions are paid to be careful, right? Finance people are paid to be careful. And I think about my own kind of corporate upbringing and R&D, we were taught not to share any of the fun, new stuff we're working on until the patent application’s filed. So it's just kind of ingrained in us not to share with people outside. So that's probably, that was one of my biases. And if I think about it, there's probably a pattern there that there might be functions are that are more likely to share, and functions that are less likely to share. Now I'll have to go back and take some data. George L’Heureux: Well, we'll talk about that. Maybe in a follow up, if I can convince you to come back on this show sometime. When you think about what customers could share with us, you kind of mentioned use cases. Is that, do you think that that's the most important thing that our customers could share with us, really, in order to maximize optimize their relationship with Dun & Bradstreet? Cecilia Petit: Definitely. I thought you might ask me what the biggest thing was, and that would be the easiest, quickest, most obvious answer for me, because even if all we're doing is orienting the customer around, say a data layout, right? The most basic thing we could do. If we know what they care about, then we can point to a group of data elements that might be particularly helpful, or an indicator that's relevant. And not that we'd ever do this, but sometimes data element names, aren't quite what they sound like. So if you didn't know it exists and what it's called, you wouldn't necessarily find it. George L’Heureux: Let's talk a little bit about maybe examples now that you're on the inside, that you've seen. We know the value of sharing use cases with the advisors on our team. Can you maybe look back at your career the last six years and think about a time when there's been a customer who hasn't done that? What's that experience been like it? How has it shaped the way that you've been able to help them or interact with them? Cecilia Petit: So obviously when they're not sharing, it's hard to really make progress, but I just had one, a couple weeks ago where they did share. And I thought that the way that that conversation shifted due to that sharing was interesting. And it just, it made it easy when it could have been, I could have given the wrong, not the wrong answer, but at least the wrong direction, the wrong advice. So this was a bank who was using our UCC data. So they came and said, "Hey, your UCC file is missing some data." So UCC is Uniform Commercial Code. And it's basically court records that show assets that are used as collateral for loans. So there's information about the borrower, the lender and the collateral itself. So it turns out that what this customers thought was missing was the secured party D-U-N-S. So that's the D-U-N-S number of the lender. Cecilia Petit: And most of our customers in that space use that data for underwriting models. So they're trying to understand if somebody's overextended or if there's already a lien on a particular asset. But, I also knew that some of them use it for marketing. So it's maybe a little bit counterintuitive, but once you hear it's kind of obvious, so they might be cross-selling an offer for refinancing a loan. So if they have a current customer that has a loan with another lender, they could offer to refinance that loan. There are also banks who go after their competitors. If the competitors are closing branches, or if they had some reputational damage lately, or if they just went through an M and A, so they target the specific banks. So that's why they need the secured party D-U-N-S number. And it turns out that in this scenario, that's what they were doing. Cecilia Petit: This was a competitive campaign. So they needed the secure party D-U-N-S so that they could group all the lenders together, so that they could tailor the messaging. So the problem with these missing D-U-N-S, or the reason that there were some missing D-U-N-S, is that when the risk, the primary use cases for risk, like it's traditional for this particular data set, we had to be very careful and we are very cautious with the kind of matches that we accept. But now that we know that it was marketing, there's no credit risk, almost no reputational risk and very little spend risk, even. So we could really safely accept lower confidence matches. So we reran the matches, got 50% more secured party D-U-N-S, which means that they now got 50% more leads. And we couldn't have done any of that if we didn't know their use case. George L’Heureux: You can really see the difference that it could make when you know the use case versus not having had it shared with you. So I guess I'll ask you, you kind of alluded to this, that it might be cultural, it might just kind of have been the business world in which you came up, but can you think back and are you able to articulate, what are the reasons that you, as a customer of Dun & Bradstreet, might not have shared your use cases all the time? Cecilia Petit: Yeah, I think the most conscious reason was kind of a practical one, and this is going to sound really blunt, but I was afraid that if the rep knew that we were making decisions on $40 million capital investments, she'd raise our prices. So at the time, I kind of only saw downside to divulging that information. George L’Heureux: I think that there are probably a lot of folks out there, data professionals, who can relate to that concern. I mean, I'll say in my time here at Dun & Bradstreet, that's not what I observed. I see our team and the teams that we work with, that they're really focused on trying to find the right solution that'll address the client needs, like what you were talking about a moment ago with finding the right way to use those UCC records. Not just trying to find ways to kind of boost the price up. Cecilia Petit: Right. Yeah. And I agree with you. I think in, in six years, I've never heard of sales teams just talk about raising prices. Just like you said, they try to find new ways to bring value to their clients. That's really how they think about it. So they think about new data assets that could help, maybe updated tools or services and support. So they're really looking to understand the real problems. And then they get all of us together to try to come up with solutions. George L’Heureux: So your concern was around price. What are some other reasons that you've seen customers, now that you're on the inside, shy away from sharing more information with us, giving us those use cases, giving us that additional background information? Cecilia Petit: So there's some sampling bias here, right? Generally we don't get to talk to the ones who are not asking questions but sometimes you can tell, right. You can hear it by the time they actually ask the question, they've been hitting their head against the wall and finally just kind of gave up and said, all right, I'm going to, I'm just going to see if D&B can help. Sometimes they flat out tell us. I had a customer tell me, so a few months back, that they were on their third attempt to build an MDM. So maybe they just didn't think to ask us during the first two. I think that's another scenario. They just don't think about it. But even on smaller things, if the data doesn't look right, I wonder how many times customers just feel bad asking. It looks like something's not right. You're not being mean by bringing it up. I mean, unless you're being mean about it, you're not being mean, but remember we can't fix it if we don't know that there's a problem. So don't shy away. George L’Heureux: I guess part of the problem might be, like you said, it's some selection bias. We, we don't know all the time when our customers aren't sharing everything that they could with us, for whatever reasons. Probably the flip side is true as well. Customers might not realize that they're missing out on potential value by not sharing that with us. Cecilia Petit: Yeah, exactly. I think that was, that was definitely part of what I was facing. I only saw downside. Nobody said, "Hey, if you do share, here's what we could bring to you." George L’Heureux: One of the other things that you and I have talked about is, we've worked in this role for a while, is making sure that the right people are in the room, that these data efforts are much more than just an IT effort or something that your DDAs are going to do. You need stakeholders in the room in order to understand and apply that framework of what the company's goals are. Have you seen a shift at all in how willing our customers are to have enough people in the room to really kind of help us understand what's going on, on their side? Cecilia Petit: I think we're getting better at making that case because we have some of these stories and, and we're building up that experience, but you know what, the other thing we're starting to see, especially in the MDM world, is that sometimes our primary contacts, the reason they're not sharing is not because they don't want to, it's because they might not have that insight. They might not have access to their internal clients. And so sometimes actually, what I've seen happen is that our account teams, because they're so well connected, they work with almost everybody in their organizations, right? They they'll work with the sales teams, marketing, finance, compliance, you name it right. There are so many use cases, we support that sometimes they can help bridge some of those connections or break down those silos. So that's, I think that's another way that we can be helpful. Actually had the first rep that I ever worked with at D&B, she said, we had this big meeting with a customer, it was on site. And when we walked out, she said, "Yes, I'm their best employee because I help connect everybody." I thought that was funny. George L’Heureux: I think it just goes to show that in this industry, as in life, there's a certain hesitancy to being willing to say, "Hey, I need some help here." But there's real value in sometimes being able to do that. Cecilia Petit: It takes some maturity to say, "Hey, I need help." And to be fair, we really can't expect our customers to know all 8,000, but I can't keep track anymore, data elements. It's just not realistic. And that's why we're here. George L’Heureux: So as we wrap up, what do you think the bottom line is here? People who are watching this or are listening to this, what's the message that you would like them to walk away from this conversation? Cecilia Petit: Hopefully they see that we want to be a partner. We want you to trust us with the challenges that you're facing, and know that we'll do our best to help. So don't be afraid to ask questions. We want to help, it's not just good business. We know that we're only successful if our clients are successful, but also we enjoy the challenge, I think. Right? I think you do. I definitely do. And I think there's also some satisfaction that comes from knowing that we're helping. George L’Heureux: Well Cecilia, I really appreciate you taking some time and being willing to go in the way back machine and revisit your time as a customer to share it with everyone on this episode today. Cecilia Petit: Well, thanks for having me, George. George L’Heureux: We'll see about that. Our guest expert today has been Cecilia Petit, a principal consultant for data strategy at Dun & Bradstreet and this has been Data Talks. I hope you've enjoyed today's episode and if you have, I encourage you to share it with a friend or a colleague. Let them know about the show. If you'd like more information about some of the things that we talked about on today's episode, visit www.dnb.com or talk to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us, until next time.
George L’Heureux: Hello, everyone. This is Data Talks, presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a principal consultant for data strategy here in the advisory services team at Dun & Bradstreet. In advisory services, our team is dedicated to helping our clients maximize the value of their relationship with Dun & Bradstreet through expert advice and consultation. On Data Talks, I get to chat every episode with one of the expert advisors at Dun & Bradstreet about a topic that can help the consumers of our data and services to get more value. Today's guest expert is Janine Moreira, a data strategy consultant at Dun & Bradstreet. Janine, tell me how long you've been with the company. Janine Moreira: I've been with Dun & Bradstreet for almost 16 years now. I've had various roles within the organization. I started in our fulfillment organization and then I worked in different areas, such as global data content, and most recently, our data advisory services. George L’Heureux: Can you tell me a little bit about your role as a data strategy consultant inside data advisory services? Janine Moreira: At D&B, our mission here is to really help our clients improve their business performance through data and insights. As a data strategy consultant, what we're really here to do is be subject matter experts for our customers around the different use cases of their data, whether it be from D&B identity resolution, master data initiatives, hierarchy assignment, or even various global data points. We're really here to help our clients make sure they understand the data, are maximizing the value of the data in the most effective and efficient ways possible. George L’Heureux: One of the things that you mentioned in that list of items that we consult with our customers about was identity resolution, what we call "matching" colloquially. One of the things that we talk about with matching and that you're frequently discussing with our customers is this idea of a multipass matching strategy. That's what we wanted to discuss today. Can you tell me a little bit about what is multipass matching? Janine Moreira: Just to take a step back, when I think about D&B matching or D&B identity resolution process, I really think of it like the foundation of a house. It's sometimes overlooked, but it's the most important thing, right? Everyone looks at the exterior of a house. They look at the shingles, they look at the windows and the front porch, and they're like, "Oh, how beautiful that house is." The foundation is never really realized. The foundation of the house is what keeps that house standing, and so when I think about D&B identity resolution, I think of it as the foundation of your data connected to our data. Really, that's the purpose of identity resolution. When we talk about multipassing, what we're really talking about is the ability to take different input permutations that clients may have of their data and take that through our identity resolution process to ideally return not just more matches, but the best match candidates, so more performance, higher-performing matching. George L’Heureux: We're really talking about trying to, and this is important to me because I'm in the process of building a house right now, so that whole idea of foundation is important. This is really bolstering the strength of that foundation of matching. When we talk about doing these different permutations, what are the extra data points or permutations that customers might have that we can help them through multipass matching with? Janine Moreira: When we think of matching, I think sometimes we're stuck in this common thought of, "Okay, we want name, we want address, we want city, state, and phone and country. Those are the critical elements that we need to match." But that's just one set of matching input permutations that a customer may have. When we talk about D&B matching, we talk about the fact that we have over 420 million records in our global data cloud, which is a massive breadth of data. But the other important note is that we have over billions of prospective data points to match against because when we talk about the D&B D-U-N-S Number and the value that it brings and the fingerprint of a business that it is is that we may have additional points of information on that record, so we may have not only business name, we'll have multiple trade styles' names available maybe. We could have the primary operating address, as well as the registered address. We could have former address or former phone information. We may have CEO or former CEO information. All of these are data points that we have when we take your input record and we take the data that you're providing to us to try and have the best outcome of match, so we match against all these additional data points. When we talk about multipassing, it's a very similar concept. You may also have multiple data points, maybe not realized in the existing data set that you're looking at today, but maybe you have a supplier file and you have a company name and you have the remittance address and phone number. But if you work with your legal or your finance, you may have the registered address, the primary operating address, you may have the registered or legal business name. Those could be additional data points that you provide to us that we can evaluate in our matching process. George L’Heureux: It's really about trying to take as much of the good data that a customer might have and compare it to what we have in our data cloud and our match reference file, and give really the greatest opportunity for one of those to match and give us a strong connection to a company in our data cloud. When I think about what I've seen in data in some of my previous roles throughout my career, I think of company name fields that have things like "The ABC Company formerly known as George's Donuts," right, and that's the kind of thing that you're talking about, right? Janine Moreira: Exactly right. We may see customers have two names in one name field, right, exactly what you're saying, "Janine Moreira DBA, Janine's Subway Shop." Ideally, those would be two separate names and we could attempt to match using both those names, right, and see, where are we getting the best match candidate returned? George L’Heureux: We do just that, we take all these permutations and we try matching with each of them. Then what happens internally? Can you step through that? Janine Moreira: Depending on your use case and your criteria, we would absolutely want to evaluate with you the best outcome. We have some standard matching logic for multipass specifically for the US. Globally, we do have some best practices, but ideally, what we like to do is talk with our clients and define their use case, define how the data's going to be used to determine really how we want to approach that multipass. It could be done a few different ways. To your point, we talked about different information. Clients may also have non-traditional data points that they want to match with us, such as URL or domain, email addresses. They may have things like registration numbers or tax IDs, so we can talk about a flow of, "Okay, when your traditional name and address information's set doesn't meet your performance threshold, how can we look to evaluate these alternative data points to see if we can reach an achieved match candidate for you that would meet your use case needs?" George L’Heureux: When you talk about performance thresholds, you're talking about things like the confidence code or MatchGrade string patterns and things like that. Janine Moreira: That's exactly right. We try not to put it in a box of confidence codes. We certainly have clients, many clients who use confidence codes as a point of a performance threshold, but there are so many other things, such as match grades and match data profile points that we can use to create those performance thresholds with clients. George L’Heureux: In the end, though, really, what we're talking about is the same goal that we have with what we might call "traditional matching, traditional identity resolution," and that's trying to get to the idea of one best match, right? Janine Moreira: That's exactly right: Who can we return to you as the best match candidate that most closely resembled who you were asking about? George L’Heureux: Hopefully, that is an improved response, it's an improved answer because now we've got these multiple data points on the client's side that we're able to compare to the billions of match reference points on our side, whereas we might've had a fairly strong match before, that additional information could potentially take us to an even stronger match, an even more confident match. Janine Moreira: That's exactly right. You may also see not only just increased performance on your matches, but also traditionally where you may have only been setting us one set of name and address and there's this percentage of records that don't meet your thresholds that you have today, now, when you try and attempt to rematch it, potentially on these other data points you have, you may watch this percentage of records that we're not being matched at the level that you needed and just on the cutting room floor now meeting the acceptance threshold that you have, therefore increasing your ability for automation, right, auto-accepted matches and the insight that you're getting back. George L’Heureux: I would imagine that a lot of times, customers aren't even aware of the information that they have that they're not providing to us. They might have additional phone numbers or additional business names that they're not even thinking about, and so therefore not even sending to us. What tends to be the typical process for customers to understand, "Oh, I've got more data than I thought that I could use for matching?" Janine Moreira: When we initiate any kind of a matching project or matching consultation with our clients, ideally, we certainly talk to them about what I call it "the bare minimum," right? We need name, we need address, city, state, phone, country, preferably. The more you give us, the better match outcome that you're going to have. But we also talk about these additional data points with them so that they can start to explore internally within their own organization if there are different pieces of information that they can obtain. Whether it's through their finance, where they may be able to get legal name and legal address of the business, or with their marketing organization, where they may have CEO and/or additional contact information and/or additional names. We try to explain to them, "We don't want to put you in this, This is all we can take, name and address." We certainly have standard layouts where we guide a customer, but we have to have those client conversations where we talk to them about, "If you have these additional data points, talk to us. Let's talk about where you're getting them from, how much fill you have, and let's evaluate how we can use these in our match performance." George L’Heureux: The interesting thing, I think, having been on the client side before, is when you have conversations like that, not only do they address the item at hand, the idea of, "Do we have additional things we can use for matching?" but oftentimes, they open up a whole new world of discussion for the client around other things that they could be doing with these data that they're rediscovering. Janine Moreira: Yeah, absolutely. There's a lot of conversations that come about through talking about what they have available. We'll bring up URLs or domains and how they may be able to use those matches, so maybe it's not a traditional name-and-address match they're returning back at a high confidence code or a MatchGrade string acceptance that they have, but maybe using that URL, they can get within the right family, and that can be used for specific use cases within their organization. There's really no data that has to be left on the floor. We can talk about all the different ways they can use the insight we're returning to them as well as what are the things that they can do to help improve that. Why wouldn't a customer take advantage of this? Why wouldn't they take advantage of a multipass matching strategy? Janine Moreira: Sometimes it's just the effort. It takes time sometimes internally within their organizations to talk about where they could get these different data points. Other times, processes are so ingrained that they have to do a lot of heavy lifting to achieve how do they productionalize getting these additional data points into the system. Those are all conversations that we'll have with clients. Really, ideally, whenever we talk about multipassing, we're typically testing it out first with clients so we can show them the value that it brings. That's one of the things that we'll do on the data advisory teams is really work with our customers to prove that concept out. George L’Heureux: I think we've all gone by the idea that the more potentially good data that you can give to Dun & Bradstreet for matching, the better your match results are going to be. I mean, it feels like it might be considered almost an investment or beneficial to go hunting around for this data to some extent or another, right? Janine Moreira: Absolutely. If you think about it, if you're getting an 80% match rate today, and by passing some additional data points, you increase that to 85%. Now, you have a 5% lift. You think about potentially, if it's a supplier style, how much spend was in that 5% lift I have, I mean, potentially, you could be increasing quite a bit of resolution on some of your highest spend suppliers or some of your potential biggest prospects that you have, so there's a lot of value in doing it. George L’Heureux: To your point, I mean, first, I love talking about it in terms of customers, prospects, or leads, right? I mean, instead of just matches, those are things that really have a meaningful impact on your top line or your bottom line as a company. By thinking about them in terms of that's an opportunity for revenue, that's an opportunity for avoiding risk rather than just matches, I think it changes the discussion a bit. Janine Moreira: It does. Every time we talk to our clients, "matching" is certainly a familiar term to them, but you're exactly right. We want to make sure we're understanding, "Okay, how many clients are we resolving customers that you do business with or want to do business with? How many prospects are we identifying for you?" Again, all of that is the foundation, which is what D&B identity resolution is built for. George L’Heureux: Janine, as we start to wrap up here, let me ask you this: Someone's watching this or listening to this conversation about multi-task matching, are there a couple of key points that you'd say you would want them to walk away from this conversation with? Janine Moreira: I would definitely want them to walk away with talk to your D&B team. If you're evaluating what your portfolio looks like today, talk to your D&B team about things we can do to help you. Have that conversation. Think about the data points that you have today where you may say, "Well, I just have a remittance address." Are there other areas in the organization where you may be able complementary fill that record with more, whether it's the legal address or the legal name? Are there URLs that you have available? Then talk to your D&B team about what D&B may able be able to do with that insight and help you improve your performance and your ability to connect more customers to the D&B data or more prospects to D&B data. George L’Heureux: Well, Janine, I really appreciate you sitting down and taking the opportunity to chat with me today about multipass matching and the benefits it can have and how our clients could really start to take advantage of that to improve their match rates and potentially their bottom line. Janine Moreira: Absolutely. Thank you very much, George. Have a good day. George L’Heureux: Our guest today has been Janine Moreira, a data strategy consultant at Dun & Bradstreet, and this has been Data Talks. I hope you've enjoyed today's episode. If you have, I encourage you to share it with a friend or a