Overview of the Road Map

The road map describes typical behaviors, capabilities, and challenges at each stage of development. It provides guidance on investments and actions that are necessary to build an organization’s analytical capabilities and move to higher stages of analytical competition.

Figure 6-1 provides an overview of the road map to analytical competition and the exit criteria for each stage.

Stage 1: Prerequisites to Analytical Competition

At stage 1, organizations lack the prerequisites for analytics. These companies need first to improve their transaction data environment in order to have consistent, quality data for decision making. If a company has poor-quality data, it should postpone plans for analytical competition and fix its data first. Dow Chemical’s path is instructive. It began installing one of the first SAP systems in the United States in the late 1980s but did not begin serious initiatives to use data analytically until enough transaction data had been accumulated.

Even if an organization has some quality data available, it must also have executives who are predisposed to fact-based decision making. A “data-allergic” management team that prides itself on making gut-based decisions is unlikely to be supportive. Any analytical initiatives in such an organization will be tactical and limited in impact.

Once a company has surmounted these obstacles, it is ready to advance to a critical juncture in the road map.

Assessing Analytical Capabilities

Once an organization has some useful data and management support in place, the next task is to take stock and candidly assess whether it has the strategic insight, sponsorship, culture, skills, data, and IT needed for analytical competition.

One financial services executive offers sage advice to anyone getting started: “Begin with an assessment—how differentiated is your offering versus what you and your customers want it to be? Big data and analytics can be a real game changer. But you must have a clear vision of what value looks like if you are going to figure out how to unleash that vision.”

While each stage of the road map reflects the ability of an enterprise to compete on analytics, different parts of the organization may be in very different stages of development. For example, actuarial work requires an appreciation of statistical methods that may not exist to the same extent in other parts of an insurance company. Or a pharmaceutical firm’s marketing analytics in the United States may be far more sophisticated than they are in other countries or regions simply because the US operation has greater access to data and an analytically minded manager in charge.

Just as some business units or processes may be more or less advanced than others in the enterprise, some aspects of the business are likely to be more analytically astute than others. For example, an organization may have a tightly integrated, highly standardized, and flexible IT environment but little demand for analytics, or, conversely, the demand for analytics far outstrips the capabilities of the IT organization. A utility might use machine learning capabilities to manage its electrical grid but not elsewhere in the enterprise. There may be many user departments and even individuals with their own analytical applications and data sources but little central coordination or synergy.

Organizations need to assess their level of analytical capability in three main areas. (Table 6-1 provides an overview of these key attributes, each of which is equally vital to successful analytical competition.) One cautionary note: executives are often tempted to just obtain the data and analytical software they need, thinking that analytics are synonymous with technology. But unless executives consciously address the other elements, they will find it difficult to progress to later stages.

TABLE 6-1

The key elements in an analytical capability

We’ll address the organizational issues in this chapter and go into greater detail on the human factors in chapter 7 and the technical ones in chapter 8.

A company needs a clear strategy in order to know which data to focus on, how to allocate analytical resources, and what it is trying to accomplish (what we refer to as “targets” in the DELTA model that we describe in chapter 2 and later in this chapter). For example, the business strategy at Caesars Entertainment dictated the company’s analytical strategy as well. When the explosion of newly legalized gaming jurisdictions in the mid-1990s ground to a halt, Caesars (then Harrah’s) managers realized that growth could no longer come from the construction of new casinos. They knew that growth would need to come from existing casinos and from an increase of customer visits across Caesars’ multiple properties. To achieve this goal, the company set a new strategic focus to drive growth through customer loyalty and data-driven marketing operations. This strategic focus enables Caesars to concentrate its investments on the activities and processes that have the greatest impact on financial performance—its distinctive capability. Implementing this strategy required the company to expand and exploit the data it had amassed on the gaming behaviors and resort preferences of existing customers. (See the box “Choosing a Strategic Focus or Target” for examples of where companies chose to focus their initial analytical investments.)

