AN INTEGRATED APPROACH to ANALYTICS
Successful organizations are using an integrated organizational model which facilitates the effective broader use of analytics beyond a data scientist community. It’s benefits include:
Greater fungibility of processes and resources
Sharing and scalability of best practices
Process agility and enablement of experimentation
Flexibility to acquire best in class data analytics tools and data types
Operating from a consistent data universe
Three Keys to Remaining Competitive in the Data Analytics Arms Race
2016 data analytics capability studY
Organizations face ever-increasing pressures, making effective use of data analytics an area of opportunity for competitive advantage and a necessity if institutions are to remain in the race
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THE NEXT FRONTIER OF DATA ANALYTICS
CONCLUSION: As more and more organizations are adopting data-driven principles, other players will need to keep up in an environment that can arguably be described as a zero-sum game
deeply understand their goals and motivations to retain them
access customers with the highest potential value and
In the context of growing pressures, it is imperative for organizations to be able to:
DATA ANALYTICS CENTER OF EXCELLENCE
DATA MANAGEMENT AND PROVISIONING
CENTRALIZED CAPABILITIES FUNCTION
Analytics Team
Business Unit 4
Business Unit 3
Business Unit 2
Business Unit 1
EXECUTIVE LEADERSHIP
Operations oriented deployment prevalent for some and the “next frontier” for many
5%
24%
25%
28%
29%
39%
56%
68%
75%
78%
Customer interaction - voice/images/video
Identifying and managing enterprise risks
Preventing revenue loss
A/B split testing
Conducting process Quality assurance
Production monitoring/process optimization
Cross selling/up-selling
Optimizing operational processes
Conducting customer/market research to drive insight
Optimizing sales and Marketing processes
More and more successful organizations are adopting data-driven principles, capabilities, and a growing body of their own analytical talent to remain competitive or to extend their lead in the race. For others, data mastery and the deployment of modern analytical capabilities and advanced methods will be an imperative to keep up.
68% of surveyed organizations are using data analytics beyond marketing, sales and customer targeting.
Organizations who have achieved the most notable uplift have invested in more extensive data analytics, moving to the next frontier of data analytics applications.
At a number of leading organizations the balance of IT resources and investments deployed has shifted dramatically from more traditional transaction processing, data services, and infrastructure provisioning towards shared, democratized analytics capabilities.
Many leading organizations (44% of surveyed organizations) have transitioned towards an integrated data analytics operating model, with a two-tier cross-enterprise arrangement.
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44% of surveyed organizations have transitioned towards an integrated operating model
Through our cross-sector study of 80 organizations, we have found that innovators who have successfully deployed data analytics to achieve significant uplift share three characteristics in common.
In this challenging environment, non-traditional dynamic startups are cropping up in multiple sectors and vying for the same "sweet spot" segments pursued by traditional players. In a growingly saturated market, customers are inundated with digital messaging and are highly selective about who they trust, what they respond to, and the level of service they expect. This creates an even stronger need for traditional players to reimagine their processes and even the fundamental value proposition to capture and retain the most valuable customers.
Many companies are operating in an environment of sluggish growth and significantly compressed margins and where the competition is increasingly leaner, smarter and more agile. Regulatory expectations are placing a further strain on revenues and costs, and increasing operational demands. Against this backdrop, it is imperative for organizations to be able to access, serve, and delight valued customers through enhanced customer targeting enabled by advanced analytics.
Leading organizations have long recognized the importance of having a data-driven analytics-excellence strategy as a competitive differentiator. However, in the context of growing market pressures, effective use of data analytics is arguably now becoming table stakes.
Three Keys to Remaining Competitive in the Data Analytics Arms Race by Paul Mee, Partner, Oliver Wyman
infographic
This quick feedback loop is akin to a sugar rush. But there can be real energy in that sugar. One major hotel chain that used data analytics with real-time data to answer questions about customer preferences increased annual booking revenues by tens of millions through dynamic room pricing optimization.
New capacity additions
• Room rates are forecasted on a daily basis using external inputs • Updated rates are immediately published on the hotel website and travel aggregators
Local vacancies and room rates (own and competitors)
Hotel room inventory (own and competitors)
Seasonality
Weather
Local event and conference dates
Factors (Supply Examples)
Room Rate Snapshot
Dynamic Pricing Engine
Factors (Demand Examples)
Real-time daily room rate forecasts
Internal and external market data used as pricing inputs
Dynamic pricing engine utilizes relative supply and demand factors
Investment and value realized: Case study A hotel operator increased annual booking revenues by $40 MM through dynamic room price optimization
An enterprise knows how, when, and why targeted consumers respond to a given marketing action. Given holistic data capture, the enterprise understands how a product or service was used, the nature of the customer experience, and the likelihood of a repeat purchase.
With techniques like A/B split testing and machine-based learning, data-driven marketing actions can be quickly tuned for optimal results. As a consequence, sales, marketing and customer-targeting optimization have been the predominant areas of analytics investment.