colleague. Let them know about the show. For more information about multipass matching or other things that we've discussed on today's episode, I encourage you to visit www.dnb.com or to talk to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L’Heureux: Hello everyone. This is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a Principal Consultant for Data Strategy and the Advisory Services team here at Dun & Bradstreet. In Advisory Services our team is dedicated to helping our clients maximize the value of their relationship with Dun & Bradstreet through expert advice and consultation. On Data Talks I get to chat each episode with one of the expert advisors at Dun & Bradstreet about a topic that could help consumers of our data and services to get more value. Today's guest expert is Susan Wellenkamp. Susan is a Data Strategy Consultant at Dun & Bradstreet. Susan, tell me, how long have you been with the company? Susan Wellenkamp: Oh goodness. Can we say just over 30 years? George L’Heureux: We can say however you'd like. Susan Wellenkamp: Yeah, let's go with that. It's been a while, but I've been in many different jobs, but all with Dun & Bradstreet. George L’Heureux: Can you tell me a little bit about the work that you do in your role as a data strategy consultant right now? Susan Wellenkamp: Absolutely. I work closely with Fortune 1000 companies in bringing together their information with Dun & Bradstreet information to drive business efficiencies and really using information to drive knowledge and actions in the marketplace. George L’Heureux: That's great. Susan, one offering that we've been talking about a lot inside of our team recently and with our clients as well is what we call ELI. But what is ELI? Susan Wellenkamp: Well, ELI sands for Extended Linkage Insight, and it is one of the new solutions that Dun & Bradstreet has brought to the marketplace. I will add that it is something that our customers have been asking for for years, and years, and years. Dun & Bradstreet is very well known for its legal linkage. Legal linkage is really defined as where one company owns greater than 50% of another company. George L’Heureux: What does Extended Linkage Insights bring beyond the legal linkage? Susan Wellenkamp: Exactly. What extended linkage does is, it brings together entities that may not be legally linked, but have similarities. It could be alternative relationships like franchisees, franchisors, dealerships, agencies, health care. It could be a number of different relationship types, and then on top of that, extended linkage also brings together through modeling entities that may have affiliations with the parent company, with the bigger structure, the bigger organization. George L’Heureux: Can you give me an example of what that modeled linkage might look like? Susan Wellenkamp: Absolutely. What we do is, we use evidence scores. One of the things that ELI does is it creates a brand name, and it will look at all of the legally linked entities and create a brand name for those, and then go out into the universe of businesses and identify other businesses that may look similar. They may have similar names. They may have similar industries. They may be in the same location. They may have the same domain. There are actually 11 different evidence scores in ELI that basically bring these groupings together that we can then provide to our customers to understand their expanded relationships. George L’Heureux: I would imagine that the strength of those scores and what they look like in combination with each other, we can use those to help explain to clients just how confident we are in that extension of the family tree. Susan Wellenkamp: That's exactly right. There's something that we call a source code that basically takes those 11 evidence scores, and with the values, the grades that are in those evidence scores combined together will predict the likelihood, the higher confidence that yes in fact this entity should be considered a part of the broader entity. George L’Heureux: So you and I in talking with clients and just really playing with it ourselves inside of Dun & Bradstreet can see just how powerful this extension can be. Is there a reason that we keep it separate from the standard legal linkage? Susan Wellenkamp: Well, legal linkage is absolutely crucial to customers. If you're looking at a financial use case as a for instance, they need to know who has financial obligation for a particular deal. If the company is having difficulties paying, who is the organization that owns this other company, so legal linkage is an absolute must. ELI associations are looser than the legal linkage and would include things like, and I had mentioned this before, affiliations through franchisees, through agencies, through dealerships, where you've got a relationship with another business but there's not a legal ownership. We with ELI will tell you that. George L’Heureux: So ELI really, because it includes the legal linkage plus all of this additional stuff, it's more of a supplement to our standard legal linkage. It's not a replacement. Susan Wellenkamp: It is not. It is absolutely not a replacement. It is used to augment the legal linkage and do it with eyes wide open. So if you're using a financial use case, you really would use ELI data in a different way than say if you were a marketing use case like a go-to-market use case where you're defining territories, or who owns what account, and how are we going to divvy the account up. Here you might be looser in the way you're defining those relationships where you want Joe's territory to include all of the pieces of that organization whether it's legally linked or not. George L’Heureux: When you would talk about tighter versus looser, that's not a binary decision though. We've got a dial essentially that clients can use to say just how loosely they want to incorporate extended linkage information. Susan Wellenkamp: That's absolutely right. There's a source code that ranges from a three at the lowest to a six at the highest, and again, those evidence scores feed into that. What you're able to do is look specifically at the source code that's associated to a particular record and decide whether you want to include that or not. I've got a customer who uses the extended linkage, and as a for instance, they will pull for their use cases everything that is a source code of a five or higher, and really leave the threes and the fours to manual stewardship where if they've got a particular account that they're reviewing they would have stewards looking at those lower source codes. George L’Heureux: So as with most things, it's a combination, a little bit of science and a little bit of art where you can auto accept some things, and say we have to leave the decisions to humans on some of these other things. Susan Wellenkamp: That's right. That's right. George L’Heureux: So what are some of the other use cases we've seen? We've talked about financial a little bit. We talked about marketing, go-to-market type things. Another area where I think we've seen some success with it is in things like vendor management on that supply side. Susan Wellenkamp: Exactly. That's just what I was going to say. Vendor management is a very strong use case. I had a customer who was using extended linkage to pull together all of their vendors, and part of the challenge with legal linkage was, as a for instance let's use an example, they did a lot of business travel before COVID hit, and one of their big vendors was Hilton Hotels. As you may well know, Hilton Hotels is made up of corporate-owned locations, but many of them are franchisees. When this customer was looking to understand all of the spend that they do with Hilton, they don't care whether it's legally owned or if it's a franchise. When they're going in and they're negotiating with Hilton for the discounted rate, they want to show all of the spend that they're bringing to that organization, and so ELI just was perfect for the vendor application. George L’Heureux: That's a great example, and I think between the several examples we've talked about, it really illustrates some of the power of ELI to our clients. So I guess if there's someone out there who's not using Extended Linkage Insights and thinks this could be something that could really help them, what do they need to do in order to be able to start using it? What's the interface like for Extended Linkage Insights? Susan Wellenkamp: We provide extended linkage through flat files that you could get on a monthly, on a quarterly basis, or you could get it through APIs. Basically what you want to think about is, what is the universe of businesses? Do you want everything that D&B knows about the ELI universe? Or is it certain geographies? Is it a list of your top 2,000 customers, or your top 2,000 vendors? How do you want to define it? There's a lot of flexibility when it comes to defining the specific universe that you're going to take. George L’Heureux: Are you able to quantify at all, even in general terms, what some of the benefits are that our clients have seen as a result of beginning to use ELI where they hadn't before? Susan Wellenkamp: Absolutely. We did a research study as we were coming to market with the ELI data, and we've estimated ... What you want to understand is, customers are doing this. Businesses are using D&B's legal linkage, but they're augmenting themselves records that they think should be part of the relationship. There's a lot of cost associated to doing that manual work. And the study that we did showed that about 70% of that cost could be avoided, which is huge. George L’Heureux: It is. Susan Wellenkamp: Now that's not to say that there's not still some work that needs to be done. ELI is a great starting point, but there still might be more that is out there that you want to bring in. Basically, if the evidence scores are not high enough, then we might not bring certain entities into the relationship. You may find that. But one of the really cool things about ELI is there's a feedback evidence score where we will take that feedback in and build it into ELI so it's constantly learning. George L’Heureux: That is really neat, and to know that it's not just a finish it, we think we're done, but it's an evolving, it's a growing, it's a learning tool for our customers to be able to use. Susan, as we wrap up, what's the one thing that you hope people who are watching this or listening to this might walk away from this conversation with? Susan Wellenkamp: I think it's really around the efficiencies that leveraging the ELI content can bring to bear. It is the cost avoidance. You're probably doing some level of this work today, and basically D&B can bring you, on a platter, a lot of that work so that instead of you having to create it yourself, we're bringing it to you and you're just dealing with potentially some exceptions. George L’Heureux: Well Susan, I really appreciate you taking time today to sit with me and chat about this topic. I hope that it's been really useful to a lot of the people watching or listening. Susan Wellenkamp: That's great. Thank you so much George. George L’Heureux: Our guest expert today has been Susan Wellenkamp, the Data Strategy Consultant at Dun & Bradstreet. This has been Data Talks. I hope you've enjoyed today's episode, and if you have, I encourage you to share it with a friend or a colleague. For more information about what we've discussed on today's episode, please visit www.dnb.com, or talk to your company's Dun & Bradstreet representative today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L’Heureux: Hello everyone. This is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a Principal Consultant for Data Strategy and the Advisory Services team here at Dun & Bradstreet. And in advisory services, our team is dedicated to helping our clients maximize the value of their relationship with Dun & Bradstreet through expert advice and consultation. And on Data Talks, I chat each episode with one of the expert advisors at Dun & Bradstreet about a topic that can help the consumers of our data and services to get more value. Today's guest expert is Peter Voutsinas. Peter is a Data Advisor within Dun & Bradstreet. And Peter, how long have you been with the company? Peter Voutsinas: I've actually been with Dun & Bradstreet for most of my business career, coming up on 30 years. Within those 30 years, I've spent about half that time in our data and analytics roles in our data analytics side of the business, as well as the other half being spent in business development and sales leadership. So in addition to gaining the comprehensive knowledge that I have around our data and analytics, I've also been able to see the impact of how customers benefit from those services and solutions that we offer. Additionally, I spent five years outside of Dun & Bradstreet in roles where in some cases I had access to Dun & Bradstreet services, in some roles I didn't. So I gained a keen appreciation of how those services can either help you or inhibit you from meeting your own strategic goals in those roles. George L’Heureux: So can you tell me a little bit about what you're doing in your current role as a data advisor? Peter Voutsinas: Absolutely. In my current role, I support a portfolio of strategic customers, primarily in the RTM space or retail, manufacturing, transportation. And in that role, I consult and advise customers on how to use our data, education around our data, making recommendations on how they can apply those solutions and services to things such as MDM and also working on how those apply to the different enterprise areas like credit and risk, sales and marketing, and supply and comply. George L’Heureux: So one of the things that we had wanted to talk about and that we have the opportunity to do that today is hierarchies. Now, this is something that we talk about with our clients all the time, and they're also known as corporate family trees. They're a real big part of a lot of master data efforts, but why are hierarchies so important? Peter Voutsinas: I think the first thing to think about is the way you use them is really informed and inseparable from the use case. So when you start talking about hierarchies, there seems to be a tendency when I work with my customers to simplify them. In fact, customers who are using data integrators usually get the advice to over-simplify and try and create a very one size fits all use case. We recommend that for a host of reasons. George L’Heureux: So this one size fits all hierarchy strategy, can you tell me what some of the benefits of it might be that gets it recommended so much and what are some of the cons that really maybe cause it not to be as useful as people might think? Peter Voutsinas: You know, from the benefits, and I certainly understand where the integrators are coming from, the idea is to try and get the data into something, for example, like an MDM platform. So get the data ingested and create your baseline platform. However, if you don't take the next step, which is to really understand the complexity of hierarchies, you may be missing or not getting the full picture of whether you're putting customers in your database or you're doing prospecting. So you may actually, that simplification may actually result in you missing the insight that could be valuable for the use case you're trying to, you know, the outcome you're trying to get from a use case. George L’Heureux: So in what you've just said, it sounds like there are kind of different insights that different groups inside our clients, their enterprises might be looking for from the same set of hierarchy data. Can you give me an example of how these groups would view or use hierarchy data differently? Peter Voutsinas: Sure. And I'll go back and you may hear me say the word use case quite often. It's very critical. So for example, if we were to look at the different enterprises, credit and risk may have a view that they needed their customers for their use case. Sales and marketing may have a completely different need and need a different view for their use cases. Same with a supply and complier vendor master use cases. And then of course you may have a business planning or just an overall overarching MDM strategy, which may also have a completely different need for views. To try and create a one size fits all that would accommodate all those different views is a fairly mighty task and one that's not easily achieved. George L’Heureux: So with that in mind, we have lots of clients, lots of different use cases and business goals that they're trying to achieve. How do you, when you're doing this consulting work, usually approach trying to figure out what the right type of strategy is to recommend while still trying to keep it as simple as you can, because obviously that's something that clients still care about as well. Peter Voutsinas: Sure. The first thing is ask questions. Again, what is your use case? What is your desired outcome? Why is it that you have a need to identify and understand hierarchies. To go back to what we talked about with the different enterprise group, you know when you ask those questions, the folks in credit and risk may say, "Our main goal is to be able to understand the full impact and risk within a family tree, whether it's rolled up to see the total exposure and potential risk, or whether it's right down to an individual entity." So being able to see that view. Peter Voutsinas: However, you talk to sales and marketing and their use case is entirely different. They may want to assess white space. They may want to use it to segment, to do lead generation, they may want to use it to do sales territory rationalization. Again, totally different use case. And you can go on suppliers have the same to be able to recognize the risks in their supply chain or certifier vendors or compliance. So again, may need a totally different view. Some may meet very discriminant views in the hierarchy and several players, and some may just need a very simplified rolled up view, but typically they're all very different, very unique. And in some cases get to the point where customization may be the solution for identifying and creating hierarchies. George L’Heureux: I find your answer really interesting because I think that it reveals that with a lot of these tough questions in data management, more generally, the answer is "it depends." And let's look at your use cases. I mean, working backward like that, it really is important when you're dealing with something that's complex. Peter Voutsinas: Absolutely. And you know, again, we talked before, I support the retail manufacturing industry, what may work for those views and their needs for hierarchy may not work if, for example, if I was supporting financial services or tech or other industries. So while there's some broad categories, I think we're all familiar with credit and risk, supply, compliant vendor and sales and marketing. You know, there's some very high level goals that all these enterprises aspire to that can vary very differently when you get down to the use cases and down the specific industry needs and the customer base that they're working with or prospect base. George L’Heureux: So you kind of touched on it a minute ago. I maybe want to dive in a little bit more around this natural friction that exists between trying to keep things simple, and at the same time allow yourself to roll up to a certain degree and narrow in on a particular entity or set of entities. Can you give me some examples around how different industries that have different verticals are approaching these types of headaches that they need to resolve? Peter Voutsinas: Sure. Again, I think when we talk about, again, I'll say the word use case, when we talk about what those specific entities do, the outcomes are from that they're trying to do from the hierarchies is once you understand that goal, what does my hierarchy look like? What do I need to find out? What am I seeing? What am I not seeing? Again, going back to that example of the credit risk, the friction you speak of balancing that roll-up view for an entire family versus that view of the single entity, where maybe you have a lot or a little exposure, but it's still contributes to that overall risk, being able to identify that. And what's your ability? Are you doing that yourself? Are you deriving those hierarchies yourself? Are you getting that information third party? And how do you know you're getting the correct insight and you're getting that correct view for that outcome. George L’Heureux: And you've kind of talked about how having a variety or some different hierarchy views can help that and maybe improve upon the idea of a one size fits all strategy. Can you give me some insight into what do these different hierarchy views look like? Peter Voutsinas: Absolutely. So you can look at views. So conventional when most people think of hierarchy, they think of corporate hierarchy, which is basically driven from a legal standpoint from stock majority ownership. So often when you talk to customers about hierarchy, that's what they're familiar with and that's what they know. But that's not the only hierarchy, especially as we've seen the business landscape change right now. Things like the gig economy and e-commerce, and just the overall and globalization have really contributed to a shift in how hierarchies and enterprise view, and even how an enterprise itself operates. As we talked about the different groups and the different use cases, they have a very unique and not necessarily the same in overlap. So, the ability to tailor that to what your need is, is why it's so important to understand how you're deriving these hierarchies. Are you doing it yourself. In cases where we see customers try it themselves or customize, we tend to run into a lot of frustration and what we call hierarchy headaches. George L’Heureux: We've talked about a couple of those before, and I imagine whether or not you're building it yourself, whether or not you're focusing on just the corporate hierarchy, you are something different than extends into things like franchises or alternative ways of being associated. That's going to impact how well you're able to actually address those use cases that we spoke about. Not to say use cases again. Peter Voutsinas: Absolutely. Well, let's take an example of that. So if we look at a company, well-known company, like the Toyota Corporation, you've got Toyota Motor Corporation, which is a corporate entity and all of the various divisions and arms that would roll up in that corporate family. However, if your use case also entails that you need to see franchises, typically franchises is selling a brand or a name like Toyota aren't necessarily associated with the Toyota corporate family. They are typically owned by the franchisee. So you may want to see that exposure, but if you are only looking at corporate linkage, you would not derive that if you didn't also look at the alternative linkage relationship. We talk about alternative linkage. Some other examples are the government, universities, and businesses and industries that are not generally dictated by legal or stock ownership. George L’Heureux: Yeah, you had the example of Toyota. I think of other industries, like a lot of hotels and ShoreTels, a lot of quick service restaurants. They follow that same franchise model. Peter Voutsinas: Sure. And in some of those companies, what they're actually doing is they are not owning any more of those locations. They're franchising everything out. So the actual locations and entity and stores are actually not owned by those companies. They just supply the goods and services and the branding. So if were trying to get that higher exposure, the challenge is how do I make sure I'm including all those views? How do I know if the McDonald's is part of the McDonald's corporate family? Or how do I know if it's just a standalone entity owned by somebody in my hometown. George L’Heureux: So you pose that great rhetorical question. And let me pose one back to you that isn't rhetorical. How does Dun & Bradstreet help? How does someone like you help a client who's looking at that situation and has to figure out how to resolve it. Peter Voutsinas: Well, again, once we've learned and understand the use case and what the customer's outcome is, we can then look at the data that they have. We'll look at what they're seeing, look at what's working, take that information, add some of Dun & Bradstreet's different hierarchy views, and see if we can derive a better picture or meet the outcome that they're working for. So, in that last example, if they do want to see the extended family of Toyota franchises, our alternative linkage combined with the corporate language would help a customer get that view. And then you can use those for applications like understanding white space, understanding the nature of the relationship, understanding buying opportunities. And those I've mentioned are sales and marketing applications. Or if we were to go back to credit and risk, understanding your risk and exposure and the associations between a dealer versus your relationship with the corporate entity. George L’Heureux: So this is all information that is for the most part out there. It's not that it can't be found somewhere. It's mostly public. Why wouldn't companies just go and do this themselves? Why turn to a place like Dun & Bradstreet to get this information? Peter Voutsinas: The challenge is based on how you derive that. So if you were just trying to identify that via a name or name and address match, you can run into a whole lot of issues where the company may not be using a brand name, or you may not be able to derive that association or you may get multiple returns and not be able to understand where that entity sits in the corporate family. Dun & Bradstreet through our D-U-N-S number are able to link all those together and show you what those relationships are, whether it's in a corporate structure or we can show you parent, headquarter, child relationships, or whether we can link that alternative brand or franchise relationship for you. So we're able to do that via the D-U-N-S Number and our process of how we show customers hierarchy through how we derive hierarchy in our commercial database. George L’Heureux: Peter, great information. As we wrap up, what's the one thing you might want people who are watching this or listening to this, to walk away from, from our conversation today. Peter Voutsinas: Well, you might have heard this before, but your hierarchy of needs have to be driven by use case. So, what is the output? What is the outcome you're looking for? What is the use case specific to your enterprise or your initiative and what is the outcome you're looking for? A good barometer of that is if one of your data end users feels like they're missing something, they probably are. So in that case, we can help you assess those and provide insight around hierarchy and help you meet those outcomes. George L’Heureux: Well, thanks, Peter. I really appreciate you sitting down and sharing some of your expertise and your knowledge around this topic today. Peter Voutsinas: My pleasure, George, thank you. George L’Heureux: Our guest expert today has been Pete Voutsinas, a Data Advisor at Dun & Bradstreet. And this has been Data Talks. We hope you've enjoyed today's discussion. And if you have, I encourage you to please share it with a friend or a colleague. And if you'd like more information about things that we've discussed on today's episode, please visit www.dnb.com or reach out to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L’Heureux: Hello everyone. This is Data Talks presented by Dun & Bradstreet, and I'm your host George L'Heureux. I'm a principal consultant for data strategy in the advisory services team here at Dun & Bradstreet. In advisory services, our team is dedicated to helping our clients to maximize the