for Your Company
Data-First Modernization Strategy
How to Drive a
Start by identifying the real desired business outcomes— with or without data. Apply agile principles to reorient your data and analytics strategies. Tackle culture, appoint a visionary, and get ready to hit the gas.
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PART 2
PART 1
INTRO
COVER
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By Beth Stackpole
Case in point: In a recent survey by IDG on behalf of HPE, 49% of the respondents described their organization as “strategic about how data is collected, made available, and used to deliver proactive insights that derive value.” But if you move beyond the survey results, the details are a little cloudier. The truth is, many CIOs and business leaders can’t answer the following five questions with assurance, details, or hard numbers—casting shade on their company’s ability to maximize, even monetize, data’s true value:
Have you identified real, lucrative uses for your data that will drive new revenue and profits? Do you have a process in place for scaling new ideas related to data? Do you have a strategy for operationalizing your data? Is there a sense of urgency among your CEO/C-suite to do something about this in the next 12 months? Is there someone in your organization who is responsible for building and expanding a strategy for capitalizing on data?
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If you ask most CIOs if they have a data and analytics strategy, they’ll say yes.
If you don’t have a detailed answer to these five questions, you may be focusing your attention on the wrong things or applying your resources in the wrong places. Here’s why you need to look at things a different way … along with a road map for how to reorient culture, technology, and data management processes to deliver a real bottom-line return on your data. //
Beth Stackpole is a veteran reporter who has covered the intersection of business and technology issues for more than 20 years.
Content strategist: Barbara Call | Art Director: April Montgomery Illustrator: Simoul Alva / Levy Creative
The data-first modernization challenge
Capitalizing on data’s real value
The
Data-First Modernization Challenge
The list of challenges surrounding how to modernize your business and IT to enable monetization of data is long, and understandably so. Many of the issues are long-standing, complicated, and interconnected. For starters, although data is often touted as a key resource, it’s often not treated in the same way as traditional enterprise assets such as capital equipment or physical buildings. According to the NewVantage Partners “Big Data and AI Executive Survey 2021,” only 39% of the responding companies are managing data as a business asset.
Second, many companies devote the bulk of their time and resources to data collection, data preparation, and data management. They focus on technology and tactics at the expense of establishing a clear picture of strategic objectives, potential use cases, and quantifying the value that data can bring.
“Most companies are generating real business value from data; it’s just not directly attributed to data,” contends Doug Laney, a data and analytics innovation fellow at West Monroe and author of the book Infonomics. Laney says that although other enterprise assets are measured based on factors such as quality characteristics, revenue contribution, or ability to be utilized, data metrics are currently mostly restricted to attributes such as volume and velocity.
— DOUG LANEY, author of Infonomics, data and analytics innovation fellow, West Monroe
We’re using data and analytics, but we’re not connecting the dots between the efforts and the business value. It’s a problem of attribution.
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But it can take months or even years to build out data repositories flush with data culled from enterprise systems or collected at the edge. Managing this potential treasure trove is a drain on IT time and resources. Perhaps most importantly, the effort may be for naught in the end, as huge swaths of data often aren’t necessary for driving insights relative to stated business goals. In fact, IT decision makers responding to the IDG survey believe that their companies leverage only about half (49%) of data sets to derive direct business value and 34% said they are falling short of their strategic data goals.
To this point, some organizations remain stuck on the volume of data—they channel efforts to accumulate data; ingest it into data lakes; and tend to basic data management tasks such as data structure development, cleansing and quality assurance, and data recovery and backup.
34% of IT decisions makers responding to an IDG survey believe that their companies are falling short of their strategic goals.
34%
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Roadblocks to Data Value
A company’s people and culture can end up blocking effective and profitable data initiatives. It takes dedicated, ongoing time and resources to change behavior in response to what data reveals. At the same time, organizations may rely on input from a small group of data scientists and analytics experts versus tapping business users who know what questions need answers or what insights are most useful. CIOs, in concert with business leaders and newly established data executive roles, need to encourage collaboration and cross-enterprise partnerships in order to break down organizational silos and foster the out-of-the-box thinking that unearths innovative uses for data.
Data governance, including issues related to security and regulatory compliance, as well as accumulated legacy debt, may also complicate the ability to cash in on data value. Without a modern infrastructure and data-first business processes, organizations aren’t nimble enough to outpace competitors or drive additional revenue growth through new business and operating models.