To have a significant impact on business performance, analytical competitors must continually strive to quantify and improve their insights into their performance drivers—the causal factors that drive costs, profitability, growth, and shareholder value in their industry (only the most advanced organizations have attempted to develop an enterprise-wide model of value creation). In practice, most organizations build their understanding gradually over time in a few key areas, learning from each new analysis and experiment.

To decide where to focus their resources for the greatest strategic impact, managers should answer the following questions:

• How can we distinguish ourselves in the marketplace?
• What is our distinctive capability?
• What key decisions in those processes, and elsewhere, need support from analytical insights?
• What information really matters to the business?
• What are the information and knowledge leverage points of the firm’s performance?

As organizations develop greater insights, they can incorporate them into analytical models and adapt business processes to leverage them and increase competitive differentiation. These strategically focused insights, processes, and capabilities form the basis of the organization’s distinctive capability.

Analytical competitors design effective decision making into their processes to ensure that analytical insights get translated into action and ultimately enhance business performance. They incorporate a way of thinking, putting plans into place, monitoring and correcting those plans, and learning from the results to help shape future actions.¹ For example, UK police analysts learned that they could predict from early behaviors which juveniles were likely to become adult criminals. The analysis concluded that if the UK police could proactively intervene when kids began to pursue the criminal path, and prevent them from going in that direction, they could dramatically reduce the number of crimes ultimately committed.² However, in order for this insight to have any impact on crime rates requires more than awareness; it requires a close collaboration between police, educators, and human services workers to establish programs and practices aimed at eliminating the root causes of crime.

Finally, to ensure that strategy is converted into operational results, organizations must define and monitor metrics that are tied to strategic enterprise objectives, and align individual incentives and metrics with business objectives.

Choosing a Path

After an organization has realistically appraised its analytical capabilities, it must next choose which path to pursue. Organizations blessed with top management commitment and passion for analytics can move quickly through the “full steam ahead” path, while the rest are forced to take the slower “prove-it” detour.

Stage 2: Prove-It Detour

To those already convinced of the benefits of analytical competition, having to avoid the fast track feels like an unnecessary detour. Indeed, this path is much slower and circuitous, and there is a real risk that an organization can remain stalled indefinitely. We estimate that having to “prove-it” will add one to three years to the time needed to become an analytical competitor. But executives unwilling to make the leap should take a test-and-learn approach—trying out analytics in a series of small steps.

For organizations taking the detour, analytical sponsors can come from anywhere in the organization. For example, at one consumer packaged goods manufacturer, a new marketing vice president was shocked to discover that the analytical capabilities he took for granted at his former employer did not exist at his new company. Rather than try to garner support for a major enterprise-wide program, he logically chose to start small in his own department by adopting an analytically based model for planning retail trade promotions. In situations like this, initial applications should often be fairly tactical, small in scale, and limited in scope.

Despite its drawbacks, there are also important advantages to taking the slower path. Any true analytical competitor wants to have a series of experiments and evidence documenting the value of the approach, and the prove-it path helps the organization accumulate that empirical evidence. As managers get more experience using smaller, localized applications, they can gain valuable insights that can be translated into business benefits. Each incremental business insight builds momentum within the organization in favor of moving to higher stages of analytical competitiveness.

There are practical reasons for taking the prove-it road as well. By starting small, functional managers can take advantage of analytics to improve the efficiency and effectiveness of their own departments without having to get buy-in from others. This approach also requires a lower level of initial investment, since stand-alone analytical tools and data for a single business function cost less than any enterprise-wide program.