value of their relationship with Dun & Bradstreet through expert advice and consultation. On Data Talks, I chat each episode with one of the expert advisors at Dun & Bradstreet about a topic that can help consumers of our data and services to get more value. Today's guest expert is Bill Sprague. Bill is a data strategy consultant at Dun & Bradstreet. Bill, how long have you been with the company? Bill Sprague: I just celebrated my 34 year anniversary. George L’Heureux: My goodness, congratulations. Tell me a little bit about what you do in your role. Bill Sprague: So, my role as a data strategy consultant is working with the clients to help them consume our data in the best demonstrated practice, helping them identify what they can use in terms of ingesting our data from flat files to API calls and making them best suited for whatever their business or use case is. George L’Heureux: Can you tell me a little bit about how you ended up in a role like this? Was it what you've always done at Dun & Bradstreet or you kind of stepped through a number of different positions to get to this point? Bill Sprague: I stepped through a large number of positions starting in operations back when I was right out of college working. I actually used to write D&B reports. I compiled them and made them available for people, and then went into an internal consulting job. I actually worked as a sales leader for seven years at D&B, and then at the end of that stint I took over for a longstanding D&B associate that worked on data quality in a similar role. From there, we morphed that position into what is now known as the DAS team with us multiple consultants that we have working to help clients. George L’Heureux: So, you might have just a little bit of experience at some of this stuff. Bill Sprague: Yeah, there's a little bit of knowledge. George L’Heureux: So, what we had wanted to talk today about was D-U-N-S recertification. Before we go any deeper, can you just explain briefly what that is? Bill Sprague: Sure. So, D-U-N-S recertification is really for customers who are taking on our D-U-N-S Numbers. So they may get them through a batch feed or a file that they have, or they are conducting a number of match transactions through our API services, and they're housing those D-U-N-S Numbers in one of their internal databases, right? Whether it's a data warehouse or some other place where they store that data, the D-U-N-S recertification is there to help customers try to stay in sync with our data. So we occasionally have D-U-N-S Numbers that are retired or change, so we want to be able to perform some kind of recertification that allows them to keep their D-U-N-S Numbers in sync with the Dun & Bradstreet Data Cloud so that they can have access to the right information. George L’Heureux: So when you say retired or changed, that's not the same thing as an entity going out of business, right? That's different. Bill Sprague: That's correct, it is different. So when something goes out of business, what we're saying when we indicate a company has gone to out of business status is that we no longer can confirm operations at that site or that D-U-N-S Number. So we don't actually retire that D-U-N-S Number in a case like that, that would stay with that business that went out of business or that location that went out of business. Then for a customer who needs some kind of archive data, they would have access to that D-U-N-S Number of the out of business record. The recertification or the retired D-U-N-S Number is when we actually make a change to a site to give it a new D-U-N-S Number, either because of that company has had a duplicate identified or through some kind of organizational structure had a change where we needed, based on our internal policies, make a change to that D-U-N-S Number. So one D-U-N-S Number is retired and another D-U-N-S Number survives, and the recertification will point them to their surviving D-U-N-S Number. George L’Heureux: Okay, so there are some cases where Dun & Bradstreet is going to assign a new D-U-N-S Number. You mentioned one of them being duplicates, and then you talked about some internal policies or procedures that would cause us to do that. I bet a lot of customers think that it doesn't happen all that often. Can you go into a little bit more detail on what would cause us to do that? Bill Sprague: Sure. We don't usually change D-U-N-S Number, it's not something that's a high rate of occurrence, but it's not uncommon either, right? There are reasons this can happen. I talked briefly about one where even through all our efforts to try to minimize the number of duplicates in our system, we occasionally find them. We have a process in place that sweeps our system looking for duplicates and one is identified. We'll actually look at the two records that we have and we'll make sure that we aggregate the data between the two, and one will be a surviving and one will be a D-U-N-S Number that gets retired. Another instance is they have some kind of organizational change, where a location may have been a branch at one time and they've made a change in that organization to have it become a headquarters location. In that instance, it's D&B practice to retire the one D-U-N-S Number that was a branch and keep the D-U-N-S Number that is now the headquarters, create a new one for the headquarters. So we'd want to, if someone came in looking for that company they had prior, we'd want to be able to point them to the new headquarters location. Through recertification, we're able to do that for them. George L’Heureux: So, would something like a merger or an acquisition have a similar effect then depending on how that was structured? Bill Sprague: It would. There are instances where merger acquisition is one of the activities as well. Some merger acquisitions are a buying company, if you will. We'll take a number of locations to out of business, so it's not that instance, that's an instance of taking something to out of business. But in a number of occasions, we see where they take over a business and change the structure, right? They'll change something that was a branch and make it a headquarters now due to the new customers' organizational structure. So, there are times when it will and times when it won't be a factor in that. George L’Heureux: So, having to update a D-U-N-S Number as a result of this type of recertification, where we hand in or a customer hands in one D-U-N-S Number and we say, "Hey, you're going to want to change it to this." That really isn't a result of Dun & Bradstreet screwed up, it's because there's more forces at work, right? Bill Sprague: That's correct. It's really around data dynamics, right? So as you're aware, we have a Data Cloud of over 400 million records and keeping up with those data dynamics is quite a chore. What we designed the recertification to do is find those type of changes, right? Whether it's due to merger acquisition or a company making some kind of organizational structures, or in the instance, like I said, where we happen to find a duplicate record, we want to be able to be keeping up with those data dynamics and provide an avenue or a process that will help the customer stay in sync with our data. George L’Heureux: So we have essentially an audit trail, you might call it, of things like this that we keep track of that we can pass along to the customer when they ask us about it to help them keep that synchronization. Bill Sprague: That's exactly right. We've created a process which includes a D-U-N-S audit trail, which we'll keep in the database and we'll keep on our end a previous D-U-N-S Number or the one that was retired, and we'll point it, if you will, a connection to the new D-U-N-S Number or the surviving D-U-N-S Number. That D-U-N-S audit trail's allowed us to do that. Not just at a point in time, but be able to historically go back. So if someone comes to us with a six month or a year old D-U-N-S Number that had been retired, we can still point them to the new D-U-N-S Number, allow them to access that D-U-N-S Number in our data cloud and get the kind of information they need on that business. George L’Heureux: Yeah. You mentioned earlier duplications, kind of structural changes, things like that that would cause these to happen. You mentioned that the rate is pretty low. Do you have a sense of where that is at, how often does this happen? Bill Sprague: Yeah, from a duplication standpoint, it's in the very low range, 1% range. The last information I had, it was about that range, so the amount that are affected by duplication is fairly minimal. In fact, when someone recertifies a file, we don't expect a large percent of the D-U-N-S Numbers that they have to get a new D-U-N-S Number, right? It would be the exception versus the rule when we point you to a new D-U-N-S Number. Most of the D-U-N-S Numbers would remain the same, and each time you came in to look for it, we'd provide you back the same D-U-N-S Number. But when it does happen where there's a change, we want to be able to have your data stay up in sync with our data. So, the recertification process can help. George L’Heureux: So this recertification, we've been talking about it throughout. Is it automated? How does it work? Bill Sprague: That's a good question. There's a couple of different ways that recertification can happen. Up to this date, the technology has been ... so most people like to do it in a batch. So if they have 500,000 customers and vendors, they want to check them all at one time, so either annually or semi-annually someone might come to us and ask us to recertify their entire list of D-U-N-S Numbers that they have. With modern technology that's come around in APIs, we do have the ability to set monitoring in place. So if you came in with that old D-U-N-S Number, the monitoring service or the API would actually direct you automatically, provide you with a small note or description of what happened with a code saying, "Due to our recertification, we've assigned a new D-U-N-S Number. Here's your original D-U-N-S Number, here's the code of what happened and here's the new D-U-N-S Number or surviving D-U-N-S Number for you." So you can do it in either batch or in, if you have the technology of the API installed, there's a way to do it through the API as well. George L’Heureux: So if you don't do this, let's talk about what happens if you're not recertifying your D-U-N-S on some sort of periodic basis. What are the risks? What can occur with your data? What could be the downstream business impacts? Bill Sprague: Yeah, that's always the question in business, right, is what's the risk of doing nothing? In this case, the risk of doing nothing is becoming out of sync, right? It's having a population of D-U-N-S Numbers that you have stored in one of your systems somewhere that no longer exists, that's not a valid D-U-N-S Number in the D&B Data Cloud anymore. So not having some kind of recertification or process in place to get the new D-U-N-S Number, you're going to be out of sync and the information you had on that D-U-N-S Number, whether it be firmographic or demographic information, no longer exists. In fact, the D-U-N-S Number has been retired at D&B and there's no data associated to it at all. George L’Heureux: Now, I've been in the data world a long time and I know that companies use the D-U-N-S Number as a key. Anytime that you're talking about replacing a key, there's going to be challenges there. What are some of the biggest challenges from kind of an adoption standpoint of recertification to making this work and making it work properly? Bill Sprague: Yeah, the adoption, how you want to work with this is understanding what downstream systems are impacted, right? Where do your systems house a D-U-N-S Number, or whether it's as a unique identifier or just a caring attribute or value that you have associated to that business. So, one of the biggest challenges is understanding the downstream impact of a D-U-N-S Number changing. We want to work with a company to make sure that in every system that's impacted or is tied to a D-U-N-S Number, when a change is made you're able to take that and carry that through the entire lifecycle so that it goes down to the end user. Each person who is using that D-U-N-S Number as an attribute has that change and can make that update. That would be the challenge that most people find. George L’Heureux: So, if we've got a client out there who's watching or listening to this and hearing what you're having to say and they realize they're not doing D-U-N-S recertification today, what steps should they be taking? What would you suggest to them? Bill Sprague: : Yeah, I think they first want to determine are they doing this in some way? Do they have some kind of regression or do they have some kind of recertification for the D-U-N-S Number itself and all the attributes that are tied to it? If they don't, contact the D&B representative, either the sales team, or if they have a DAS consultant like myself, contact us and we can give you the options. If your maturity, your technical maturity is that you're using the APIs, then it can be done that way, or if we want to set up some kind of a batch processing. Then you'd have to determine too what kind of frequency you'd want to do that on, how much of the data dynamics do you want to remove, either quarterly or semi-annually or annually. George L’Heureux: Bill as we wrap up, what's one thing you might want people to walk away from this conversation with? We've gone over a lot in terms of recertification, but what's the one takeaway you want people to have? Bill Sprague: Yeah, I think it's to realize that data's dynamic, right? You have to understand that there's data dynamics that happen every day, and D&B managing the data cloud has kind of set up the processes and files in place to help you keep up with that dynamics and be able to make the changes to the D-U-N-S Number, or understand when something has changed so that you can have the most valid and up-to-date information. When you go to access to Data Cloud, you're getting the right information on the right business so it doesn't impact your downstream. George L’Heureux: Bill, I really appreciate you taking some time to sit down and chat about this to share your expertise on this topic. Bill Sprague: It's been my pleasure, George. Thank you. George L’Heureux: All right, our guest expert today has been Bill Sprague, a data strategy consultant at Dun & Bradstreet. This has been Data Talks. We hope that you've enjoyed today's discussion, and if you have, we encourage you to please let a friend or a colleague know about it. If you'd like more information about what we've discussed on today's episode, I urge you to visit www.dnb.com or to talk to your company's Dun & Bradstreet specialist today. I'm George L'Heureux, thanks for joining us. Until next time.
George L’Heureux: Hello everyone. This is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a principal consultant for Data Strategy in the advisory services team here at Dun & Bradstreet. In advisory services, our team is dedicated to helping our clients maximize the value of their relationship with Dun & Bradstreet through expert advice and consultation. On Data Talks, I chat each episode with one of the expert advisors at D&B about a topic that can help consumers of our data and services to get more value. Today's guest expert is Holly Reed. Holly is a data strategy consultant at Dun & Bradstreet. Holly, how long have you been with the company? Holly Reed: So well, first George, thanks for the invite. So I've been with Dun & Bradstreet for 31 years in numerous roles, including both as an individual contributor, as well as having leader roles within content, operations and then sales support. George L’Heureux: So, can you tell me a little bit about what you see as your role in your current position? Holly Reed: Sure. So like you said, I'm a data strategy consultant. I really consider myself a subject matter expert in our match technology, as well as the Dun & Bradstreet Data Cloud. I have a deep understanding of the D&B match metadata, and that's really the how and why a match was made. I also am a consultant, so I work both new and existing customers to optimize their match results and to provide recommendations for match improvements. I also have a portfolio that I help support. I have 16 customers within the financial institution segment or space. So, I consider myself a banking expert. George L’Heureux: That's great. I know that that segment of our customer base is an area where they take advantage of our topic today quite a bit, and that is we're able to help our customers do what we call C2B matching. So, let's start with first things first, break it down for me. What is C2B matching? Holly Reed: Okay, I'll start at the basics. George L’Heureux: All right. Holly Reed: So, C2B matching, all it is it's consumer to business matching. It's where we can identify businesses associated with individuals in our client's consumer universe. So if you think of financial institutions, they have both consumer and commercial accounts, we can identify those business leads within that robust consumer portfolio. By robust, I really mean the volume of consumers within the portfolio. Using our Match technology and the historical Match Reference File, we can identify individuals who own businesses. So you think of CEOs, presidents, and any other additional executives. George L’Heureux: So, help me understand why this is so appealing to financial institution customers. Why would they to do this? Is the quality of leads you get back that much better? Holly Reed: So, it's the quality of leads, it's a warm lead and it's easy. I say, it's easy. So C2B provides new leads to cross-sell and upsell, and it really takes advantage of those existing relationships that you already have. So if you think about a bank, a banker or that relationship manager, they may know that consumer, that individual, and how easy would it be for them to, during a casual conversation like we're having, offer a commercial credit card. Also, they're more likely to open the offer since it's an existing relationship. So you think how many times are you receiving a piece of mail, and you're like, "Oh, I'm not dealing with that institution," you're going to throw it out. So we have an existing relationship, we're going to open it up. If the individual has a personal loan or a credit card, you may have a credit history. So, I have a client who has 44 million consumer accounts and 1.4 million business accounts. So if you think about that, that's a huge opportunity to cross-sell to people you already know. George L’Heureux: You talked a minute ago about the different types of historical data points that we could match to. You talked a little bit about CEO, other executives. Is there a way to limit kind of the scope of what you're looking for? I don't want to find someone who's maybe, and I'll put this in scare quotes, just the CFO of a company, I'm looking for just that CEO. Can I narrow it down that well? Holly Reed: Absolutely. Really, D&B can fine-tune really their matching dial. Using the Match metadata, which includes confidence codes, Match grade strings and Match data points, along with the historical Match Reference File. So using all of this information, these matching attributes, we can identify what exactly you're looking for. So if you're looking to target the CEO, we can say, "You know what? We only want to provide leads back that have a Match data point of 03, which is the CEO." So yeah, we can definitely utilize that Match data to get to the attributes that you're looking for. George L’Heureux: So we've already talked about this in the terms of financial institutions and how they'd use it, the 44 million individual customers that they have and the 1.5 million business customers and how those come together. Are there other segments of our customers, of our clients that would be able to take advantage of this? Holly Reed: Yeah. So the one that I think about that's probably underrated, I'm going to say like a tech company. So, they may offer equipment financing or consumer credit cards. So, how great would it be that they're looking at these consumers doing C2B matching? So instead of someone purchasing equipment based off of myself individually, an offer goes out for a business credit card or some type of business financing. When you're looking at C2B, there's so much more profitability within the business products, they're more expensive. So, it's a great opportunity. George L’Heureux: So, that's a great example of how we can use it in financial institutions, you and I have seen this really be applicable beyond that though. We were talking about dentists before we started recording this, and I think that would be a really interesting use case to share. Holly Reed: Okay. So just as you said, so another area that I've been working with one of my clients is professional designation. So if you think of dentists, so DDS, you think of Esquire, CPAs or MDs, what a great opportunity. You're looking for that type of profession within your consumer universe, your consumer portfolio. So with C2B matching, we can identify really that professional that is also that business owner. Working with this client, we were actually able to get to a 50% match rate from their C2B matching. George L’Heureux: Now, how do we narrow it down so that it's just on something, like in this case, dentists? Is that a use of an industry code or is there another approach that we do there? Holly Reed: Okay, so for this, it's the use of ... so D&B does have the SIC or the industry code, so yes, we can do exclusions, inclusions based off industry code. But the other key factor with utilizing D&B is that our goal or my goal is to really get you to that actionable, a lead list that you can use. We always approach it as a waterfall. Now we're not saying that all customers utilize this approach, and with the waterfall, it's really including and excluding leads. So, there's some factors that our customers need to keep in mind. So, are they managing to a pure number of leads? How risk-adverse are they? How are they going to measure the campaign? Those are just a few. So, let me give you an example of a customer I'm currently working with, and they are not using any waterfall approach, they're basically saying I'm going to measure the success of Dun & Bradstreet by the number of total matches and the number of high quality matches. That's it, there's no inclusion or exclusion. Now, is that the best way to do things? No, but that's how they're measuring success. The way that we recommend that you really measure success or what approach you should take is a waterfall approach. So, using key filters to include and exclude based on your strategies and the likelihood of the client not being approved. So, the first thing I always tell the customer is remove your current customers, you don't want to market to them. You don't want to waste marketing dollars, but you don't want them to be upset thinking, "Gosh, they don't even know I'm a current customer." So that's the first thing, but you can also then segment the leads. Are you looking for customers that are less than $500,000 in revenue or less than five employees? You can remove certain industries. Cannabis is on the bad list within certain banks that I deal with, so remove cannabis. The other thing we always say is utilize the D&B indicator. So, we have activity or marketability indicators that we can exclude from your lead. So the first is out of business, you don't want to market to someone who's out of business. Next, you can look at what we call unable to confirm or UTCs. So, those are records that are showing very limited business activity at that specific site. So, we can't confirm the status of the business. We're not sure if it's out of business, so we're calling it UTC. Now, most of our customers just automatically exclude UTCs from their leads, however, we have some very smart customers that actually include these UTCs and they test and learn from them, because what they found is that most people forget about this universe of records. So because most people forget about them, when this universe of records receives some type of marketing opportunity, they're the first ones to jump at it. Now you may not always approve, but again like I said, test and learn, see if and when you can use this set of records. Then the last two, public records. So you can remove open bankruptcies, you can automatically exclude those. You can also look at suits, liens, judgments, and the dollars associated with them. Then lastly would be really the credit worthiness. How are they paying their suppliers? Are they likely to still be in business in 24 months? Are they likely to pay their suppliers? So, those are just a few ways that we can help our clients utilize inclusion and exclusion rules to really get to the leads that they're looking for. George L’Heureux: What it really comes down to, it sounds like for me, is that it goes beyond just matching this customer information and finding a business from it, there's all these dials that customers can use to really narrow down their focus and make sure they're getting that lead list that they're really interested in. Holly Reed: Exactly. It's the ability to really move the dial to their use case and get to what they're looking for exactly. George L’Heureux: So, are there minimum requirements for someone to be able to do C2B matching with Dun & Bradstreet? Holly Reed: That's a great question. So absolutely, customer input data has a direct impact on the match quality. You've probably heard the old saying, garbage in is garbage out. So, I truly believe that. We always recommend that specific data points are provided on the input data or the input file. So for example, you want to include first name, last name, physical address, mailing address, if you have it, city, state, zip code, and also telephone number. So, that's the input data. Then from a volume perspective, no, there is no limit to the number of records that can be provided, because if you think about it, many consumer portfolios consist of millions of records. The last part of the minimum required, I just wanted to touch that C2B matching utilizes the same technology as B2B matching. We have the historical Match Reference File, contains billions of current and historical data, and those are really those alternative match points that we're using. So CEO, additional executives, executives. On a typical C2B match, the results are about 10% to 15% match, successful match rate. George L’Heureux: So Holly, before we finish up, let's talk takeaways. What would you want someone who's watching this or listening to this to kind of walk away and remember from this conversation? Holly Reed: I would say that C2B matching can help in a variety of use cases. We've talked about the financial institutions, we've talked about tech companies, professional designations. So if you think about it, it's an easy way to expand your lead pool. You can prioritize your leads on key data attributes, you can find new prospects. You already have relationships with them, so take advantage of it. I know that didn't come across right, but utilize those relationships to build upon them. Then lastly, reach out to your data advisory services team or your client director, we're here to help. George L’Heureux: Well, thank you, Holly. I appreciate you taking the time today to join me and to share your expertise on this topic. Holly Reed: Thank you. George L’Heureux: Our guest expert today has been Holly Reed, a data strategy consultant at Dun & Bradstreet, and this has been Data Talks. I hope you've enjoyed today's discussion, and if you have, I encourage you to share it with colleagues or friends. For more information about what we discussed on today's episode, visit www.dnb.com or talk to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us, until next time.