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But overall, perhaps the biggest challenge to successfully monetizing data is the lack of centralized or coordinated data strategy. Many companies don’t even have a decentralized vision that ties back to defined use cases. Without clear objectives and a strategy for how and where data can be leveraged, along with a visionary to advance strategy, organizations run the risk of siloed initiatives, nonstrategic projects, or ideas that can’t be scaled effectively. //
Companies that lack executive-level understanding of how data and analytics can deliver are often the ones struggling the most to realize value and advance intended business outcomes. “If it’s not an enterprise priority, companies don’t see real business value,” says Matt Maccaux, global field CTO for Ezmeral Software at HPE. “If the CEO, COO, or CFO says we’re a data-driven company and we’re going to make money on data, that’s when you see the change.”
— MATT MACCAUX, global field CTO, Ezmeral Software, HPE
If the CEO, COO, or CFO says we’re a data-driven company and we’re going to make money on data, that’s when you see the change.
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The benefits are real. Digital enterprises that can capitalize on data at scale can:
Capitalizing on
Data’s Real Value
Create new products and services in a decentralized world—digital, disruptive, innovative, at the edge, or all of the above
Transform data into insights to develop more-informed decision-making
Digitize the physical world to gain insights for optimizing operations and creating new efficiencies
Deliver new and differentiated customer, employee, and/or partner experiences Gain a competitive edge, capture new market share, and/or drive revenue growth
Modernize infrastructure—move to the right mix of hybrid cloud to optimize cost, performance, and agility
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Such benefits are simply not possible, at scale, if companies don’t reevaluate their edge-to-cloud strategy to accelerate and optimize data ROI. This involves determining and building the right data architecture and infrastructure, from the data center to the edge, as well as deploying the right data platforms and enlisting the right talent and executive leadership.
At UPS, data and analytics are already fuel for generating significant business value, according to Ken Finnerty, president of information technology. Internal and external data is being tapped to predict shipment volume and optimize delivery routes, and historical data, combined with dynamic external data and optimized routes, helps UPS generate accurate delivery windows, keeping customers apprised of shipment status. “By optimizing delivery routes, we are avoiding millions of miles annually—reducing both operating costs and carbon footprint,” Finnerty says.
— KEN FINNERTY, president of information technology, UPS
By optimizing delivery routes, we are avoiding millions of miles annually— reducing both operating costs and carbon footprint.
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Developing a road map to roi
A trusted partner with high-performance computing expertise
But putting all of this into action is much harder than listing requirements. Applying software development practices, specifically agile principles, can fill some of these gaps and provide a workable framework enabling IT and business leaders to reorient data and analytics strategies with an emphasis on measurable value. Adoption of agile thinking ensures that organizations can move away from multipage strategies and multiyear deployment to creating data factories that scale on a dime and develop data value quickly and effortlessly across the enterprise.
The IDG survey found that organizations know some of what they need in order to facilitate data ROI. Top-rated items on the list include:
Seamless integration of multiple data sources
Access to better analytics tools and services
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38%
61%
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Based on interviews with and input collected from more than 30 CIOs and CDOs, as well as industry experts and thought leaders, here’s a blueprint for getting started on this new approach … [click on any tab to learn more]
Accelerate ideas-to-market as part of a digital agenda.
Apply an agile methodology.
Explore design- thinking concepts.
Test and iterate
Break down silos.
Adopt a data-first culture.
Establish leadership.
Use a common language for data and analytics.
Foster a data- literate workforce.
Create data value at scale.
Facilitate data enablement.
Treat data like code.
Choose a data strategy.
Use automation wherever possible.
Develop metrics and KPIs.
Most organizations put too much focus on technology implementation. Instead, apply more focus on the up-front strategic work to define key business objectives and tie data and analytics initiatives to desired outcomes. It’s important to engage stakeholders throughout the business who understand the data, challenges, and opportunities; an essential first step is enlisting them to help identify use cases where data and analytics have high-value impact. At Kelly-Moore Paints, key users throughout the organization are regularly engaged to capture feedback and concerns as well as enlisting their input as action items as the firm evaluates vendor partners, according to Rebecca Meyer, commerce applications IT director. “We’re allowing them to help shape the strategy from the bottom up with executives driving it from the top down and we meet somewhere in the middle,” Meyer says. “Culturally, we’re bringing more people into the decision-making process, but we’re not tying up their schedules with the day-to-day aspects of the project.”
