From Data to Decision, moving beyond the Hype III 04-09

Executing an Analytics Strategy Will Change Behavior

The main goal of a formal organizational strategy for data and analytics is typically to improve decision making with analytics in a wide realm of activities. These might include customer segmentation, pricing, identifying new markets, managing supply chain risk, fraud detection, creating efficiencies, and improving operational effectiveness. Our survey results and interviews offer strong evidence that successful analytics strategies dramatically shift how decisions are made in the organization. Organizations with formal analytics strategies exhibit four key characteristics.

1. Executives are both proponents and users of analytics

In the most analytically mature organizations, senior management, including members of the C-suite, are much more likely to use analytics than their counterparts in less mature organizations. (See “All Management Levels Use Analytics in Analytically Advanced Organizations.”) One survey respondent from an Analytical Innovator company remarked, “Data analytics is used by C-suite for providing strategic direction to the whole organization. It is also used by middle management to improve day-to-day operation of the organization.”

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Figure 9: All Management Levels Use Analytics in Analytically Advanced Organizations

Successful analytics strategies shift how decisions are made at all levels in the organization.

Senior managers in Analytical Innovator organizations also tend to be more open to change their business in response to analytical insights. One manager reported that “a long-standing premise for a business line was disproved using analytics, bringing into question both its pricing and viability going forward.” Another Analytical Innovator respondent said, “We recently analyzed the prescribing behavior of tens of thousands of physicians [in the U.S.] over the past six years in order to inform our launch strategy. The analysis changed marketing decisions and significantly improved the potential outcome. The analysis and model predictions changed our behaviors and strategic plan.”
Respondents from Analytically Challenged organizations offered a more negative appraisal about the use of analytics by senior management. Some emphasized the reluctance of senior managers to incorporate analytics in their decision making:

“Senior management tends to come from sales/marketing functions where gut feel and/or outsourcing have dominated, so they just don’t have the skills or understanding.”

“Top management are people before the computer; they have more confidence in their intuition and feelings than in hard data analysis.”

“Our senior partners believe that an organization of our size does not have time/resources to apply analytics, and they are convinced that their intuition is the best tool to use. They fear being wrong.”

“Our CEO is ‘often wrong, but never in doubt,’ and hates seeing anything that contradicts his intuition.”

2. Analytics and intuition are blended, not balanced

In more analytically advanced organizations, managers tend to give more weight to analytics when making a wide range of decisions than managers in less advanced analytical organization. (See “Blending Analytics With Intuition.”) From cost-cutting decisions to more strategic choices, data is deployed to a far greater extent in the most advanced analytical companies. This reliance on data is a reach for many Analytically Challenged organizations, whose executives may perceive analytics and experience-based intuition in terms of a trade-off or, worse, as different approaches to decision making that are inevitably at odds. “Intuition versus data is a false dichotomy,” counters Sprigg of IHG. “Great analytics teams love intuitive thinkers who love data, because it’s that intuition — that human spark — that brings ideas and innovation. When I work with a decision maker, we identify the questions we’re trying to answer and we come back to them with answers. I love when this process sparks an entirely new conversation and someone says, ‘Oh, now that I know this, wow; now I need to think differently in these three other areas.’”

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Figure 10: Blending Analytics With Intuition

Managers in more analytically advanced organizations lean more on analytics than in less mature organizations.


Furthermore, when analytical tools bolster intuition, new opportunities can be created. Joseph Bruhin, chief information officer at New York-based beverage conglomerate Constellation Brands, says analytics has helped the company’s own sales force have strategic discussions with retailers about the relative value of different opportunities such as product shelf placements. When the analytic evidence from the company’s visualization tool showed the comparative benefits of different product placements or offerings, and that evidence matched the experience of retailers, the tool came to be used as a credible platform to have new kinds of conversations. “It’s been a massive and very positive transition,” says Bruhin.

3. Analytics is applied strategically

The strategic use of analytics increases with analytical maturity. Analytical Innovators are much more likely to apply analytics strategically than Analytically Challenged and Analytical Practitioner organizations, which tend to use analytics for more operational purposes. (See “Operational Versus Strategic.”) Using analytics to ask and answer larger strategic questions can deliver significant benefits. In 2005, a major car company’s executives wanted to assess how cohesive the company’s international operations were. According to Gahl Berkooz, the former global head of data and governance, “We started generating metrics around how close we were to a global enterprise. How common are our parts, for example. Once we generated those analytical metrics, people realized that there was very little commonality between the products and the different regions [Americas, Europe, and Asia]. We saw a huge opportunity to deliver savings and efficiency.”  

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Figure 11: Operational Versus Strategic

Analytics is used strategically in more analytically advanced organizations.