George L’Heureux: Hello, everyone. This is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a principal consultant for data strategy and the advisory services team at Dun & Bradstreet. In advisory services, our team is dedicated to helping our clients maximize the value of the relationship with Dun & Bradstreet through expert advice and consultation. And on Data Talks, I chat every episode with one of the expert advisors at D&B about a topic that can help consumers of our data and services to get more value. And today's guest expert is my manager, Liz Barrette, who's the leader of data advisory services at Dun & Bradstreet. Liz, now how long have you been with Dun & Bradstreet? Liz Barrette: 17 years. George L’Heureux: And In this role that you're currently in, what do you do? What are you doing for our customers every day? Liz Barrette: Well, I lead a team of incredibly intelligent data experts. And our role is to work with customers who are trying to take their data and turn it into power and ensure that Dun & Bradstreet is a significant value along the way. George L’Heureux: And before Dun & Bradstreet, you were a customer. Right? Liz Barrette: I was. Right in the middle of my D&B tenure, I left D&B, after about 10 years, and became a global master data practitioner and a D&B customer. George L’Heureux: It's interesting that you use that term, because that's exactly what we wanted to talk about today, was master data. Now over the last couple of years, and in particular, maybe the last few months, we've seen a shift in our industry where we're talking a lot more about the terminology that we use on a daily basis, sometimes without even thinking about it. One of those terms is master data. We're seeing some companies move away from that now. Can you talk a little bit about why we're seeing that shift? Liz Barrette: Sure. And I really do believe that the nature of the environment today is actually exacerbating it. But the terminology around, really, a handful of terms have always been very scary terms. Two of them that I can think of is the term "master data" and the other is "governance". They tend to invoke fear and loathing, I think, a lot of times out there. And I mean it in an appropriate way. Mainly from the perspective of mastering data, is extremely hard. And the term "master", nevermind the context in the environment today, it invokes the sense of something in control, in charge, something at a very highly skilled level, a sense of excellence. Liz Barrette: And coming in and trying to run these kinds of programs or supporting mastering, the reason I say it's very scary, most people steer away from that because it's so hard to do that, from the perspective of excellence and control, people want agile. They want things happening right now, today. And to be able to provide this capability requires a lot of understanding about data, an awful lot of understanding about people and the ability to move the needle, as well as really understanding the business and what you're trying to accomplish. So from that perspective, that terminology, "mastering", can be very difficult. George L’Heureux: So how- Liz Barrette: Then you have the other side... Sorry. Go ahead. George L’Heureux: I was just going to ask, how do we take some of the electricity out of that term, then? How do we take some of that charge out of it and make it clear to clients, to the industry, that it's achievable? Liz Barrette: Well, I guess the question becomes, what is it that they're really trying to achieve? Right? When you think of a master data program, it's from A to Z. And it's more about a journey through the mastering process, or through the journey of enterprise data management. Everyone that has data in a company is on this journey in some way, shape, or form. A lot of times they can be starting at the very beginning, where they're working with Excel and files and spreadsheets, and trying to make heads or tails of what that customer might look like, and what kind of business they're doing with them today. Another part of the journey may be that they're within their CRM system, and they're really trying to understand that the potential and the opportunity around those customers and prospects. The next part would be around bringing together their CRM and their financial systems, and bringing those together. It is a journey along the way. And each part along the way, you can get value out of. You can, what you would call master, or really make sure that the data is of a certain quality to provide the insight and the value that's going to bring power to the company at that time. And if we think about it from that perspective, they're doing this all along the journey. It's not a from start to finish and you have to wait to get those results. It is happening all along the way. So I think part of it is that terminology. “Master” means you're not going to be a master data program until you're finally finished and it's a global scenario. No. You're doing this all along the way. So trying to get them to understand it's a process, it's a perspective, but it's not necessarily about control. It's about using the appropriate tools and foundation and the data that's going to help them take their data and make it more valuable to them. George L’Heureux: So part of that shift toward that sort of understanding has got to be education around what you just talked about, which is that there is an iterative nature to this. There's a step-by-step nature to this, and it's not that all the value is going to be at the end of the journey, but it's steps throughout. Liz Barrette: Yeah. And also think about it from this perspective. That when you're working with data and you're trying to take it along its journey to power, to a powerful component, you're working with some of the most important assets that the company has: data and the resources around that people. And both of those are significant assets to a company. And to be able to do what you need to do, you have to be able to bring those together in a way that takes the data and maybe actions upon it. Maybe stewards it. Maybe changes definitions. But being able to have some kind of common thread in-between is really the crux of this. So when you're talking about education, the first thing you have to really understand is what is it you're trying to say? Where is the power going to come from, and what kind of power are you trying to get out of it? So thinking about it from that perspective, the education comes more around, how do we work together? What does the data look like in various different components, different systems that we have? How do they talk together today? And where are some of the gaps? Are we only looking at things from an internal perspective? How do we get the outside view? So there's all sorts of things that, you know, you can change the nature of this massive undertaking into something that has components to it, which will show that value. And during that time, you will be educating, you will be understanding, and working together collaboratively and collectively to move along that journey together. And most times you can't do it... I highly don't recommend don't do it without having expertise like a Dun & Bradstreet come to the table and support you with our global reference database. Mainly because you spend a lot of cycles and time trying to come up with something that experts have available to you already. And working with a partner can get you moving along that journey much faster, and much more effectively. George L’Heureux: And so you have that idea of not reinventing the wheel with things like reference data from a global data partner, like Dun & Bradstreet. And then you hit on something else that I really think is key. And I've seen in my career. Obviously, you've seen in yours, which is this idea, and this false idea, that an enterprise master data program is something that's limited to IT, or just one part of the company. In reality, for it to be successful, it's got to encompass functions and groups from around the organization. Liz Barrette: Absolutely. We have something here that we've worked on, which is almost an organizational readiness assessment. It's being able to understand what does IT think about their program? Where do they think they are in their journey versus a sales associate, versus the marketing team, versus the financial system and programmers and developers. And it's always really interesting to see that a lot of times we'll find that technology and IT may feel that they're further along in their process then, say, a sales person or the marketing team, because they're further down getting those assets through, and being able to take those and translate it to something that they can work with. And being able to understand how to interpret how people feel that they are engaged in their enterprise data management journey, I think, is very critical here because you can build anything and everything you want, but if people aren't finding value in doing something with the results of your work effort, it just doesn't end up being valuable. And usually, those are the programs that change up, are unfunded, and don't end up coming to fruition. And unfortunately, I see more times than not these programs being unsuccessful, because they're not thinking about both assets, the data and the organization, and how to make sure that, collectively, they're coming together to ensure value of your program. George L’Heureux: So I want to shift gears a little bit and tie it way back to something you said when we first started talking, which was about governance being another one of these terms that sends chills up the spines of people and has really loaded connotation. Why do you think that is? Why does that term, "governance", cause so much... I Don't even know the word for it. So much concern, so much frustration, so much worry in organizations? Liz Barrette: Well, just thinking about the term, et cetera, it's authority, it's policy, it's protection, it's rules, it's regulation. Most of the time, it means change in some way, shape or form. And most people will steer away from change wherever possible. When you're thinking about governance, you have to realize that it's more just about, in a sense, a governing orchestration. Think about our or our government today, right? We can't run as an entity, as a country, without having some kind of governing body helping us. If you're working on your data journey without having some kind of data governing body helping move that and progressing that, making sure that the policies and the procedures that are coming up around your data, let's just think about one. Creating a data record, right? A customer record. Making sure that you're creating it appropriately with the right amount of insights as you're going through the intake process. If you don't have that, if you're not making sure that it could meet the business needs, you have a heck of a lot of extra work that needs to go into it. So making sure you work together to understand what are the rules, the laws around your data itself, you will actually up not being as successful as you could be, and end up doing an awful lot of rework. But again, I think it's just terminology. Most of the time you have to have these processes, these governing bodies in place. But it doesn't mean that it's about ownership and restriction. It's much more about enablement for that consumption. George L’Heureux: When we talk about governance, sometimes those things can be as simple as when you're entering a record, like you suggested, into a database, making sure that there's not already that record in the database, making sure you're not creating a duplicate. And that's a fairly simple statement to make. And when you can break it down that small, sometimes they're really simple steps you can take to fulfill that need. Liz Barrette: Absolutely. And just by having, you know, "Please check your system to make sure that record isn't there" can be one step. And then, having someone review and make sure that the record you've created isn't a duplicate is an easy second step. Or a lot of times it's part of some of these platforms now, to make sure those are reconciled. But without thinking it through, without understanding what you're trying to do and how, you'll end up with a large database of stuff that you now end up having to sift through and work through, and it's a significant amount of effort. George L’Heureux: So you had mentioned that it was maybe a little bit terminology. Are there things that we do, are there things that we can do, that the industry can do, terminologically, to take some of the power, to take some of the concern out of the terms like governance or master data, to make it not so scary for enterprises that are embarking on these types of journeys? Liz Barrette: Well, I honestly don't know that we're going to change the word governance. I think that's pretty much a staple. It is what it is. I do think there's something to be said about master data. I don't know what the proper term is, but I really look at the idea of the Rosetta stone. It's a connection, it's a capability that enables this language to talk to this language, and this country to engage with this country. It's that same kind of thing that when you're bringing data together as a collective whole within a business, is about how do you make the data connect and talk to each other with some kind of common focus, some kind of common key that can help drive that enablement, that power of connection. So what's the right term, other than master data? I don't know. There's been so many terms that have worked around there. But I think it's really what works for your business. If you feel that master data, programs, process, whatever is appropriate for you, fantastic. Hang your hat on that and work with it. But if it's something that would cause friction or uncomfort within your business and your team, your resources, then you absolutely should change it to whatever is appropriate for you. More than not, the real trick is are you on the journey? And do you understand that you are, and where you are? And are you working with teams of people who can help you move that journey? So I'd love us to stay away from worrying about what the term is versus what are we doing to get there and move along faster. In this world right now of being able to digitize much more and having to work from home and having the need to understand things much more broadly from a perspective of things being so disparate, I think being able to bring your data together and connect it is even more imperative in order for businesses to succeed. So I can see many more companies moving ahead and saying, how do we do this together? How can our business proceed without being on this journey and understanding where we're at? So from my perspective, term is a term. We can call it whatever we want, with whatever business. Let's worry more about how we can help each other to progress. George L’Heureux: A rose by any other name. Right? Liz Barrette: Exactly. George L’Heureux: So before we go, Liz, if someone's listening or watching right now, and they're just at the start of this type of journey, whatever we want to call it, what would be your piece of advice for them early on? Liz Barrette: The most important is to be able to do three things. Make sure that your company has a vision, they have a reason, a need, to look at their data and use it in such a powerful way that's going to help progress the business. Think about what your strategy will be. How are you going to get there? And then who's going to own it, from a perspective of doing the work, nevermind the executive sponsorship. If you can put those three pieces together, now you've recognized that you've got something, and you need to do something. It's the who, the what, the why. And once you have that set up, or you're thinking about it, bring in someone like a Dun & Bradstreet that's going to help you to understand how to help you progress along that journey. Really, never do it alone because it is so time-consuming and difficult. And there's a lot of bias that can happen when you do things a lot more manually. There's so much available to support you and help you. And you can move along your journey within months if you bring in an expert to support you. George L’Heureux: Well Liz, thanks so much for joining me and sharing your opinion and your expertise on this topic. It's really been an enjoyable conversation. I hope others have gotten something out of it as well. Liz Barrette: I did, too. Thank you so much for your time, George. Really appreciate you. George L’Heureux: Our guest expert today has been Liz Barrette, the leader of data advisory services here at D&B. And this has been Data Talks. We hope that you've enjoyed today's discussion. And if you have, we please encourage you to share it with your friends, your colleagues. Let them know about the show. And if you'd like more information about things that we've discussed on today's episode, please visit www.dnb.com, or talk to your company's D&B specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L’Heureux: Hello everyone. This is Data Talks presented by Dun & Bradstreet and I'm your host George L'Heureux. I'm a principal consultant for data strategy in the advisory services team at Dun & Bradstreet. Here in advisory services, our team is dedicated to helping our clients to maximize the value of the relationship with Dun & Bradstreet through expert advice and consultation. On Data Talks, I chat each episode with one of the expert advisors at Dun & Bradstreet about a topic that can help consumers of our data and services to get more value. Today's guest expert is Scott Smith. Scott is a data strategy consultant at Dun & Bradstreet. Scott, how long have you been with the company? Scott Smith: Well, George, I'm proud to say I've spent my entire career at Dun & Bradstreet and I've been in positions everything from a financial and credit analyst to participating on our product teams, to leading teams and probably the most significant amount of time I've spent in an implementation role working directly with our customers, so probably two decades worth. George L’Heureux: There's not a lot of people who can say they've spent their entire career with a company. I think it must say a lot about the interest level in the work that you found within Dun & Bradstreet. Scott Smith: Yeah, it's really interesting. I'm part of what we call our data advisory services team and really at a high level and what our team does, we work with our customers to translate and interpret the meaning of D&B data and we're trying to help them with integration and consumption into their own environment. So what's interesting about that, I spend the majority of my time in our ... we have a really tremendous team on data advisory services with a lot of experience and we spend most of our time trying to help customers really understand data and helping them to get the most value from their purchase with Dun & Bradstreet. So I'll give you one analogy, George. I think this is a really good one. If someone buys a high-performance car, typically they will be able to drive that vehicle off the dealership and get from point A to point B. Likely, they won't take advantage of all of the features and functionality until they're educated about it, until someone actually shows them what that high-performance car can do. So it's meant to perform at a very high level, but until someone actually educates them and walks them through, they're not really seeing the full potential of that vehicle. So it's really the same thing with D&B data. When our team gets involved, we're helping our customers fully understand the data and maximizing the value and really, basically, we're trying to help them get so the data performs at a very high level. George L’Heureux: Well, I'll tell you, after we get done with this conversation, Scott, I'm going to have you come over and tell me what that one button in my car does that I've never used and still haven't figured out. Scott Smith: Sports mode, yeah. George L’Heureux: What we had wanted to talk today about was identity resolution, what a lot of people call matching. Let's start with the basics. What is identity resolution? Scott Smith: So you've heard matching, you've heard identity resolution. Simply put, it's when you're connecting your organization's data to a trusted set off commercial reference data. That's really what it comes down to. So I work with customers all the time where you have customers, let's say, trying to clean up their CRM, they're doing some basic data cleansing, removing extraneous characters from their data, trying to clean it up with the right data, for example, trying to link it. And we have many customers that really the ultimate goal here is to augment what they have in their environment with all the richness that Dun & Bradstreet has to offer in terms of data assets. So that's really matching - is trying to merge those two together. And I think in my experience we're looking at our customers are really taking advantage working with D&B because we have right now 420 million entities in our Data Cloud. So, it's really the goal here is to match to our Data Cloud and to give them, what we call, a D U N S Number, which is a unique identifier for a business. Once we do that, we unlock all the richness of the D&B Data Cloud into their environment. So really that's the first step in my mind. That's really the key is unlocking that first step with matching our identity resolution so they can get to all that richness. So demographics on a business, firmographics, predictive indicators and that type of value. George L’Heureux: So how would customers do this if they're not using Dun & Bradstreet? I mean, that seems like it has to be something they'd have to build themselves. Scott Smith: Yeah, well, that's a good question. And, George, we do see customers that are making that attempt to do their own matching. So there's MDM merge-match software packages out there. You see a lot of customers trying to do that type of activity. There's an exact match, a phonetic fuzzy match, that type of thing. But the key here is they're trying to do it without a referential third party like D&B. So that's really the difference here. Also, we provide that vast rich insights. I mentioned 420 million records that we're currently tracking to in our Data Cloud, but the other benefit is really the D U N S Number. I mean that's something around the world that is adopted as a unique identifier and really a trusted source. George