Once you’ve clearly defined your business goals, work to pattern your data and analytics initiatives after software development projects in order to quickly launch initiatives and capitalize on results. This means defining a process that includes gathering requirements, mapping data to meet clearly defined business goals, and investing in technologies and platforms that automate data operations to seamlessly scale pilot projects. Sunbelt Sunbelt Rentals has injected agility into data initiatives by intentionally designing their most critical projects to generate quick wins, establish credibility, and build momentum, according to JP Saini, Sunbelt’s chief digital & technology officer. “You have to have the mentality of entrepreneurism at scale, aligning your team on the vision, expected business outcomes, and with clear accountability. The key is getting your teams to have skin in the game upfront, otherwise the whole entrepreneurism thing fails,” he explains. “We make sure our success criteria is right-sized from the get go … which has enabled us to move more quickly and achieve more impactful business outcomes.”
Exploring design-thinking concepts to keep data efforts tailored to the business users they are meant to serve while establishing new roles to translate business requirements into action will help decrease overall time-to-delivery of new projects. Start with targeted use cases, and showcase success. Championing small wins is a critical piece of a wider campaign to garner enterprise support for data initiatives, which, in turn, promotes scalability.
Explore design-thinking concepts.
In addition to streamlining project cycles, an agile development approach frees you and others to concentrate on high-value tasks such as identifying, testing, and iterating on ways data can be transformative. That way, all hands are on deck advancing your enterprise digital agenda versus bogged down in routine and manual data management and operations work.
Test and iterate.
The right technology platform is crucial to success, but building and nurturing a data-driven culture is where the rubber really meets the road. This entails moving beyond IT-driven initiatives to reorient the entire enterprise toward data and analytics. Research shows there is still a long way to go. According to the NewVantage survey, less than a quarter (24%) of the respondents have forged a data culture or consider themselves a data-driven organization.
Changing culture and breaking down both organizational and information silos is central to this transformation. It starts with enlisting support at the highest levels of the company to establish data’s impact on meeting critical business goals. UPS, for example, is working hard to democratize use of analytics so the entire organization can benefit and put insights into action. The company has established a training curriculum to nurture data fluency skills and has created an enterprise data office (EDO) within its corporate strategy organization to ensure that the entire leadership team remains focused on prioritizing data-driven projects, supporting data governance, and measuring the results of data investments. “If leaders demonstrate support for data governance processes and data-driven metrics, employees will too,” Finnerty says.
Beyond ownership at the C-suite level, executive positions such as chief data officer or chief analytics officer help raise consciousness of the role of data and analytics across the enterprise. It’s important that these roles don’t foster additional silos—they need to work in close alignment with you and your team, serving as a conduit between different business constituencies to keep everyone centered on common data and analytics goals. “The CIO can help an organization very quickly skill up, break down silos, and use new technologies and tools to go beyond its comfort zone,” says HPE’s Maccaux.
This is especially important as the lines between business stakeholders and IT departments, as well as IT and operational technology (OT) fiefdoms, continue to blur. It’s also essential for clear and effective communication between all groups. Creating basic ground rules for data sharing—including data-sharing agreements, cheat sheets, and enterprise guidelines—helps create cohesion across the enterprise. It also ensures that data and analytics pilots get off the ground faster and ultimately scale more effectively.
At the business user level—and among IT staff—there’s work to be done on fostering a data-literate culture that breeds success. Training and educating users with the skills to serve as citizen data scientists and stewards enables them to unlock the value of data closest to where it can have the greatest impact. To that point, self-service and low-code/no-code capabilities are essential for democratizing data and data-driven action. These options also help mitigate reliance on highly paid data scientists and open doors to average users to identify and orchestrate potentially valuable use cases.
Foster a data-literate workforce.
It’s one thing to launch a successful pilot, but for data and analytics to move the needle on business ROI, it’s all about creating a data infrastructure that can scale flexibility and efficiently—from edge to cloud to data center—to meet evolving business needs. One essential order of business: Use a common framework and structure for data governance so data is secured, meets compliance standards, and can be shared consistently across the greater enterprise. As Kelly-Moore Paints maps out its data warehouse and analytics initiatives, knowing where the company is today and what the goals are for the next five years are central to its ability to scale pilot projects across the enterprise. “Knowing our long-term strategy lets everyone engage in the steps that go along with scaling data initiatives,” says Meyer, citing the company’s growth plan as an example. “Knowing what data is driving it and where it’s driving to is key,” she explains. “You have to know your destination.”