An enterprise can precisely measure how effective certain actions or representations are from a ROI perspective
The next frontier in data analytics is operations efficiency. The benefits can bigger, broader, and potentially more sustainable. Examples of applying analytics to operations include:
DEVELOP a case for action and confirm where and how this can be verified with more analytics, prototyping, and select pilots.
PLAN AND PREPARE an operational blueprint; change strategy and performance metrics associated with the envisioned operational transformation and data-related capabilities.
DETERMINE the data requirements and what it would take meet these requirements across existing and new/alternate data sources.
PRIORITIZE the next-generation or digital-alternative operating arrangements where data could make the greatest difference.
IDENTIFY the operational areas and customer-experience processes where significant improvements could be realized with more, or better, data.
Today, leading firms are casting a broad net in terms of collecting data—from social media telematics, advanced sensors, video and IoT (Internet of Things) technological capabilities. The shift from using only structured internal data to also using unstructured internal and external data is transformative. Integrating data from multiple sources enables a step change in operational efficiency that has the potential to transform business models. In the end, data-driven operational efficiency is much more impactful and sustainable than data analytics for marketing. It’s worth exploring this new frontier.
All positive, but the above benefits can be harder to achieve than marketing-related data analytics. Applying data analytics to operations and day-to-day decision-making requires changing processes, technology, and talent at a fundamental level. To reach this frontier, an organization needs new ways of sourcing, ingesting, processing, and storing data. To pursue such strategic ambitions typically involves five major steps:
Feedback is really fast
Why?
The inaugural wave of customer-oriented data analytics focused on digital marketing and improving the customer experience.
THE NEXT FRONTIER OF DATA ANALYTICS: OPERATIONAL EFFICIENCY by Paul Mee, Partner, Oliver Wyman
Just being able to get apples-to-apples numbers puts us in a much stronger position to be able to complete the analysis needed and make related decisions with confidence.
During Oliver Wyman’s recent Data Analytics Capabilities Study (DACS), a repeated observation was made regarding analytical results. As one Chief Data Scientist commented, “We can end up with analytical outcomes that may as well be apples, bananas, and screwdrivers as they are so hard to compare or reconcile”. This raised the question as to why this phenomenon occurs and what kind of organizational arrangements represent best practices to circumvent.
If what you’ve done moves the needle, tout it. Develop a narrative around applied analytics to a specific business case and elevate it to the C-suite. Analysts often forget how bad things were in the past; they need to develop their own scoreboard and show people what widespread change management might look like.
When one group creates a useful model in terms of uplift, other groups want to apply it. But there are barriers to scale based on access to data. The integrated model depends on consistent data flows. Instead of 100 roads with bridges streaming data in different formats, you want a super highway that provides data for the community, and a more efficient way of talking to IT.
An insurer modeled the propensity to file a health-insurance claim, and then applied the same model to accident claims. Similarly, a telco developed a pricing model for phone charges — then applied the model to TV, bundles, millennials and other groups. As different teams gradually work to use data and models in a consistent way, the integrated model begins to take hold. The business case is not always an economic case, but articulates a new common-sense approach that is faster and less wasteful, and you can put a dollar value on that.
If you were to rebuild the standard architecture, what tools would you need, what data (sources and format) would scientists want, what training would business units need? Don’t try to retrofit you current tools; get new ones.
Without trying to work across the entire enterprise, form of club of smart analysts. Build a group that will share expertise, fungible models, and streams of data. Use that nucleus as a building block.
A number of design principles are shaping the successful implementation of data analytics at leading organizations. Foremost is the use of an integrated operating model. Most firms begin with a de-centralized or centralized model, both of which have flaws that impede full transformation into a data-driven organization. Here’s the typical evolution to full integration:
De-centralized (or “federated”). Disconnected cells of data scientists conduct analysis on their own terms, with their own tools, often sourcing data in an inconsistent manner.
Centralized. Highly skilled data scientists represent a Center of Excellence, executing on an analytics demand queue on behalf of business areas. Data scientists are dealing with multiple data-management formats from different units.
Integrated. Data analytics is integrated, with a CoE positioned to enable organization-wide analytics excellence and act as data broker to source and curate data. Data scientists act as enablers to the broader analytical community—creating specialized web tools and dashboards, and optimizing analytical models). This essentially democratizes data tools and allows non-scientists and non-engineers to generate insights
Some people will resist the transition away from a de-centralized model, which gives them a certain power and independence. Overcome that resistance by showing the potency of an integrated, team-based, democratized approach to data analytics.
NO MORE “Apples, Bananas, AND SCREWDRIVERS” AN INTEGRATED APPROACH to ANALYTICS by Paul Mee, Partner, Oliver Wyman
Transitioning to an integrated model is not easy, in terms of politics and governance, as it requires a new organizational mindset toward data and analytics. But looking at firms that have succeeded, even in pockets if not enterprise wide, we see a pretty straightforward path:
ENVISION A MORE ROBUST ARCHITECTURE.
TELL A STORY OF SUCCESS.
TACKLE THE FUNDAMENTALS AROUND DATA.
BUILD A BUSINESS CASE.
START SMALL AND ACT QUICKLY.