L’Heureux: So how does the additional data help in matching and what the additional value does that D U N S Number bring after the match is complete? Scott Smith: Yep. Well, I think, really, there's a couple of different benefits to our customers leveraging the D U N S Number. I first want to tell you that the referential data is something that we really find beneficial because right now out of the 420 million records, you can imagine just the rate of data dynamics and data changes on an hourly, daily basis for that volume of records, right? You have businesses that move, you have different owners or CEOs come in. Businesses are expanding, contracting, hiring employees, laying off employees, that type of thing. And all these dynamics are taking place and we're tracking in what's called our match reference file. So the benefit to that is we take that 420 million and you break out all these different match reference points. Now you're over well over 1 billion match reference points. We're tracking the legal name of the business, the trade style, the registered name. We're looking at the business address. The current business address, former address, maybe the owner's home address. So all these different match reference points we have the functionality with identity resolution at D&B to leverage that vast array of match reference points. And our goal, again, is to match to the right entity within our Data Cloud. And so I really think that's what sets us apart and that's really the benefit that D&B brings. George L’Heureux: You hit on it there, and I want to follow up on that. Scott Smith: Yeah. George L’Heureux: We've got these more than a billion match points that we can match against. Great. Let's say we match and we return a D U N S Number. What are we doing to make sure that our customers have that trust that we did it right and that we gave them the right answer? Scott Smith: Yeah. That's really a good question because I think the matching in general is customers are trying to understand why the match was made and how it was made, right? So the metadata that we offer behind the scenes is really valuable in that process. So we have not only a confidence level that we're able to say, "Hey, at a confidence code 10, we're matching that precisely to a candidate in our match reference file all the way down to a zero, which is an unmatched." But we also have what's called a match grade string, which is really measuring the accuracy of each of the key components of a match. So the business name, the address information, it could be the phone number, we're going to score those and our algorithm will then correlate that to the confidence code. But in addition to that, we'll also provide what's called a match data profile, an MDP code. And the benefit there is, I mentioned before, let's say a business moves to a different location. They expand their operations, they moved down the street to a larger facility. We're going to be able to tell why we matched the way we matched. If we matched exactly to a business address, to a record, but our customer has a totally different address, we can explain why. We have that metadata behind the scenes that we can leverage. George L’Heureux: And that enables the customer to sort of, I guess you might say, double check. I don't know that I even like that word, but they can look at, like you say, why it happened. They don't just have to take our word for it. And that tends to be something that we talk about with clients all the time. Scott Smith: Yeah. And I think the benefit to having the match metadata is also we can fine tune. We can really customize our match environment based on the customer's use case. That is really our charter here because if you think about one use case on one extreme, I work with a lot of our large banking customers, for example. So I'll use an example as underwriting, if you're in an underwriting situation and you're approving a potential contractor for a line of credit, for example, or a commercial card, you want to make sure that you have some stringent rules in place to match to the right record in the Data Cloud. I mean, we got to make sure that's right to vet that business out or from a compliance standpoint, you can have other customers that are leveraging a totally different use case for marketing. The match rules might be a lot less stringent for that type of application. So using that metadata behind the scenes, we're able to almost turn the dials, if you will, to either accept or reject, depending on your use case. So it is very tuneable and customizable. George L’Heureux: Well, let's get a little tactical here. Someone's listening and they're not currently using Dun & Bradstreet match capabilities. What are the minimum requirements? What do we need from a customer in order to try and attempt to match? Scott Smith: Yeah. That's an excellent question. When our team gets involved with an engagement, that's really one of the first items of discussion, the task on the list, because if you think about our match technology, the minimum requirement is a name and a country. But if you have on the input coming into the process, if we can have the business name, the complete address and correct address for the physical location, potentially the mailing address, the city, state, the zip, the postal code. If we can have a phone number, maybe even a URL or an email address, the more data points and even a business registration ID, for example, or tax ID, the more input we have, the better chances we'll have a higher match to the match reference file. The other process that I always emphasize is more is not necessarily better. We have to have the right data populated in the right fields. So that's part of our, at data advisory services in terms of diagnostics on the front end, we're working with our customers to help them understand how we can get to the best possible match, the highest possible match rate so that whatever their use case is you'll see the benefits downstream. George L’Heureux: So let's talk about why you do this real briefly. If you're a customer and you're not currently matching to get a Dun & Bradstreet D U N S Number, what are some of the risks that you're taking on for your business across a couple of different use cases? Scott Smith: Yeah. That's great and I'll go back to my underwriting example. If there is a potential, let's say they're using their own software, for example, and potentially not matching to the right business or doing a lot. And there's a lot of manual work involved to do your own homegrown, I guess you can call it, match algorithms. And so if you're matching to the wrong business from an underwriting standpoint, there's obviously a potential for write-offs and bad debt. You get into that angle of losses. On the flip side of that, if you're in a marketing environment and you're not capturing or onboarding the right business, you're losing out a potential revenue stream. So I really think the third part of that is leveraging back to the D U N S Number. D&B is able to connect business entities and affiliated companies through the D U N S Number. So I could be operating as a subsidiary of a parent company or a headquarter company. And just having that D U N S Number allows us to link and associate business entities together. So tremendous benefit, whether it's compliance or supply management or risk management or marketing, all those different use cases. There's a monetary, there's a return on investment for each one of those use cases, so that the higher match we can get to the Dun & Bradstreet Data Cloud, the better. And again, the whole goal is to unlock all the valuable data assets within that Data Cloud so the customer can make the right decision. George L’Heureux: So before we wrap up, let me ask you if there's one key takeaway that you would want listeners to this or people watching this to walk away from this conversation, what would that be? Scott Smith: Yeah, I would say it all starts with matching or identity resolution. It's finding out the correct entity and matching to the right D U N S Number. I think that's really the first key benefit. The data advisory team, we have a deep understanding of our match reference data and our metadata. And we have been involved in many, many engagements from a data stewardship standpoint. The process alone, we've successfully implemented the match optimization process in data advisory services hundreds of times over the past few years. So our team's approach can really guide our customers with this process and we've been really successful with it. So I guess the key takeaway is connect with the data advisory services team and we'll work with your customer to make sure that we get the highest possible match rate so that whatever their use case is, whatever their goals and charter is, we'll see success. George L’Heureux: All right. Well, thanks, Scott, for taking time to participate and share some of your expertise on this topic with us today. Scott Smith: Certainly. Thank you, George. George L’Heureux: Our guest expert today has been Scott Smith, the data strategy consultant here at Dun & Bradstreet. This has been Data Talks. I hope that you've enjoyed today's discussion. And if you have, we encourage you to please let a friend or a colleague know about the show. And if you'd like more information about identity resolution, match optimization services, or any of the topics we discussed on today's episode, visit www.dnb.com or reach out to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us, until next time.
George L’Heureux: Hello everyone. This is Data Talks presented by Dun & Bradstreet and I'm your host George L'Heureux. I'm a Principal Consultant for Data Strategy in the Advisory Services Team at Dun & Bradstreet. In advisory services, our team is dedicated to helping our clients maximize the value of their relationship with Dun & Bradstreet through expert advice and consultation. And on Data Talks I chat each episode with one of the expert advisors at Dun & Bradstreet about a topic that can help consumers of our data and services to get more value. Today's guest expert is Joe Santos. Joe is like me, a Principal Consultant for Data Strategy here at Dun & Bradstreet. And Joe, how long have you been with the company? Joe Santos: Wow, it seems like forever and a more to that. Thanks George, but almost a year, nearly a year. George L’Heureux: And tell me a little bit about what you're doing in your role. Although I think I might have a bit of a clue. Joe Santos: Absolutely. So the way I see our role is bringing out thought leadership both inside and outside of Dun & Bradstreet through contents such as this, write ups and through client consultations as well. The most important thing that I've come to know about our role is really thinking what else is possible for data. And that for me defines the role, it has us to really elevate not just the role of data, but the usefulness of data for decision-making. George L’Heureux: And before you came to Dun & Bradstreet, you were actually a customer of Dun & Bradstreet. And we've talked about this a lot you and I. You've always talked about how important it was having a referential data partner in the work that you were doing before joining the company. Why was that so important to you? Joe Santos: Absolutely. So just to be a bit of a background, I've been on the customer space for over 20 years and a customer for about 10 years. And referential data is like having a referee. If we're going to put an analogy here it's having that referee in a conversation or think of yourself. Well, if I ask you who was a champion for Formula One last year in 2020? In the conversation scenario, there'll be people who are knowledgeable of the topic, who would say it's Lewis Hamilton, British driver from team Mercedes. And there are others who are not as informed that could speculate and create their own answers from their own biases. Now, with referential data, we could go to the Formula One site or be it a Wiki page and removes all of those biases and adds really strength to your data when it comes to facts. George L’Heureux: And I'm glad that you know that it's Lewis Hamilton, because I probably would have guessed like Don Mattingly who played for the Yankees back in the '80s. But really when it comes to, what is it that gives us that trust in some outside authority? Why do we trust the Formula One website, as opposed to just my recollection or your recollection or somebody else, what makes them authoritative? Joe Santos: So let's take a couple of steps back. So among data practitioners, there's an inside view and an outside view. What's the inside view? It's the data that's generated within the company that has some biases in it, some good, some bad, and a lot of these are done because of their internal procedures, policies, many, many other reasons. And these built in nuances affect the view of the data and ultimately its quality. And sometimes the chicken and the egg thing happens, sometimes at the quality of the data that affects those nuances. And of course, oh, I have my favorite saying, that's how we've done it forever comes also as a hurdle. But with external referential data, it's created by experts. It's gotten through a lot of scrutiny and govern through tried and true processes and policies, simply put, external referential data helps you to know what you don't know. And because of these three things I mentioned; done by experts, added scrutiny, continuous truth to any of the data and tried and true policies, they get to be trusted and be referenced more and more by people. George L’Heureux: It's funny what you talked about there, knowing what you don't know, reminds me of, I think it was former Secretary of Defense, Donald Rumsfeld, who would say, there's what you know, there's what you don't know and there's what you don't know you don't know. And it sounds like the referential data is helping us to address the third of those things. But what really is the harm? If you don't use referential data and you're using some of this internal data that has these biases that you're talking about, what's so bad about that. Could you talk about maybe the range of possibilities that could occur when you use that type of potentially biased internal data. Joe Santos: Just to add to those quotes, ancient philosophers like Confucius mentioned that true wisdom is knowing what you don't know and more contemporary philosophers. And I consider Jim Rome as one of them puts it so plainly, and this is where the bias has hit us. What you don't know will hurt you, you are limiting your view for the solution you're trying to get to by these known biases. Referential data helps us address these blind spots. If you're looking at mergers and acquisitions and rely on your clients to tell you that, you're already late with your strategies, you need to know when these happen and put this in your data as they happen. How can you do a cross-sell or an upsell when not knowing who owns what. And being able to do that repetitively removes these internal biases and adds relevance, reduces the burden of data upkeep and in turn increases the relatability of your data and reduces those consequences. George L’Heureux: You're talking about real life consequences of having bad data or having bias data in it. And I suppose that that's really the key when we are talking to clients, it's about trying to figure out what the consequences are and how they could impact the business goals that our clients have. And using a referential data partner like Dun & Bradstreet allows them to your point to reduce the burden of up keeping their data and have a much more reliable data source. Joe Santos: Absolutely. That's where rubber hits the road George. If you take a look at the sales and marketing perspective, if you have an account strategy that you want to accomplish, but without outside reference data, will you be successful? Maybe. I've seen some do it, but there are limitations that are inherent to that strategy or the lack of strategy. But why take that chance? Why create an ABM, a campaign or a go to market campaign? ABM is account-based marketing, without knowing the layers and the players. Why risk your bottom line? And that's a company like Dun & Bradstreet can really help. George L’Heureux: I think one of the things that I always reflect on when we get to talking about stuff like this, is that the way that companies grow is organic. You start, you have a product, you have an idea, you start selling it, it gets some traction. You don't necessarily plan as you're in a growing for all the steps that you're going to need along the way. So how do you and you're former customers, so maybe you can speak to this. How do you know, how did companies know when that point has come, that it's appropriate to figure out which referential data providers you need? Joe Santos: So that's an excellent, excellent question. You have to understand what your goals are and what you really want to achieve. For instance, in around 2010, we have this need to make sure that we understand a company's credit worthiness so that we could extend the line of credit in, was it the proper proportions to reduce risk, because that really puts the company in a huge level of vulnerability that's really unneeded. So initially we would take a look at just the deal size, perhaps a company size, send out a survey, or just give out a default amount, but why again risk that when it is attached to our bottom line and we know areas we're in we were lagging, or we were failing at that. But being able to have a company like the Dun & Bradstreet to help us with risk referential data allowed us to really customize the credit extension that we could give to these companies and allowed us to really make these good choices with regards to assessing risk and as well as improving our company relationships. George L’Heureux: So I want you to try and take off your Dun & Bradstreet hat for a second, but not completely. You're a former customer. You got to that point with your company, where there is an obvious need for referential data. What is the decision process like? How do you come to choose Dun & Bradstreet and why? Joe Santos: With regards to choosing a referential data partner, we need to be able to define what partnering is. It's really sharing accountability. And we need to maximize that investment within the vendor. With trying to find a vendor or trying to find that partner or a vendor that we could partner, we need to take a look at the depth of the data that they have, the reputation within the market or within the industry. And of course, is there data relevant for us? And those were the three things that we typically looked at in looking for a referential data source. And in this case, the fastest way to get there is to be talking to a vendor who are experts within the data scheme. George L’Heureux: So we'd talk about this every day with our clients. And I think it's important to draw out some of these points here. Once customers engage with Dun & Bradstreet, and we're working together with them, what can they do on their side to help maximize the value of the referential data that they can acquire through us? Joe Santos: So I'll bring up again, partnership. That's a word that's very crucial to this. Partnering up means sharing accountability. Within getting an external partner you need to make sure that you get that from handshake and outside of this handshake there's the partner side or the vendor side, and there's the client side. You need to make sure that to maximize that, your data is in a form where in its ready to have that handshake. And in the past, when we brought in Dun & Bradstreet, we did that. We brought our data into a language where in, we could get that outside view repeatedly, and then we hold Dun & Bradstreet accountable for the accuracy of the data that they're giving us. And this is what's needed before we can really truly maximize this outside referential view. We need to make sure that we have this before we have an enterprise view off the customers, because if we don't have this, the enterprise view, you'll be doing a lot of things within the company in different silos, reinventing the wheel within multiple sub works. And it happens a lot George, and there's a lot of duplicative efforts that's being done that sometimes it's unnecessary, people might be doing or having the same issues, but not asking the right questions. So maximizing partners like Dun & Bradstreet, we involved them in these communications. We involve them with the problems that we have so that they can in turn consult best practices beyond the data that they offer. George L’Heureux: I think that that's one of the things that I enjoy in you that expressed to me, we enjoy together most about the role that we share here at Dun & Bradstreet is that ability to encourage partnership and to really underline the idea of it being a two-way street of accountability, where we want to help them, and we can speak sometimes difficult trues to them about what needs to happen in order for them to gain the maximum value from us. But Joe, before we finish off, let me ask you if you could summarize what the one thing is that you want people to take away from this conversation today, what would that be? Joe Santos: Getting the right data George, is only half the battle. It sounds like a GI Joe ending, right? It really is just half the battle. Yes, you need to get the data. You need to get the right data to make the right decisions, but you have to get beyond that immediate gap, that need for that gap to be filled because in reality, an ad hoc request is much more than an ad hoc request. It's sometimes just a symptom. We need to really take a look at bringing in referential data on a consistent basis to strengthen our data practice internally, as well as to widen our view of the outside world. George L’Heureux: Well, thank you, Joe. I appreciate you coming on today and sharing your expertise on this topic of referential data. Joe Santos: Thanks again, George, it's been really fun. Let's do this again sometime, dude. George L’Heureux: All right. Our guest expert today has been Joe Santos, a Principal Consultant for Data Strategy at Dun & Bradstreet. And this has been Data Talks. We hope you've enjoyed today's discussion. And if you have, please let a friend or a colleague know about the show and for more information about what we discussed on today's episode, we encourage you to visit www.dnb.com or to talk to your company's Dun & Bradstreet specialists today. I'm George L'Heureux. Thanks for joining us, until next time.