To get data into the hands of more people, organizations must shift away from the traditional “gating and control mindset” long intrinsic to traditional data management. “It needs to be more about data enablement than data control,” says West Monroe’s Laney. “You have to teach users how to use data appropriately, how not to use data inappropriately, and how to handle and manage data. Only then do you give them the keys to self-service.”
This can help facilitate scalability. Annotating, tagging, and cataloging data can ensure the proper lineage, enabling data to be repurposed, updated, and shared with context across the enterprise. In addition, watch for gaps in data that will deter attainment of stated business goals. This includes internal data silos that may still exist on the periphery of the central data foundation as well as external data sources that can bring additional value or context. In fact, 63% of the respondents to the IDG survey said that external data sources were by far the biggest resource that is untapped, potentially undermining their ability to achieve intended business value.
When devising a road map, companies with clear unifying use cases should consider a centralized data strategy. For others, a federated data strategy is the better path. The latter approach maintains a single source of truth at an enterprise level to maintain the proper controls and to address data gravity issues. It also enables distributed data access to individuals across lines of business, providing the freedom to innovate and leverage data as they see fit.
Given the complexity of data operations at scale, automation is crucial to achieving business value. Modernizing infrastructure via platforms and tools that support DataOps processes will ensure the continuous flow and maintenance of big, streaming, and traditional data to myriad business applications as well as to artificial intelligence (AI)/machine learning (ML) pipelines. Sunbelt Rentals is assembling an automation framework that spans data lineage, data movement, data retention, and data security practices, Saini says. “When things are happening at scale, you can’t have dependencies that require manual intervention—that’s where the reliance on machine learning and automation comes in to keep things moving,” he adds.
As part of the DataOps process, develop metrics and practices for continuous delivery and improvement—a tactic that promotes iterative test cycles and addresses perpetually changing data needs. Apply accepted business metrics to data such as contribution to revenue or ability to be utilized as well as established asset management standards that are relevant to the specific business or industry. “Too often companies make these up on their own rather than paying homage to other [asset] management techniques,” West Monroe’s Laney says.
Building a foundation for success
To tackle today’s data-first modernization challenges, organizations need to create a foundation that connects, protects, and analyzes data and enables it to be acted upon from edge to cloud at enterprise scale with the simplicity and agility of a cloudlike experience. To successfully unlock data’s full value, the right data foundation should include the following … [click on any tab to learn more]
A modern infrastructure …
Enterprise-grade controls …
A common as-a- service model for hybrid environments …
Modernization of critical analytics workloads …
A common platform and lexicon …
Low-code/no-code functionality and self-service capabilities …
Automation wherever possible …
Support for open platforms and software vendor ecosystems …
Shared risk models and service-level-agreement-driven partnerships …
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A modern infrastructure that delivers frictionless access to data regardless of where it resides: in a public cloud, a hybrid cloud, on-premises, or at the edge
Enterprise-grade controls delivered through integrated and native data security models that operate across a hybrid, multicloud landscape
A common as-a-service model for hybrid environments that spans data centers, cloud, and the edge to deliver agility while decreasing time-to-insights
Modernization of critical analytics workloads to support MLOps, resulting in automated pipelines, industrialized AI/ML models at scale, and shorter time-to-insights
A common platform and lexicon for discovering, registering, governing, and protecting data sets, enabling them to be consumable, with context, across the greater enterprise
Low-code/no-code functionality and self-service capabilities that flatten the curve for analytics initiatives, enabling citizen data scientists and everyday business users to fully leverage data to garner insights at the point where they are most impactful
Automation wherever possible based on policies, including provisioning of the environment and deployment of solutions to bring organizations closer to the concept of a data analytics factory
Support for open platforms and software vendor ecosystems that deliver greater choice, repeatability, and flexibility for application and workload modernization
Shared risk models and service-level-agreement-driven partnerships to ensure that intended business outcomes are met
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The bottom line
Data is well positioned as the lifeblood of modern business, but only if it’s managed and measured like other critical enterprise assets. Forward-thinking CIOs are breaking down organizational barriers, nurturing data-first cultures, and ushering in new data management paradigms, positioning enterprises to reap the rewards of data and drive profitable digital transformation. //
In the end, the consummate foundation for realizing data’s full value should allow for flexibility and agility, empowering organizations with the dexterity to meet the perpetually changing demands of business. “It’s about being a business enabler,” explains Matt Hausman, group manager, Ezmeral GTM at HPE. “Everything should be software-defined as a service to provide a fast, agile, and future-proof platform.”
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Learn More
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For information about how Hewlett Packard Enterprise can help your organization modernize its data strategy, visit
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