While the automaker’s managers were not surprised that so few duplicate parts existed, the parts inventory helped establish a global product parts taxonomy that eventually was credited with saving the company $2 billion in costs. According to Berkooz, this system embodies how analytics can enhance traditional business decision making: “Once we had the global product structure in place, any time somebody wanted to introduce what we call a new base part, we were able to run it against this single global taxonomy and see if we already had that kind of a base part in the system. This change in the structure involved changing people’s responsibilities, and people didn’t like that. We needed to have strong executive support to do this, and because of the imperative of globalization, we had that. Eventually, we were able to reduce dramatically, by over 90%, the rate of new base part number introductions.”

4. Initiatives go beyond optimizing existing processes to explore new ideas

The most mature analytical organizations have a decisive advantage when it comes to exploring the potential of data. While less mature organizations conduct pilot studies to see what they are capable of doing with data, more mature companies are using their capabilities with data to discover new ways to create business value. (See “Spirit of Discovery.”) A case in point is a population analytics initiative sponsored by the U.S. Department of Veteran Affairs called the Million Veteran Program (MVP). It is designed to answer key questions about high-priority health conditions affecting U.S. military veterans, such as heart disease, kidney disease, diabetes, cancer, and substance use. The goal is to build one of the world’s largest medical databases by collecting blood samples and health information from 1 million veteran volunteers. Data is stored anonymously to support research on the effects of genetics, military exposure, lifestyle, and health factors on diseases and military-related illnesses, such as post-traumatic stress disorder.

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Figure 12: Spirit of Discovery

Analytical Innovators are more likely to use data to make new discoveries.


“We’re at this very interesting point in time where we now have the capability to measure biochemical parameters such as genes, to do genotyping on a large scale at a reasonable price,” said Dr. Michael Gaziano, a principal investigator. The studies using this data will not only explore questions related to chronic illnesses common among veterans but will also help establish new methods for securely linking MVP data with other sources of health information, such as the Centers for Medicaid and Medicare Services.

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Figure 13: Proactive With Big Data

Analytical Innovators explore early to gain awareness about technology potential and limitations.



One way managers can explore data is through big data initiatives, which can open up new ways of conducting business. Analytical Innovators are well underway with big data initiatives, having gained experience and started to benefit from their up-front investments. (See “Proactive With Big Data.”) More mature organizations are likely to be building familiarity with other technologies, such as the Internet of Things, so that they can identify, assimilate, and apply these technologies to their organization’s specific needs.

Transitioning to the Next Phase of Analytics

For many organizations, cultivating a formal analytics strategy and ultimately linking that with corporate strategy requires changing how important business decisions are made. For some companies, the biggest stumbling block may be building processes that enable managers to not only trust the data but also trust that their reliance on data will not undermine the respect that others have for their experience. Information management is clearly a critical component of any robust analytics strategy, but at the same time, cultural norms around decision making, such as respect for and use of data, along with skills development, may need adjustment. A strategy for successful analytics will integrate the business and technology sides of an organization by providing the ground rules for how these groups work together and why. Making this transition to the next phase of analytics will often include an emphasis on four key issues:

Data awareness and responsibility. As managers rely more on data, their awareness of data in the organization — where it is, who has it, what’s available, how to find what one needs — has to grow as well. Adam Leary, lead data scientist and senior director of the data team at CBS Interactive, calls this “data awareness.” With greater awareness, however, comes greater responsibility. Curating data, for instance, once the exclusive purview of business intelligence units, is increasingly being required of general managers. Similarly, a greater number of general managers are being called upon to participate in data governance and compliance activities.

At the insurance giant Aetna, the CEO mandated reporting requirements for financial performance data across business lines after each business head presented data showing that their function was profitable, even though the company had just lost hundreds of millions of dollars.7 At the Bank of England, senior managers from across the Bank sit on a data council that addresses a wide range of data issues — from quality to access to data governance. Just as important, as recent stories about data-related deceptions at Volkswagen and Takata demonstrate, the credibility of data can be an organizational risk factor as well as a building block for achieving the potential of analytics.

Openness to new ideas. Entertaining a wide range of ideas is fundamental to cultivating both innovation and competitive advantage with analytics, but creating room in an organization to enable that to happen demands openness to new ideas that challenge the status quo, along with a tolerance for mistakes. As the philosopher Ludwig von Wittgenstein once remarked, “If people did not do silly things, nothing intelligent would ever get done.” Analytical Innovators use existing data to create or curate new data by looking at it in inventive ways, developing new attributes, or asking questions in new ways. Greg Jones, vice president of enterprise data and analytics at credit reporting agency Equifax, says it best: “We use existing data to uncover really interesting relations and identify things that we didn’t have the capabilities to do before.” For every interesting analytics anecdote, however, the people we interviewed also talked about the many uninteresting results and attempts that didn’t yield a “eureka” moment. An organization must be tolerant of these; after all, the analysis wouldn’t be necessary at all if people knew ahead of time which results would work out.