George L'Heureux: Hello everyone. This is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a principal consultant for data strategy in the advisory services team at Dun & Bradstreet. In advisory services, our team is dedicated to helping our clients maximize the value of their relationship with D&B through expert advice and consultation. And on Data Talks, I chat every episode with one of the expert advisors at D&B about a topic that can help consumers of our data and our services get more value. Today's guest expert is Don Folk. Don is a data strategy consultant at D&B and Don, how long have you been with Dun & Bradstreet? Don Folk: I am surprised to say this, but it's 23 years this month. George L'Heureux: And tell me a little bit about what you do in your current role as a data strategy consultant. Don Folk: So my current role is really around making sure the customer understands the value of the D&B data process flows, everything like that. I'm specifically an expert around matching, identification of what we're going to talk about today is duplicate, but also in regards to using the D&B data assets to the fullest abilities and capabilities that we can. George L'Heureux: And tell me a little bit about how you got to this point in your career. What was the path that made you interested in this and got you into this role? Don Folk: Sure. So over 23 years, as you'd expect, there was definitely a lot of positions that I shifted through. Starting through our delivery organizations, I understood how clients really asked for our data and used our data. And then from the delivery organizations, which I would actually create the deliverables that went out to our clients, I shifted over into our content organization and really understood what it meant to be a vendor for D&B, really for us D&B purchasing the data, their data, whatever it may be, but then I would actually help ingest that vendor's data into the workflow. So I have a pretty holistic view of data in general. I understand from the customer side, but also from the vendors side. George L'Heureux: And I think that that puts you in a really good spot to talk about the topic that you and I had agreed on here. It was really important for people to hear about, which is the idea of duplicates. And one of the things that our team deals with, with just about every client, is how many duplicates we're seeing in their data. Why is that even a thing? Why do we care? Don Folk: Yeah. I mean, realistically D&B even has duplicates in the database. We have best-in-class processes that we use to identify and resolve those duplicates, but every database in the known world has duplicates. It's just a by-product of really just collecting data from multiple sources. George L'Heureux: And so they're there but I imagine the reason that we talk about them is that they can cause problems, right? Don Folk: Very true. George L'Heureux: What kind of problems do we think about when we're talking about duplicate records and the impacts that they can have? Don Folk: Yeah. If I could sum it down to one word, it's really confidence. If the duplicates are in the data it creates this lack of confidence from your sales organization, being the client of D&B from accounts receivables, accounts payable, supplier management, all of those activities have a different structure and complication of duplicates. But again, it's really just that they're there. We know they're there and it just creates that level of lack of confidence whenever one of your salespeople find it in your repository. George L'Heureux: And if you've got more than one of something in your data store, in your database, the chance exists for me to go grab one and you to grab another and not realize that they may have two completely different views of the same customer. That's one of the challenges you're talking about there. Don Folk: Yeah, absolutely. And if we look at it from a marketing perspective, sometimes that isn't always the worst case, but if we tie it to accounts payable, accounts receivable, things where there's important decisions that are tracked at each of those independent levels and you potentially could see that some of the accounts owing or receivable from dollar amounts could be tied to both of those accounts. So definitely having those disparate duplicate views is a significant problem. George L'Heureux: Right. I mean, at that point you're not just talking about an extra record here or there, but you're talking about things that could roll up and eventually impact financial filings. Don Folk: Absolutely. And those are the concerns that you need to be mindful of as a data supplier, data collector, data aggregator, for sure. George L'Heureux: Okay. So then what do we do? How do we address the problem of duplicate data? Don Folk: Well, there's definitely a multi facet approach. First, you have to understand your use case. If it's strictly marketing, your exposure level is lessened. But if it's, like I said, from an accounts payable, accounts receivable, some supplier stuff, you have to be more aware of it. The key to this is really getting that D-U-N-S Number, our unique identifier. You have to get that D-U-N-S Number on as many records as you humanly can, possibly can within your own repository. That's the key. That's your first step in identifying the duplication. George L'Heureux: So how does that help? We get the D-U-N-S Number on all these records. What do we do next? How does that D-U-N-S Number help us? Don Folk: Yeah, so that D-U-N-S Number is the unique key that permits us to say, "This specific business entity looks and feels like this record within the D&B file." If you have multiple records or entries in your repository with the same D-U-N-S Number, that's the definition of a duplicate. Now there's reasons that can occur. And I'm sure we'll talk about that in a few minutes, but definitely that unique D-U-N-S Number will be that identifier that you can consolidate and collapse on to identify those duplicates. George L'Heureux: So we know that that's going to help for a large majority records that customers have in their databases. Anything that aligns with the D-U-N-S Number they're going to be able to see whether or not there's D-U-N-S Number overlap in that set. There are some records that for various reasons aren't going to get D-U-N-S Number. How do we help with those? What can be done with that set of records? Don Folk: Yeah. So I think the first part is understanding why they aren't actually becoming or able to be or have a D-U-N-S Number assigned to it. Is it lacking information? Or is it because the information that's supplied is in a structure that's confusing the match engine tool to a magnitude that we can't help support that D-U-N-S Number assignment process. So one is let's do a review of your data to try and figure out exactly why we can't get a D-U-N-S Number. And then secondarily, if we come up with reasons and there are valid reasons why, but we don't have D-U-N-S Number assignments, that's whenever we have to start thinking a little bit differently about how to identify duplicates within that universe. George L'Heureux: And you and I have talked before about how the presence of a D-U-N-S Number actually has a little bit of a multiplier effect. Not only are we able to get the value out of the D-U-N-S Number itself, but we've seen over time with clients that those records that don't have D-U-N-S Number actually have a higher incidence rate of having duplicates in the dataset. Don Folk: Yeah. Without a doubt. And there's many reasons behind that, but the primary kind of theme to this is that if there's missing information that prohibits us from assigning a D-U-N-S Number, that probably means it makes it more complex for us to identify if we're looking into the right business. And we could generate duplicate entries in the database to try and mitigate some of that. So it's definitely the lack of information to be able to link to a D-U-N-S Number makes it more prone to a duplicate within the repository. For sure. George L'Heureux: I've always found that really interesting. But let's say that we get down to that set of duplicates that we know are there now, whether it's using a D-U-N-S Number or other methods for things that maybe couldn't be D-U-N-S Number. Once you've identified all those dupes, how do you go about resolving them? Don Folk: And quite honestly that's the hard part of the whole equation. Identification through D-U-N-S or through other means is definitely probably the easiest aspect of this. The resolution is whenever it is very client specific. The resolution process, because we have to be mindful that all of the information that's being carried along with that specific entity within your own repository needs be consolidated. We need to actually collect that information of accounts payable, accounts receivable, all of the supplier based information. All of that information has to then be collected and consolidated into one single view of a single client of yours. George L'Heureux: So you talked about it depending on the client, how are some ways that clients might need to, let's call it personalize that resolution process beyond just aggregating the data into a single record? Don Folk: Yeah. Like I said, it's definitely, you have to be mindful of what specific use case you're going after, but again, it potentially could be a manual review. It could be outsourcing of that consolidation once you have identified the duplicate. Creating that survivor view or kind of cherished view is something that you have to be mindful of. We can certainly help you with that, in that defining what that resolution process would look like. But again, it's definitely something you need to be aware of, it's probably the most complex component of this topic. George L'Heureux: So with the potential downsides of having duplicates in your data and obviously the related benefits that are associated with taking care of them, identifying and resolving them, why does it even remain a problem? Why aren't more companies, why isn't everyone staying on top of issues like this? Don Folk: Really, it's really around the complexity of this. A great example that I worked with a client on was there was an initiative that their sales team put into place that the individual sales team members would get an additional bonus if they brought on new supplier clients. And the outcome of that was that the sales teams created new business records for previous suppliers of this source. So the client intentionally tried to make a initiative to grow sales, but indirectly created a duplicate problem because the sales teams just put the same records in twice and it kind of looked like a new record, but it was an old business and that created that duplicate effect. George L'Heureux: So let's talk about standards, guidelines, best practices, what are some best practices that companies can use to perform duplicate resolution and really know when they've made a difference or when the juice is no longer worth the squeeze? Don Folk: Yeah. So first is, if you have the D-U-N-S Number and it's assigned definitely do some analysis to figure out exactly what your rates would be in the D-U-N-S Number universe. Knowing that that's probably the most pristine records within your database, if your percentages are over a certain threshold, then you should be concerned and certainly as a first step, look into your D-U-N-S Number universe to see what your duplicate rates are. George L'Heureux: Do you have a feel for what a percentage above which people should really be concerned is, or is that another one of the things that really depends on a client and their particular use case? Don Folk: It definitely depends on client use case specific, excuse me, specific stuff, but general a general rule of thumb is 5%. If you're exceeding 5% from a duplication rate, you really kind of got a problem that you need to address sooner rather than later. And that's a general statement. I mean, best-in-class is generally around one to three, is really my industry feel for a standard, but anything above five is definitely something that you need to address sooner rather than later. George L'Heureux: Okay. So Don, as we wrap up, someone might be out there listening or watching and they're hearing this and wondering whether or not their database has this issue, how prevalent it is and what it might be impacting them with, what do they do as a first step? Don Folk: So like I said, definitely understand what that duplication rate is by just looking at your own database, if there is a D-U-N-S Number assigned. If there isn't, I think what I would recommend is really reaching out to the consultant team. I think that we can certainly help you with some best demonstrated practices and best means of identifying those duplicates. But again, I think it's really just looking into your own database where there is a D-U-N-S Number to start to figure out exactly what that rate would look like for your specific use case. George L'Heureux: Well, hey, Don, I really appreciate you taking time to sit and chat with me about this topic and sharing your expertise over your many years of work here at D&B with everyone who's watching or listening, helping them understand the importance of identifying and resolving duplicates. Don Folk: Well, thanks for having me. I really enjoyed talking about this, it's passion. George L'Heureux: Our guest expert today again has been Don Folk, a data strategy consultant here at Dun & Bradstreet. And this is Data Talks. We hope that you've enjoyed today's discussion and if you have, we encourage you to please share it with a colleague or a friend, let them know about the show. And if you'd like more information about things that we've discussed on today's episode, please visit www.dnb.com or talk to your company's D&B specialist today. I'm George L'Heureux, thanks for joining us. Until next time.
George L'Heureux: Hello, everyone. This is Data Talks, presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a principal consultant for data strategy here in the advisory services team at Dun & Bradstreet. In advisory services, our team is dedicated to helping our clients to maximize the value of their relationship with Dun & Bradstreet through expert advice and consultation. On Data Talks, I chat every episode with one of the expert advisors at Dun & Bradstreet about a topic that can help consumers of our data and services to get more value. Today's guest expert is Liz Walters. Liz is a data advisor at Dun & Bradstreet, and Liz, how long have you been with the company? Liz Walters: I have been with the company over 20 years. So a lot of time spent working with our customers in exactly this capacity. George L'Heureux: And can you tell me a little bit more about what it is that you do in this role? Liz Walters: Well, we help our customers make the data actionable, make it useful, and where a lot of that comes into is in order for our customers to get the anticipated value so they can do the scores, the metrics, the analytics, the starting point is getting their data to map to our D-U-N-S Number, because that facilitates linking our data to their data and they can do all those other wonderful things. And so that process we refer to as matching. And so a lot of that 20 years has really been spent in helping our customers match. How do they find the right D-U-N-S Numbers for their business universe? George L'Heureux: Well, I think we should switch seats. What a great segue, Liz, into the topic that we wanted to talk about today, which is match accuracy, confidence, and how do customers improve their confidence in the matches that they're getting back from Dun & Bradstreet. Other experts that we're having on the show have talked about identity resolution match, so let's talk about match confidence. What is it? What does that mean? Liz Walters: Match confidence is D&B's way of expressing how confident we are that we found the match that you were looking for. Our match process doesn't deliver a yes or no answer. Yes, it matched. No, it didn't. The process is designed to identify what we believe to be the best candidate, and we express why we feel that it's the best candidate to you through a certain something called a match string. It's got a series of 11 different data elements with grades. And to make that more useful to our customers, we translate that into a confidence code. So how strongly do we feel that we've found the correct match for your input criteria? George L'Heureux: And so why is that match confidence important? Why are those grades important? What do they do for a customer? Liz Walters: Well, it really helps hone in and understand that is their match project going the direction that they want to. To give an example and take it out of business world, say that you're looking for your high school friend, Liz Walters, who you know lives in California. If you look for Liz Walters in California, you will find, I think there's about 10 Liz Walters in California, and one of those is probably the Liz that you're looking for, but you really don't know for sure. You have no high degree of confidence how to move on with the data. Which one of the 10 do you focus on? What do you do next? And so that result will be expressed to you, "Well, here's the Liz Walters we found with a low degree of confidence," and then you can decide what additional work do you want to put into to figure out is this the Liz that I'm looking for versus if you had a Liz Walters in California and you had an exact match on a phone number, well, now you've got a high degree of confidence that you found the right Liz Walters, and then you can now proceed with your next step with Liz Walters. George L'Heureux: And that's what we basically refer to as match stewardship, where that confidence level allows you to take certain steps automatically, or maybe they fall into another bucket where you have to do a few more manual steps to confirm that it is who you think it is. Maybe you call all those Liz Walters up and ask if they went to the same high school as you. Right? Liz Walters: Exactly. So it really helps. So when you've got your match criteria, it helps group those inputs. So, okay. These you can do an automatic action on, some you will want to take some manual intervention with, but the match process will help you narrow down on that bucket of who you need to spend some manual time with. George L'Heureux: So when we think about how we improve match confidence and try and get more up into that sort of automatic action step, what are some of the recommendations that we end up making to a lot of our customers that are useful to them in terms of improvement? Liz Walters: Well, a very important component of any match criteria is input data quality. What data are we getting from the customer in order to match? And it's always worth a little bit of time to go through that data to make sure that you're giving high quality data to D&B. Just as an example, we did a match once for a large manufacturer and the results came back that they said, "D&B, you clearly don't know what you're doing. You matched a third of our file to donut shops. We don't do business with donut shops." And it turned out that what was happening was that the customer was writing, "Do not use," in the customer field for records that they meant to retire, and those hadn't been cleansed out of the input file. And so, “do not use” looks a lot like “donut”, so based on other criteria, there were matches to donut shops. So things like that, customers don't always assume ahead of time that there may be things in the input file that they really don't want to give to D&B to begin with. So if you spend a few minutes looking at the input data, am I giving data that I really ought to be giving to D&B for as part of this match process? George L'Heureux: Well, great. So now not only do I want donuts, but I'm going to be completely unable to focus on the rest of this. So when you get a file, is that something that you're looking for? Do you go through and try and see whether or not a customer has sent things like that? Is it something that you call out to them? Liz Walters: Yes, absolutely. We'll go through, and it generally doesn't take too much time, but it's good just to kind of sift through, I kind of call it playing the data, letting it flow through my fingers, just to catch anything odd. So one of the big things that we would look for are big blanks of blank data. The less information that we get to help us identify the customer, the less confident match we'll have. So going back to the Liz Walters, if you're looking for Liz Walters in California without an address and a city, it just makes it that much harder to come up with a strong decision. So if we're getting a customer file and they're missing a lot of street addresses or missing a lot of cities, that's something to call out that says we can still attempt to make a match based on what you're giving us, but be aware that that reduces our ability to give you a high confidence match. George L'Heureux: That can't be the only thing that we're looking for, though. Liz Walters: Oh, no. George L'Heureux: I mean, blanks are obviously going to be a problem. What else are you looking for? Liz Walters: Another thing is things like test data. Something that I like to do is do a frequency count of the various input fields, actually both the frequency count and an alphabetic sort, because both of those ways of looking at the data has a tendency to float weirdness to the top. I hesitate to say bad data, because almost always that information is useful for some purposes. So going back to our donut shop, the do not use was useful information for that customer, for their purpose. It just didn't help us with the match. But if you do the frequency counts and the alpha sorts, weird things will tend to float to the top and you can see, "Wow, I really didn't recognize that I'm giving so many numeric values to D&B." Maybe that's a contract number instead of a business name, or if you do have a habit of doing ... We see things like “test”, “do not use”, “internal use only”. Words like those will float up to the top and then you can go, "Oh, wait a second. Maybe I need to take a second look at how I pulled this data to give it to D&B." George L'Heureux: I think it's a really good distinction that you're drawing there that it's not necessarily bad data. It's just maybe data that while it's useful, it's in the wrong spot or lacking context. It could be confusing. I know that I've made suggestions, "Hey, let's move this do not use indicator into its own field," or, "Let's move this over into a different place." Another thing that I see a lot of the time along those lines is “attention” or “care of”. When I see “attention” or “care of”, that indicates to me we've potentially got two different companies here, and that can really get confusing. Liz Walters: Yes. When information is conflated, that can be very difficult for D&B to work with because we don't know, well, is their address, is that for the company that you're really doing business with or is that just where that company happens to be located for this engagement? And that's not going to help us find the business that you're looking for in our database. George L'Heureux: So all of these tactics that we're talking about, the searching for blanks, looking through frequency counts to checking for “do not use” so that you don't get all donut shops, these are all tactics that are part of a broader strategy. Is there a way that you define what that overall broader strategy is? Liz Walters: Do you mean for the customer? George L'Heureux: Either for the customer or for what you're doing on behalf of the customer. What are we really trying to get to in doing all of that? Liz Walters: Well, in part, it really would be towards match optimization. How can we optimize your match results so that you get the most useful data at the end of the match process? George L'Heureux: When we do this type of thing, have you identified ... I guess I should ask rather we're able to quantify how much better those match results look for a customer before they take these types of data quality actions versus afterwards, aren't we? Liz Walters: Yes. So you can compare. You can isolate some of these, the bad stuff, stuff that maybe you shouldn't have given to D&B, and compare and show that if you hadn't given that to us, your overall match rate would have been X percentage higher and just impact of wow, what is happening if you're giving us information that we can't use to match. George L'Heureux: The other thing that strikes me about the strategy is that it results in broad categories of data weirdness, to use your word, that we can address with simple solutions. If you've got “do not use”, then the solution is to strip “do not use” from that field. If you've got tests all over the place in the field, you're removing the word test or dealing with blanks in a similar fashion. So hopefully you can take the biggest issues and start to eliminate them from having a quality impact on your match. Is that right? Liz Walters: Definitely. And it can highlight for customers if there are patterns that they really weren't aware of, they can take separate steps. Another pattern you might see if you see a lot of personal names, if people are putting the wrong ... Instead of putting the business name, they're putting their contact name, maybe another field needs to be entered into the input so that you can specify, okay, here's the business name versus the person that I tend to talk to at that company. So it helps open up the customer's eyes to different ways they may want to organize their own data. George L'Heureux: So customers can maybe recreate or replicate some of what you're doing by taking some of the steps that you've identified here. If they do detect problems, are there steps that they should take before they send us the data to match? Liz Walters: Yes, ideally ... Well, again, it depends on the nature of the customer. If there's things like if there's ... It's a lot of test data that you want to strip out and don't give to us, because that's just going to muddy the waters in the results. If they're finding things like there's a lot of conflated information, maybe, well, create another record or clean up that record so that it isolates the one that they're really looking for. If they are interested in finding information on both those companies, create a separate record where they clearly identify company one versus company two. George L'Heureux: Right. And the other thing, and oh, gosh, this is always an issue and you want to try and make sure that you're finding the right balance here, but do you have any recommendations on how to help customers recognize when maybe the juice is no longer worth the squeeze. Where the work that they're putting into trying to improve their data for match purposes, it's just costing more than they're going to get out of it. Liz Walters: Yes. It's hard to quantify. You can't say, "When X happens," but when you realize you're spending a lot of time on a file and you're not finding any results, you do realize there's a threshold where it is time to move on. So you could, if the customer has a value indicator where they, how valuable are these customers, you may get to a point where I'm identified, I'm able to resolve all of the customers that are truly important to me. Maybe it's just not worth it to me to spend additional time on this lower subset that I am not interested in. Or a lead versus a prospect. Maybe you don't want to spend as much time on leads because you know that if you convert that lead, that lead is going to go through an internal process that will clean it up, that will help you match it. So it just may not be worth your time to try to clean up the lead