In stage 2, it is best to keep things simple and narrow in scope. The steps essentially boil down to:

1. Finding a sponsor and a business problem that can benefit from analytics
2. Implementing a small, localized project to add value and produce measurable benefits
3. Documenting the benefits and sharing the news with key stakeholders
4. Continuing to build a string of localized successes until the organization has acquired enough experience and sponsorship to progress to the next stage

An organization can linger in stage 2 indefinitely if executives don’t see results, but most organizations are ready to move to the next stage in one to three years. By building a string of successes and carefully collecting data on the results, managers can attract top management attention and executive sponsorship for a broader application of analytics. At that point, they are ready to progress to stage 3.

Table 6-2 summarizes some of the major differences in scope, resources, and approach between the full-steam-ahead and the prove-it paths.

TABLE 6-2

Attributes of two paths to analytical competition

For an example of a company that chose the prove-it path, we will now look at the experiences of an organization that has begun to develop its analytical capabilities. (We have disguised the company and the names of the individuals.)

Stage 3: Analytical Aspirations

Stage 3 is triggered when analytics gain executive sponsorship. The executive sponsor becomes an outspoken advocate of a more fact-based culture and builds sponsorship with others on the executive team.

Executive sponsorship is so vital to analytical competition that simply having the right sponsor is enough to move an organization to stage 3 without any improvement in its analytical capabilities. However, those organizations pursuing the prove-it path will have already established pockets of analytical expertise and will have some analytical tools in hand. The risk is that several small groups will have their own analytical fiefdoms, replete with hard-to-integrate software tools, data sets, and practices.

Whether an organization has many analytical groups or none at all, it needs to take a broader, more strategic perspective in stage 3. The first task, then, is to articulate a vision of the benefits expected from analytical competition. Bolstered by a series of smaller successes, management should set its sights on using analytics in the company’s distinctive capability and addressing strategic business problems. For the first time, program benefits should be defined in terms of improved business performance and care should be taken to measure progress against broad business objectives. A critical element of stage 3 is defining a set of achievable performance metrics and putting the processes in place to monitor progress. To focus scarce resources appropriately, the organization may create a centralized “analytics hub” to foster and support analytical activities.

In stage 3, companies will launch their first major project to use analytics in their distinctive capability. The application of more sophisticated analytics may require specialized analytical expertise and adding new analytical technology. Management attention to change management is critical because significant changes to business processes, work roles, and responsibilities are necessary.

If it hasn’t already done so, the IT organization must develop a vision and program plan (an analytical architecture) to support analytical competition. In particular, IT must work more aggressively to integrate and standardize enterprise data in anticipation of radically increased demand from users.

The length of stage 3 varies; it can be as short as a few months or as long as two years. Once executives have committed resources and set a timetable to build an enterprise-wide analytical capability, they are ready to move to the next stage.

BankCo (again, we have disguised the company and other names) is an example of an organization moving from stage 3 toward stage 4.

Stage 4: Analytical Companies

The primary focus in stage 4 is on building world-class analytical capabilities at the enterprise level. In this stage, organizations implement the plan developed in stage 3, making considerable progress toward building the sponsorship, culture, skills, strategic insights, data, and technology needed for analytical competition. Sponsorship grows from a handful of visionaries to a broad management consensus; similarly, an emphasis on experimentation and analytics pervades the corporate culture. As the organization learns more from each analysis, it obtains a rich vein of new insights and ideas to mine and exploit for competitive advantage. Building analytical capabilities is a major (although not the only) corporate priority.

While there are many challenges at this stage, the most critical one is allocating sufficient attention to managing cultural and organizational changes. We’ve witnessed many organizations whose analytical aspirations were squelched by open cultural warfare between the “quant jocks” and the old guard. A related challenge is extending executive sponsorship to the rest of the management team. If only one or two executives are committed to analytical competition, interest will immediately subside if they suddenly depart or retire. In one case, the CEO of a financial services firm saw analytical competition as his legacy to the organization he had devoted his life to build. But his successors did not share his enthusiasm, and the analytical systems developed under his leadership quickly fell into disuse.