Signals about the importance of analytics. Employees frequently look for signals about what is important to management, and whether what is important today will be important tomorrow. Establishing organizational structures such as data councils, data labs, and centers of excellence signals to staff that the organization is taking data seriously as a core asset. Senior managers who use analytics themselves and set clear expectations about staff’s use of data in proposals make visible statements about the importance of analytics. One interviewee says he uses the phrase “the analytics side is bringing in the truth” to signal to people in the organization that the voice of data and evidence will be a key part of the conversation.

Decisions that blend analytics with intuition. Managers in more advanced analytics companies give more weight to analytics when they make key business decisions. However, if the reports from Analytically Challenged respondents are any indication, the humility required to rely on analytics remains a stretch for some executives. Equip senior managers with skills and the attitude to appreciate that analytics can take intuition much further, in some instances, than intuition by itself. Help managers appreciate that the process of developing analytics is not a mechanical process devoid of intuitive leaps. As Sprigg of IHG notes, analytics teams love intuitive thinkers, especially among general manager decision makers. Some of the best analytical results may come from inspired collaborations between IT and traditional decision makers that forge new questions and elicit new types of insights. The perceived dichotomy between analytics and intuition is false for two reasons: Intuition has a critical role in developing analytics; and blending analytics with intuition in decision making can produce more effective results than either alone, especially when making strategic decisions.

Conclusion

Managing with analytics is now a mainstream idea, though not a mainstream practice. It is not surprising that Gartner Research identifies getting “the right information to the right person, at the right time” as a strategic technology trend.8 Accenture has identified this issue as a top priority for CIOs.

Even so, many companies underappreciate the organizational resolve necessary to achieve this goal.
On the technology side, simply identifying where and what data exists in an organization can be extremely valuable, but it is also an arduous, time-consuming exercise that few organizations pursue.

The Bank of England and the City of Amsterdam are two exceptions: In their efforts to  institutionalize analytics, each began to reinforce its analytics foundations by taking an inventory of the data sets in their respective organizations. This tedious task identified nearly a thousand data sets at the Bank of England, and 12,000 among city departments in Amsterdam. “Inventory sounds quite boring,” remarks one executive, “but it’s fundamental. We need to know what we’ve got to know how to manage it.”

On the management side, deepening the use of analytical decision making changes management behavior at several levels: how managers blend information and experience to make specific decisions, how managers and technologists together build processes to create the right information, and how managers improve their skill sets to make the best use of data. Together, achieving these shifts may fundamentally change how managers operate. Yet few companies have a strategic plan for analytics or are executing a strategy for what they hope to achieve with analytics. Without a strategy for advancing the use of analytics among decision makers, the desired results from data-driven insights will be elusive.

On both the technology and management sides, organizational resolve is the difference between experimenting with analytics and using analytics to achieve strategic ends. If executives believe that analytics can help them gain a competitive advantage in their markets and, by implication, help reverse the downward trend among companies gaining a competitive edge with analytics, they must recognize and engage in the hard work that’s necessary to achieve these results.

Our research clearly shows that companies in the early stages of their analytics maturity are far less likely to have a strategic plan for analytics. This matters, especially in this era of rapidly evolving evidence-based management. In the twentieth century, the rate of technological change and business trends occurred on a much slower time frame, making it easier for companies to catch up if they fell behind the latest tech trends. But today, even as they gain better access to data, less analytically developed companies struggle to develop data-driven strategic insights. Veteran managers accustomed to experimentation and pilots with twentieth-century cycle times may (increasingly) find themselves at a disadvantage among companies that move rapidly and purposefully from experiments to new technology adoption.

More analytically advanced organizations ensure that the right data is being captured or created on an ongoing basis. In these organizations, information management is an organizational goal, not a technical one. For many organizations, especially among the growing numbers of the Analytically Challenged companies, it is time to recognize that to get the most out of data and effectively improve decision making with data across the organization, better algorithms and better analytical talent are necessary but not sufficient. Changing how decisions are made is a crucial part of how organizations function, and buying the latest technology fad alone may not cut it. With access to useful data becoming less of a problem, CIOs might consider joining with other colleagues in the C-suite to “develop the right information so that the right person with the right skills can use it at the right time.” The right information might not exist if the right questions have yet to be asked.

Courtesy MIT Sloan Management Review.

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