before it becomes a customer. George L'Heureux: That's a great point, that your process is going to be dependent on your particular situation within the company and what other resources you have at your disposal. Liz Walters: Yes. George L'Heureux: So how can Dun & Bradstreet help, as we wrap up here, Liz? You're doing this every day. Customers are listening to this or watching this. How can you help them to really get the most out of their match? Liz Walters: Make sure that you're giving us as much information as you can that helps us with the match. And also those value indicators of things that will help us understand how important that record is to you. That can help us convey back to you how much effort you want to spend into resolving the other records. Things like age of the account, total revenue size. If you have an eternal anticipated future value metric. Things like that won't help us resolve the match, but can help us stratify the results for you so that you know where the customer wants to hone in and spend their time on the match file. George L'Heureux: And that all comes back to that whole cost versus benefit calculation, making sure you're spending time on things that are actually going to be valuable for you. Liz Walters: Exactly. George L'Heureux: Yeah. Well, thanks a lot, Liz. I really appreciate you taking some time today to sit down and share your expertise on this topic with me and everyone who's watching or listening. Liz Walters: You are very welcome. Thank you. George L'Heureux: Our guest expert today has been Liz Walters. She's a data advisor at Dun & Bradstreet, and this has been Data Talks. I really hope that you've enjoyed today's discussion. And if you have, I encourage you to please share it. Let a friend or colleague know about the show. And for more information about what we discussed on today's episode, please visit www.dnb.com or reach out to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L'Heureux: Hello, everyone. This is Data Talks, presented by Dun & Bradstreet. I'm your host George L'Heureux. I'm a Principal Consultant here on the Advisory Services Team within Dun & Bradstreet. In advisory services, our team is dedicated to helping our clients maximize the value of the relationship with D&B through expert advice and consultation. On Data Talks, I'm going to chat every episode with one of the expert advisors at D&B about a topic that can help consumers of our data and services to get more value. And today's guest expert is Tim Petre. Tim is a Data Strategy Consultant at D&B. Tim, how long have you been with Dun & Bradstreet? Tim Petre: 43 years. George L'Heureux: My goodness. Tell me a little bit about what you do in your current role as a data strategy consultant. Tim Petre: Okay. Basically, I'm in the financial services vertical, specifically with insurance. I work directly with our sales representatives and directly with the customers, understanding what their use cases are and supporting them through education, working through specifically match files, hierarchies, just explaining our data and how they best can use it to support their business. George L'Heureux: And how did you come into this role at D&B over the course of your career within the company? What were the steps that kind of led you to this role over time? Tim Petre: I've been doing what I've been doing for about 10 years now. And most recently, this last seven has been specifically with this group. I started out as a DUNSRight™ consultant explaining our data and DUNSRight process, which is the patented process from our global data collection all the way through analytics. So I've been pretty much in this role, like I said, for the last 10 years. George L'Heureux: So you and I had wanted to chat a little bit today about matching, which really is one of the core competencies that we have at Dun & Bradstreet. So, let's start with the basics. What exactly is matching? Tim Petre: Matching basically is what we call our identity resolution. The matching process is patented with Dun & Bradstreet, basically what it does, it takes our customers data and matches it to the best record that we have in our file. And it's based basically on what the customer is providing us as data input. And then based on that, we'll compare it to our match reference file and provide them back either a single record that matches best to what they provided or to a candidate list where they can choose from. George L'Heureux: And those candidate lists or that best record, that includes the D-U-N-S Number, which really is kind of the key to all the information that's inside of the Dun & Bradstreet Data Cloud. Right? Tim Petre: That is correct. Yes. George L'Heureux: So in a way, matching is kind of how it all begins for most of our customers. And I would imagine that accuracy with that match is really going to be critical. Tim Petre: It's absolutely critical. Basically, the customers coming to us and depending on what their use case is, whether it's for marketing or for trade credit, they have specific reasons why they want to have that record matched to a D-U-N-S Number. And it's just not providing a D-U-N-S Number. It's providing all the other ancillary information that we provide back with that. So, it's public filings on the business. Is there trade references, how they're paying their bills, who are the owners of the business, what are their operations? So it's all that additional information that we're able to provide through matching. And we have to ensure that we're providing back the record they're specifically looking for. George L'Heureux: With that being the case, with our customers relying on us, to help them understand that connection between their data and our data, how can we, and how do we make our customers rather feel comfortable that those matches are accurate? Tim Petre: Once we match a record, we provide back additional information on that record. So we provide back the confidence code is how confident are we in that match. It goes from a zero, which we don't have anything in our file that comes close to even matching to their input data all the way up to a 10, which is saying that this is definitely the record that you want. So we would write a confidence code. We provide Match Grade strings, which explains their input data compared to our reference data. How do they compare to each other? Are they the same? Are they similar? Do they match at all? Or you didn't provide the information, but we have it or vice versa. Tim Petre: And then finally, we had provide back when we call them match data profile codes. And that explains, for example, you provided ABC company. We gave you back as the Dun & Bradstreet record XYZ company, and explains saying that that's a match for example. You provided us for example, a trade style, and we're providing you back the primary business name with the legal business name on the record. So it's giving additional information on why we made the match we've made. George L'Heureux: And I have to imagine that if we're going to ask our customers to use, not only these matches that we give them, but also the tools that we give them for evaluating the matches that they get back. This is something that we've tested a lot. And I know we drink our own champagne to use a phrase by using this internally. Can you explain a little bit about how we use that same process internally to improve our data cloud? Tim Petre: Correct. Okay. Yes, the exact same matching logic that we use for our customers we use internally. So for example, if we have bankruptcy information, we have to ensure that we're putting that information on the right record. So we're actually using our own tools. Our confidence codes are match grade strings, our MDP codes, to ensure that we're identifying the right record, that we're applying, not only bankruptcies, but suits, liens, judgments against the business, any type of derogatory information, we're ensuring that we're providing an on the right D-U-N-S Number and the right record within our file. George L'Heureux: So you kind of talked about it a little bit when you were discussing those Match Grade strings, and that ability to not quite get an exact match. So this system of matching that we have at Dun & Bradstreet, I know it allows for some degree of uncertainty, right? Can you explain how that fuzzy matching works? Tim Petre: Yes. So what we do within our match grade strings, is we have values that we assign to the comparison of the input data to what's in our match reference file. And we have the ability to say whether we think it's an exact match, or if it's a similar match, for example. So if you came in as ABC company and you only want it, and we were only able to provide ABC company back to you, that would be a very limited type of match. We do standardization normalization to our files. And what we do is not only do we do it to our own internal file to add information, to ensure that it's appended to the right D-U-N-S Number, but we also do that same logic to the customer's input file. So we're comparing apples to apples. George L'Heureux: So, I think that that's a really important point, right? I mean, if we have something in our own file and, to use your example, it's ABC corporation. If we do something like we take out the periods and the extra spaces and the extra hyphens, and commas, and things like that in the names that we're storing in our database, we're applying those same types of transformations to the data that our customers provide us and ask us to match on. Tim Petre: Right. That's absolutely correct. Yes. We want them to ensure that we're doing the same standardization normalization process to the both sides of the equation, to the input record and to what we have in our file. George L'Heureux: So with the size of our reference file, what are we doing to make sure that we're staying up to date that our data are staying right, and that our customers are always going to be able to match against updated data? We have lots of processes in place to make sure that that stays good. Are you able to talk about that just a little bit? Tim Petre: Yeah. I mean, that's outside of the matching, that's more of our global data collection, but yes, we have over 30,000+ sources of data that we use. That's constantly being updated. That information, like I said, we use the same type of matching logic to take that information and ensure that it's matched to the correct record within our database. That's constantly refreshing our businesses. We have over 5 million updates that we do a day to our global database of over 400 million records. George L'Heureux: So, that's a little bit about what we're doing to make sure that we're staying good on our side. But as you mentioned before, this is kind of a two way street, and it depends on what the input data from the customer looks like too. What can customers do to make sure that they're getting the best results they can from a matching exercise with Ben and Bradstreet? Tim Petre: That's an excellent question. Basically, what we request from the customer is to provide a, a temporary file that we're able to take a look at and make recommendations up front, that we have the ability to identify areas of opportunity within the file. For example, do they have test records that they're providing, which they really shouldn't be as part of our normal processing with, of the match file? How do they look? Do they have any special characters? Are they providing the actual business name? Or are they abbreviating? We would request that they spell out the business name, the full business name, are they providing addresses, or they're providing the country. If it's a global record information like that, we're able to help them understand how they can improve the quality of the data. George L'Heureux: And you and I have even talked, and something as simple as including a phone number can improve the match results, improve the quality of the match results by a significant amount. Tim Petre: Yeah. Not only a phone number that can actually raise the potential of a match anywhere from five to 10% in some cases. But we also ask that once we do our match, many times, the customer thinks this is the best file that they have. And there could be something like the actual address is a remittance address as opposed to the physical address of the business. So within the company, that different areas within a company they may have where they do have multiple files, one that would potentially have the business name with the physical address. One with the remittance address, we asked them to provide both basically. So we can do a test and learn to understand which file is the better one to use for the overall match. George L'Heureux: It sounds like there's always something else that we can do to try and gain a little bit more optimization out of that match process. But Tim, as we wrap up today, what would be one thing that you'd want listeners or people viewing this today to walk away from this discussion about identity resolution and match? Tim Petre: Well, match starts everything. If you've got to make sure that we're providing the customer with the best record that we have, and it's working the customers to ensure that they're providing the highest quality input data, because that'll ensure that we're providing the correct record that they're looking on in the backend. George L'Heureux: Well, Tim, Hey, I really appreciate you taking time today to meet with me, talk a little bit about the match process, how D&B helps it, how our customers can make changes to improve things themselves and really sharing your experience of over four decades at Dun & Bradstreet with everyone watching, listening today. Tim Petre: Thank you for having me. George L'Heureux: Our guest expert today, again has been Tim Petre, a data strategy consultant here at Dun & Bradstreet. And this has been data talks. We hope you've enjoyed today's episode. If you have, we encourage you to please share it with friends or colleagues, let them know about the show. And for more information about what we've discussed on today's episode, visit www.dnb.com or talk to your company's D&B specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L'Heureux: Hello everyone, this is Data Talks presented by Dun & Bradstreet. I'm your host, George L'Heureux, I'm a Principal Consultant for Data Strategy in the advisory services team here at Dun & Bradstreet. In advisory services, our team is dedicated to helping our clients to maximize the value of the relationship with Dun & Bradstreet through expert advice and consultation. On Data Talks, I chat each episode with one of the expert advisors at Dun & Bradstreet about a topic that consumers of our data and services could use to get more value. Today's guest expert is Dan Wadding. Dan is a data advisor in the data advisory services team. And he's been in this role for eight years. Dan, tell me a little bit what you do as a data advisor. Dan Wadding: Well, first of all, thanks for having me George, I appreciate it. So as a data adviser one of my objectives is, I manage a portfolio of accounts. Right now I'm a managing a portfolio in the financial services' industry, as well as the insurance industry. And really, I act as the subject matter expert and I work holistically with our technical advisory and analytical solutions, and importantly I help a lot of our customers optimize their match results and make sure they're getting the true value of the implementation of their solutions. George L'Heureux: So, how did you end up in this role? Was this something that when you were eight years old, you said to yourself, "This is what I want to do when I grow up." Dan Wadding: I was on as strategic assignment for Dun & Bradstreet, probably about 12 plus years ago. And I saw an opportunity to work on a high-tech firm where it actually had five dedicated data advisers on it. And I just thought, wow, this is the biggest customer at Dun & Bradstreet, I could make an impact on this, I could help engage with them, show them the solutions and optimize the benefits and the value of Dun & Bradstreet. So I actually, I left a corporate initiative to go for this, and I've been in this role ever since so 12 plus years ago. And we've evolved from... We were information consultants and we've evolved into this data advisory role under new leadership teams, where we partner more closely with our analytical solutions on our technical advisory teams. George L'Heureux: Very cool. And as you and I were prepping for this, you had said that you wanted to make sure that you were giving voice to something that's really basic to us here at Dun & Bradstreet and to all of our customers. When I say basic, I don't mean that it's necessarily simple, but it's really fundamental to what we do and how we do it. And that's the D-U-N-S number. So let's start with that. Dan, what exactly is a D-U-N-S number? Dan Wadding: The D-U-N-S number, I mean, acronyms are acronyms when we try to eliminate acronyms in our vernacular, right? So D-U-N-S stands for The Data Universal Numbering System. So it's a nine digit number that Dun & Bradstreet assigns to every business location that we've identified that is economically active. Their doors are open, they're conducting business. It's unique, it's persistent, it's been around for a while. It was actually developed in 1963, as Dun & Bradstreet was launching into the digital age, leveraging computer systems, their databases. And the important part is that it's probably leveraged now by almost 90% of the fortune 500 companies. And it's really the universal standard for business identification. George L'Heureux: So when you say that these companies around the world, fortune 500 companies are using it, what exactly are they using it for? When you say business identification, what does that mean? Dan Wadding: They leverage it through our patented matching entity process. We can assign a D-U-N-S number. Let's just say we're bringing it in new to our global cloud, right? So if we don't have it, we're going to go through this patented matching technology. And we're going to assign a D-U-N-S number to a business entity. This D-U-N-S number is going to interact within their organization of themselves, but other customers are going to leverage that D-U-N-S number of how they can interact with that D-U-N-S number in other organizations. And what I mean by that is they can tie family trees together. They can do all kinds of great things because it is so unique. It's just not some random D-U-N-S numbering system. It's not an AGN, it's not an alternative key. It's truly a unique universal system. And it's has eight key attributes to it. I mean, it's global, it's unique, it's persistent, it's constant, it's non indicative is probably the most important part, universal hierarchal and foundational. Just want to touch upon each of those real quick. From a global perspective, it's assigned to businesses and more than 220 countries and territories. So our data cloud covers that massive scope of I'll call it the business landscape around the world. It's unique so it's assigned to business locations and they have a unique, separate and distinct business operations. So if you were a branch and you are at a headquarters' location, each of those entities are going to receive a D-U-N-S number. And I talked about those orgs and we can go into that a little bit deeper, but it's persistent. So it stays with that business. It's never reused, never reassigned. It stays with that business and I hate to use this term, but from cradle to grave, right? Once we find them and they emerge and we're going to stay with that business until they cease business operations or they just fade off and we did not pick up any more signal data on them. It's not indicative. A lot of people believe that those nine digits mean something. They don't those nine digits don't tell any aspect of the company or to which it's assigned or any other bearing of that business entity, nine digits or nine digits, a D-U-N-S numbers a D-U-N-S number is what we always tell. It's universal so it's really relied on businesses around the world, as well as various governmental agencies that require anyone that they're doing business with to come to them with a D-U-N-S number, or they come to them on a Bradstreet, with their data files to obtain a D-U-N-S number. I touched upon this and its hierarchical in nature. And what I mean by that is it reveals relationships about how a specific organization is linked into another one of family trees worldwide, right? So you could look at a site D-U-N-S number, it rolls up to a headquarter parent D-U-N-S number. They roll up to a domestic ultimate D-U-N-S number and ultimately it rolls up to a global ultimate that ties in that locate those how one site tied into all of those other entities, respectively from a hierarchal standpoint. And then the last key portion of the D-U-N-S number is its foundational. As a lot of entities are moving into the master data realm, it's part of their master data strategy to harmonize their data across the enterprise. So in my dealings with companies, as they implement their master data strategies with Dun & Bradstreet, some will use it as a dual key. Some will use it as the primary key. So it depends on the structure of that business entity and how they view if they have internal keys that are necessary to keep their business alive and well. George L'Heureux: Thanks for going through all that. I want to key in on something because as you went through that list and before you help elaborate on each of them, you called out non indicative and you said that might be the most important one. Can you tell me why you see that as maybe one of the most important of those attributes about the D-U-N-S number? Dan Wadding: It's unique to Dun & Bradstreet and I'll get into the weeds a little bit. Years ago before we changed, when we evolve the D-U-N-S number, there used to be systematic checks in a D-U-N-S number. And this is before the business population really exploded. So we had a finite set of D-U-N-S numbers available in the Dun & Bradstreet D-U-N-S number pool. And we used to leverage what was called a mod 10 check, and what it would do. It would run a mathematical equation and then it would evolve and check that ninth digit and tell you it was a valid D-U-N-S number. As we began to run out of that pool, we then begun to implement a D-U-N-S number of mod 10 plus five factor with more mathematical equations to it as well, business population again booms and explodes. So we've taken those equations away from the general public and stop touting them if you will. And we just tout that the business, the nine digits are truly not indicative. There's no bearing that if it's a business that's worthy of credit extensions, that's worthy of being a supplier, that they're active or inactive. It's really the key to getting into – what I call - I call it “all the goodness” that Dun & Bradstreet can provide. And that's whether you need credit information, you need supply comply information or basic marketing information, right? Once you have that D-U-N-S number, you can tie in correlate that into all of the solution sets that Dun & Bradstreet offers. George L'Heureux: So I guess the key thing about it being non indicative then is that it doesn't tell me anything about the business, just the number itself, but that allows for things to change, and for us to be able to reflect that. And if I own George's pizza that D-U-N-S number is going to stay the same, even as my credit worthiness, even as my phone number is even as the ownership changes over time. Right? Dan Wadding: Exactly. So if you go through a business name change, let's say you move your address. Let's say George retires. And you bring in a new CEO, or let's say you change, you evolve into new lines of business. The dynamics of that business operation will not have a bearing on your D-U-N-S number. And I'm going to caveat that. And my caveat is, if you, let's just say, if ABC company merges with XYZ, when we announced the merger on day one, let's say on day 20, the merger's complete. And we begin to look at those family trees and who's going to survive. And who's going where there are certain times when D-U-N-S numbers will not get changed, but they’ll be put the rest if you will. Let's just say, a branch moves into another they're consolidating locations. We will begin to consolidate those D-U-N-S numbers. One will survive, but we have a behind the scenes audit trails that help all of our customers that if they have that D-U-N-S number of ABC, and let's say it's consolidated into XYZ due to that merger acquisition, we have audit trails that we offer to our customer, that we can point them of the D-U-N-S number of company A to company X, Y, Z. George L'Heureux: Yeah. And I know that what we call re-certification is something that one of our other colleagues is looking to speak with us on a future episode of this and so it just shows how it all ties together and how critical the D-U-N-S number really is to understanding businesses. How many of them, you talked about the explosive growth before, how many exist today? Dan Wadding: So within the Dun & Bradstreet Data Cloud, there are over last count, over 420 million. And that is constantly evolving as different markets start to explode in leveraging different types of data sources and data signals. So sky's the limit on this. We've grown substantially over the past five to 10 years. I remember in my younger years at Dun & Bradstreet, we had a big celebration over hitting a hundred million D-U-N-S numbers. I was a former global D-U-N-S number administrator at Dun & Bradstreet earlier in my career. And it was interesting to watch the evolution of this just in the world, just growing so much in the D-U-N-S number, staying there as that persistent key. And an interesting note, I will tell you. We've done studies with customers to see if we could start providing alternative keys or different numbers. And we did focus groups with them and we looked at, and we would show them side by side, the values of each and the alternative key versus D-U-N-S number. And 100% of the customers said, do not provide us with an alternative key. The value of the D-U-N-S number drives everything home, whether we're dealing with our customers, our suppliers, our potential market prospects in our own internal systems. George L'Heureux: I mean, that's the kind of thing that you want to hear – the value it gives. Dan Wadding: Yeah, absolutely. George L'Heureux: Briefly you alluded to it earlier. Could you talk about how D-U-N-S number gets assigned? We've seen this explosive growth in the number of D-U-N-S numbers. And so I think that could beg the question, is it because we're changing the way that we assign D-U-N-S numbers or is it just because the business landscape has grown so much over the course of time? Dan Wadding: Yeah. Not changing the way we're assigning D-U-N-S numbers for sure. But it is the explosive growth in the global landscape. Particularly if we look at the growth over the past, say 12 months, China has exploded incredibly by millions of additional new sources. So traditionally they didn't have sources available to them. Now they're bringing in new sources, we leverage our patented matching technology to see if when you have those entities, you always got to double check because the last thing you want to do is put duplicates in your system, right? So we always use our leverage our matching technology which is part of the DUNSRight process to see if we have them that entity in the cloud. And if not, we'll assign one and add it to that business entity. And now they're resident to our Dun & Bradstreet data cloud. And we begin the DUNSRight process there. And we begin to track that evolution of a business. We might've picked them up as they're an emerging business. And we begin to monitor all those various data signals as they evolve all through their life cycle. Some may quickly come and go and some may be there for quite some time. Dun & Bradstreet's an example of being around for over 175 plus years. George L'Heureux: So is there a way for a company that's just, just emerging, they're starting to do business. Is there a way for a company to raise his hand and say hey, Dun & Bradstreet here I am start tracking me. I mean, I know that there's plenty of places that require D-U-N-S number to do business with them. Dan Wadding: Yeah, absolutely. And that's a great question. We do have specific teams set up where customers can call in or go through the internet and request the D-U-N-S number on their own business entity. And it's a fairly simple, straightforward process. We're going to add, we're going to ask questions, we're going to do a few checks and we're going to go through our process to make sure that it's in the cloud currently, right? And then we're going to go through the evolution of leveraging our DUNSRight process to monitor and always keep an eye on that entity, that up more signal data so that the cloud always has the most current and present view of that business entity. George L'Heureux: Dan, you talked about wanting to give the D-U-N-S number a voice. And I think that you've done that. If there's people watching or listening and you want to give them just one thing to walk away from this conversation with what might that be? Dan Wadding: The D-U-N-S number is the key to all, as I mentioned, the key to the Dun & Bradstreet Data Cloud and that Data Cloud can serve customers of many use cases, leverage the D-U-N-S number and its goodness, whether you're building out your own MDM as a unique persistent key, leverage it to obtain information on other companies, leverage it to be able to walk a hierarchal view of family trees. It is one of the most unique identifiers in the business world. I think that people have seen in a long time and it's been here for a long time. George L'Heureux: So the only last thing I'm going to say to you Dan, is I think that you should look into trademarking Dun & Bradstreet goodness. I think that has the real opportunity to catch up. Dan Wadding: Thanks. George L'Heureux: All right thank you. Dan. Our guest expert today has been Dan Wadding, a data advisory at Dun & Bradstreet. And this has been Data Talks. We hope you've enjoyed today's discussion. And if you have, we encourage you to please share it with a friend or a colleague. And if you'd like more information about what we discussed on today's episode, we encourage you to visit www.dnb.com or to talk to your company's Dun & Bradstreet specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L'Heureux: Hello everyone. This is Data Talks, presented by Dun & Bradstreet. I'm your host, George L'Heureux. I'm a Principal Consultant for Data Strategy here in the Advisory Services Team at Dun & Bradstreet. Here at Advisory Services, our team is dedicated to helping our clients maximize the value of that relationship they have with Dun & Bradstreet, through expert advice and consultation. On Data Talks, I chat every episode with one of the expert advisors at D&B about a topic that can help consumers of our data and our services to get more value. Today's guest expert is Mary Hagemes. Mary is a Data Adviser at D&B. Mary, how long have you been here at D&B. Mary Hagemes: I've been with D&B for the last 23 years in various roles throughout the company. George L'Heureux: And can you tell us a little bit about what you do in your current role as a Data Advisor? Mary Hagemes: Sure. I work specifically with our strategic customers in the insurance vertical to help them ingest our data, understand it, consume it, and use it to make a better decisions moving forward. George L'Heureux: And before that at D&B or other positions, what led you into the role that you're in right now? Mary Hagemes: Well, I've worked with some of our small customers, all the way up to our Fortune 500 customers. Working with them on mitigating risks within their accounts receivable portfolios, streamlining workflows, working with them on using data to improve their sales and marketing strategy plans, as well as in the supplier space, working with critical suppliers, understanding their risk and also spend analysis to create efficiencies within their vendor portfolios. George L'Heureux: Great. And as you and I were getting set to chat today, one of the things that you brought up as being really important that you felt like we wanted to talk about, is the idea of the importance of a name. What's in a name? And I guess that is going to be applicable to clients across all those different verticals. Why is understanding how businesses are named so very important? Mary Hagemes: Well, it's important to understand that a business can conduct business under multiple names. So, the name on the sign of the door of the front of the business isn't necessarily the business's legal name. So it's important to understand which name you're working with based on what use cases you would like to use that name and that you aggregate it all under the same record. George L'Heureux: So before we go any further, what is the legal business name? What does that mean exactly? Mary Hagemes: Sure. The legal business name, there's only going to be one legal business name and that is registered within the local country or within the United States, it would be registered with the Secretary of State, what their legal operating business name is. And then we also have trade styles we're doing business as, which a company can use multiple trade styles to do business, and that's one that's usually more recognizable. It's the one that the public would be familiar with for that company. George L'Heureux: So that trade style, to use the term you used a minute ago, that's more the name you're going to see on a sign on the street? Mary Hagemes: That is correct. If you're walking up to the business, the front door, that would be the sign on the door or the more familiar, the recognizable name of that company. George L'Heureux: Okay. So why would a business do that? Why, if you're going to name yourself one thing as your legal business name, why wouldn't you just use that as the name that you use on the door? Mary Hagemes: Well, there's multiple reasons for that. Some people like to register their company name in their name. And so therefore, that name isn't very flashy or maybe attracting as much business, so they would use a name that would help them drive business to their storefront. And there's also other reasons. There could be a management company or companies that own several different types of restaurants, for example, where you can have a company that owns a steak house, a seafood house, an Italian restaurant, and they would want to name those companies appropriately for that business so they can make the public aware of what they do and drive business through that name. George L'Heureux: So I might be a restaurateur and my company name is L'Heureux's Restaurants Incorporated, but my steak restaurant might have George's Steaks and my seafood restaurant might be called L'Heureux Seafood. That's basically the idea? Mary Hagemes: That is correct, but it's still all owned and operated by the same company, which would have one legal business name and several trade styles. George L'Heureux: So inside of D&B, how do we keep track of all of that? If there are going to be one to 10, 20 different names for a business, how is it that we manage to make sure that that's all in the same place? Mary Hagemes: Well, when we aggregate the data and collect the data for the records, we put it under a unique identifier and not just the name. So our records can hold the legal name of the business along with multiple trade styles. So we're able to say this one record under this unique identifier is going to operate under these different business names. George L'Heureux: And that unique identifier is our D-U-N-S number. Mary Hagemes: That is correct, our D-U-N-S number. George L'Heureux: And so if we do all that work and we pull it together under the D-U-N-S number, what is that doing for our customers? How are they then able to take advantage of it? Mary Hagemes: Well, they're able to take all of the information that comes from each one of those records. So, in their systems, if they have the multiple names under different records, they would be able to aggregate all the information about those records into one complete view of the customer using Dun & Bradstreet. We can help them do that. Using that unique identifier, that D-U-N-S number, we can help them bring together those records so that when they're making business decisions on a company, they can make the appropriate business decision on all of the information they have on the company instead of just pieces. George L'Heureux: So you mentioned that this information is maybe in the Secretary of State's office or other places, what stops the company, why wouldn't a client of ours, just say, "You know what, I'm going to go find out all this information and I'm going to do this consolidation myself." Wouldn't that be possible for them? Or is it too challenging? What makes it so special, what Dun & Bradstreet is able to do? Mary Hagemes: Well, it is a huge challenge. Dun & Bradstreet has over 30,000 sources of data, where we're aggregating all of this data together. We're conducting many different ways of getting this data. So it's not just getting to the Secretary of State. Yes, we have that information and we will bring that in as a legal name, but we also have other sources we're using to pick up those trade styles from our intelligence engine and all those backend ways that we're doing it through multiple sources. And so, we're sourcing out of all those different sources to be able to aggregate all of that data together. It's a very large process for any one company to take on. George L'Heureux: So, you mentioned it a moment ago. I want to go back to it. You talked a little bit about duplicates and being able to pull what might otherwise be duplicates together under a single umbrella and be able to look at it holistically. What would some of the drawbacks be if you're not doing that? If you allow those duplicates to persist in your database? Mary Hagemes: Well, the biggest drawback is you're never going to get that 360 degree view of your customer, your prospects, your vendors, whoever you're trying to make a decision on. You're going to have disparate information spread throughout your organization. And you really would like to know all about that specific company when you're making a decision. So, you're missing out pieces of information to actually put a good decision forth for that specific plan that you're making, whether it's mitigating risk or sales and marketing plans or analyzing your supply chain. All of those things you want to take a whole look, a whole view at that record. George L'Heureux: When we have that whole view, we're able to look at those different names, we're able to say, "Okay, this is maybe the legal business name. This is the trade name. This is my view of it." But it's not just limited to names, right? I mean, we're talking about names here, but the same kind of thing goes for addresses or phone numbers or anything like that, right? Mary Hagemes: Oh, that is correct. With D-U-N-S, we write a physical address, but you can also have a mailing address, a PO box. We can also have multiple sources and multiple pieces of information within that record up until even understanding the family. It belongs to. If you want to understand who owners are, ownership is above and beyond just that specific company itself, that entity. So that D-U-N-S Number allows for you to aggregate all that information in and keep a whole view of that record. George L'Heureux: I mean, clearly this is something that you're dealing a lot with our clients on, and that we think a lot about internally, just as a team. How do we convey this? How do we talk about this? How do we express the importance of it? But actually doing things in data management can sometimes be challenging. What are the biggest challenges that you see when you're talking to customers about this, maybe in terms of them understanding it or doing something about it? Mary Hagemes: I mean, the biggest challenge I would face is really just not knowing sometimes that they have to look a little bit deeper on the name. They have to understand a little bit more how they're doing business, but once we open up and show them what we have, what a legal name is, what a trade style is, how they're operating, how it comes together, how all that information comes together. It's easy to realize how much value that brings to that company because they're able to make the decisions then on all the information that they have when they're able to see the full record. George L'Heureux: So, as we wrap up here today, Mary, what is one thing that you might want someone who's watching or listening to this today, whether they're a client or not, to be thinking about, to be taking away from this discussion about different types of business names and how they impact the way we look at companies? Mary Hagemes: I would say the one big takeaway I would want people to take from this conversation is that using a company name may not be the best way for identifying a record. Using the unique identifier, like a D-U-N-S Number to identify that record and get all of the names for that company, whether it's a legal name or it's a trade style, so that when you're doing business and you're making decisions, you can use the appropriate name for your use case, where if you want the more recognizable name, the trade style, you have the information to use that versus if you want to take a look deeper into a company, legally, you might want to use their legal name. So, having all of that for you under the unique identifier, you are able to use that record how you want to with your use cases. George L'Heureux: Fantastic. Mary, thanks so much for joining me and sharing your expertise on this topic with anyone who's listening or watching today. Mary Hagemes: Thank you. George L'Heureux: Our guest expert today has been Mary Hagemes. She's a Data Advisor here at Dun & Bradstreet, and this is Data Talks. We hope that you've enjoyed today's episode, today's discussion. And if you have, we encourage you to share it with your friends and colleagues, let them know about the show. And if you'd like more information about things that we've discussed today, please visit www.dnb.com or talk to your company's D&B specialist today. I'm George L'Heureux. Thanks for joining us. Until next time.
George L'Heureux: Welcome everyone to Data Talks. I'm George L'Heureux, your host. I'm a principal consultant for data strategy here at Dun & Bradstreet on the data advisory services team. We're looking forward to having these discussions and talking to some of the experts on our data strategy team as we go through the course of the year, and our first guest for this show is going to be Dave Stulack. Dave Stulack is a data strategy consultant on the data advisory services team here at D&B. Dave, tell us a little bit about what you do in your role. David Stulack: Sure. Thanks. Thanks for having me George. In my role, I help our customers maximize the Dun & Bradstreet data and also the other assets they may have within their organization and without. Basically, I kind of piece together all of those items that help a company be more productive or more profitable. George L'Heureux: What got you interested in this type of work? David Stulack: I've been with D&B my entire work career. So I guess all of my training has been leading up to this one moment where I've learned a lot about the D&B data. Over the last 10 years or so, I've learned a lot about customer data interacting with customers and then putting those two things together, I think I could really make an impact for our customers. George L'Heureux: So as you and I were preparing and talking about all the different things that could be important to our customers, you brought up the idea of the life cycle of a business. Tell me why is understanding the life cycle of the business so important? David Stulack: Yeah. It's really important because it will dictate how we interact with customer suppliers and prospects. It's going to be different depending on what stage of life they're in. We may work differently with a start-up than with an established multinational corporation or a company that's teetering on the brink of closure. Knowing where your customer or supplier is helps you make these sorts of decisions. George L'Heureux: So let's dive into that a little bit more. You kind of talked about it a little bit, but what are the different stages of a business's life cycle that we think about when we're talking about a lifecycle in general? David Stulack: Yeah, sure. We usually refer to the beginning as kind of the birth of the business or start-up, as you had mentioned. There's a time where a business is established going through the ups and downs with normal business on maybe not every year's a great year, some years are better than others, but in generally, it's kind of stable or growing. And then at some point, some businesses may experience a decline, which could lead to the death of a business where it actually stops doing business. George L'Heureux: I mean, what would cause that to happen? I mean, we've all seen it. We see stories about businesses that go out of business. We see bankruptcies, but I imagine that's not the only way that businesses end up dying as you put it. David Stulack: Yeah. There's a number of different ways businesses may stop operating. Owners could close up shop. One example may be, they've kind of been in business for many years. They just decided to retire and kind of move on and kind of enjoy their golden years. There's another sort of closure that's maybe less thought of as a closure, but when a business gets sold or merged into another business, that's what we kind of consider mergers and acquisitions. That's kind of where one business may cease to exist and another kind of continue on with them being absorbed. Then there's also the bankruptcy, which results in a restructuring or insolvency. On some occasions, a business may also be fraudulent, which they're really tough to detect, but we have a lot different signals we monitor to let our customers know if there is possible fraud. George L'Heureux: So it sounds like you were kind of starting to talk about this a little bit, but what are those types of signals? What tells us where a business is in that lifecycle? How do we know that it's just getting started versus maybe in that established and growing phase? Even more basically, how do we know that a business, say, ‘gets born’ to use your term? David Stulack: Yeah. Lots of times a business may be operating before taking the official steps. A couple of different examples might be Aunt M is kind of knitting scarves, maybe a very small business, maybe not even thought of as a business. She may not go through the official steps of establishing that. Then, there's other businesses where they start up, maybe not intending to continue on, but things become very successful. Then, they take the necessary legal steps to kind of bring that business into being. George L'Heureux: So that's being born. On the other end of it, you kind of started mentioning this too. What are those types of signals that we're looking for that wherever we get them, however we hear about them, that would tell us that a business is maybe close to ceasing its operations, or is in that period of decline that you talked about? David Stulack: Yes. So we at Dun & Bradstreet have a number of ways to kind of keep a pulse on the heartbeat of the business, as we might call it. Decline of business activity is a really good example, which may be a decrease in sales or reduction in number of employees. We also have many different newsworthy feeds that could announce a loss of a large customer or some kind of department from the federal government, or it could also be an unfortunate natural disaster. George L'Heureux: But we're not necessarily taking any one of those as being definitive proof of one thing or another. I imagine that's sort of a preponderance of the evidence, I guess you might say, to tell us where things are at and for us to sort of give our best guesses where a business is in its lifecycle, right? David Stulack: That's exactly right. And within our data supply chain, we have a number of different algorithms that kind of piece all these things together to let a customer know what's kind of the end result of these different events. And then also, I may be brought in as well to kind of use my expertise to kind of diagnose and kind of model, in some cases, what's most likely to occur on a customer's portfolio. George L'Heureux: So we go through all that there. You have these signals. Dun & Bradstreet, for example, can help to coalesce those signals into some sort of an alert or a trigger or some status maybe even. You might dig in and get a little bit more personal with the data and figure out what it says. So we know where they are at now in their life cycle, or we know where we think they're at. How does that help our clients? David Stulack: Yes. So it helps the customer and we give them the right information, it could be as simple as a score, or it could be some consulting services where there's more of a kind of two-way conversation, but it helps the company or customers ultimately take the right action depending on what's happening. If a customer is declining, they may be in danger of not getting paid right for goods and services. If they could take action sooner rather than later, they may have a better chance of recovering some of those accounts receivable. On the other side, if a business has been sold, the successor business could offer a great opportunity for that company. Maybe it's a small company that was acquired by a very large company. Now, you kind of a foot in the door for this very large company where it could lead to more opportunity. George L'Heureux: And I guess on the other side of that, if you don't have this type of information, if you're not privy to where we think a particular company is in its business lifecycle, I imagine that's probably putting your own business a little bit more at risk because you don't have the information that you need to make the best decisions that you could. David Stulack: Yeah, absolutely. On the risk side, as I mentioned, the accounts receivable balances may kind of go up, right. Usually companies have kind of a limit with most of their customers, but in tough times, they may kind of allow those limits to be surpassed, especially in the environment we're in now. But companies may waste a lot of time and resource trying to go after that money when quite frankly, there could be no hope, that the company may have already closed up. So it's very important to know where they're at in that cycle or how close they may be to closing. On the other side, a sales and marketing type use case, you may be trying to sell products and service to a company, spending a lot of time and resources sending sales folks out when we're able to do so. And basically, quite frankly, they're unable to even buy those goods and services. And you may also miss out on opportunities where a company is entering some kind of growth phase where you want to kind of get in early, so when they're looking to grow, you could offer your goods and services and kind of grow along with them. That's kind of the dream. Basically, it comes down to using your resources more efficiently to reduce costs and maximize opportunities. George L'Heureux: Right. I mean, you said it. You took the words out of my mouth. It comes down to this cost versus benefit balance that you have to do, and the more information that our clients have, the better they're going to be able to weigh the potential benefit against the costs that they're going to have to put in to trying to get that done. Right? David Stulack: Right. George L'Heureux: So we talked about it in kind of general terms. We touched on a little bit about what D&B can do in terms of the data assets that we have and some of the modeling and the consulting work that we can do. Can you dive into that a little bit deeper for me, how we can help our clients? David Stulack: Yeah, sure. So we refer to generically all the things that we kind of gather, all the different pieces of the puzzle as signals and the signals that we typically really keep a close eye on are public filings, such as suits, liens, and judgements, velocity of trade, which is basically accounts receivable. We take all those pieces in and we model that information. We could deliver something as easy as a numeric score, kind of on a scale from zero to five, one to 10, that can indicate growth decline or inability to pay bills. That really helps our customers kind of see a trend if they're kind of trending up or trending down, might give them a tip as to where the company is heading. We also ingest thousands of newsworthy sources and those newsworthy sources kind of point to a merger and acquisition, layoffs, and we also attract a lot of address changes. A company could be moving, and the reason they may be moving is either to go to a smaller or larger location, that could indicate some kind of increase or decrease in viability and even knowing the business industry could help apply macro trends. George L'Heureux: So that's really interesting and kind of specific, but really interesting the type of data that D&B can provide to help here is you might even be looking at a company that's moving from a 40,000 square foot facility to a 20,000 square foot facility. And you could indicate that that's a signal of where they're at in their business life cycle potentially. David Stulack: Right. And they may also be purchasing another location. So maybe they're growing and now they just need additional space. So we could actually add another address or a site and make that connection. George L'Heureux: There's a ton that sounds like it's here and a ton of different ways in which you and the others on our team are able to help our clients. If there was one thing that you want your clients and anyone who's watching or listening to this to kind of come away from this discussion with, what would that one thing be? David Stulack: Yeah. There's so many different pieces, but I think I would boil it down to, you need to know where your customer supplier is in their life cycle. That's really going to help you deploy your resources, and what we kind of talked about throughout this whole interview or conversation is it'll help you be more effective and perhaps more profitable. George L'Heureux: Well, thanks, David Stulack, for joining me today on Data Talks and sharing what you have to know about the lifecycle of business with all of our viewers, all of our listeners and sharing your expertise over all these years. David Stulack: Thanks, George. Thanks for having me. George L'Heureux: Hopefully you enjoyed this episode of Data Talks. I'm your host, George L'Heureux, a principal consultant for data strategy here at Dun & Bradstreet. We're going to have more of these coming throughout the course of the year, and watch and listen to each episode as it comes out. If you're looking for more information on how Dun & Bradstreet can help your business to succeed more at what it's trying to do in terms of its business goals, we encourage you to visit www.dnb.com or to reach out to your sales associate for more information. Thank you. Look forward to seeing you next time.