As each analytical capability becomes more sophisticated, management will gain the confidence and expertise to build analytics into business processes. In some cases, they use their superior insight into customers and markets to automate key decision processes entirely.

In stage 4, many organizations realign their analysts and information workers to place them in assignments that are better suited to their skills. As a company becomes more serious about enterprise-wide analytics, it often draws together the most advanced analysts into a single group to focus on strategic issues. This provides the organization with a critical mass of analysts to focus on the most strategic issues, and provides the analysts with greater job satisfaction and opportunity to develop their skills.

Once an organization has an outstanding analytical capability combined with strategically differentiating analytics embedded into its most critical business processes and has achieved major improvements to its business performance and competitiveness, it has reached the final stage.

Stage 5: Analytical Competitors

In stage 5, analytics move from being a very important capability for an organization to the key to its strategy and competitive advantage. Analytical competitors routinely reap the benefits of their enterprise-wide analytical capability. Proprietary metrics, analytics, processes, and data create a strong barrier to competitors, but these companies are always attempting to move the analytical bar further.

Executive commitment and passion for analytical competition in this stage is resolute and widespread. The organization’s expertise in analytical competition is discussed in annual reports and in discussions with investment analysts. Internal performance measures and processes reinforce a commitment to scientific objectivity and analytical integrity. Analytics are used to drive innovation across the organization. And predictive and prescriptive analytics are an important and growing component of the enterprise’s product offerings as well.

However, analytical competitors must avoid complacency if they are to sustain their competitive advantage. They need to build processes to continually monitor the external environment for signs of change. They must also remain vigilant in order to recognize when changing market conditions require them to modify their assumptions, analytical models, and rules.

We’ve described a large number of these companies already, so we won’t give an example here. Each stage 5 company is different in terms of the strategic capability it emphasizes, the applications it employs, and the path it followed to success. But they have in common an absolute passion for analytics and a resulting strong financial performance.

Progressing Along the Road Map

As you can see from these examples, becoming an analytical competitor takes more than an enthusiasm for data analysis. Many executives seeking to build their analytical capabilities begin by purchasing software, hiring quantitative analysts, and piloting some kind of analytical initiative. While those actions can be a good start, they are just that—a beginning from which analytical leaders must build in order to truly develop their analytic capability. “Analytics is a muscle we build,” according to Elpida Ormanidou, formerly vice president of global people analytics at Walmart and now vice president of advanced analytics and testing at retailer Chico’s FAS, Inc. “You cannot buy yourself into an analytics capability.”³

The path to success with analytics will contain speed bumps along the way. Managers not yet on the full-steam-ahead path may be tempted to either shift resources away from or shut down completely an analytics initiative if business conditions put pressure on the organization. Also, the shift to analytics will most likely require employees to change their decision-making processes. It takes time for the organization to adjust to new skills and behaviors, but without them, no real change can occur. As a result, the most important activity for the leadership team is to keep analytical initiatives on track and to monitor outcomes to ensure that anticipated benefits are achieved.

At every stage of development, companies need to manage outcomes to achieve desired benefits, setting priorities appropriately and avoiding common pitfalls.

As we described in chapter 2, the DELTA model provides guidance to executives seeking to create a road map for building their organization’s analytical capabilities. DELTA, the Greek letter that signifies “change” in an equation, seemed like a fitting acronym to describe the elements and steps needed to implement an analytical capability. Unless you are blessed with analytical leadership and culture, becoming an analytical competitor means significant change for an organization. To recap, DELTA stands for:

• Data: Leveraging data to glean valuable insights
• Enterprise: Managing and coordinating resources at an enterprise level
• Leadership: Fostering an analytical leadership team and culture
• Targets: Focusing analytics investments on the best, high value areas
• Analysts: Developing and managing analytical talent

We’ll briefly summarize each of these capability elements here.

Conclusion

This chapter has explored the key attributes of an analytical capability and provided a directional guide to the steps that lead to enhanced analytical capabilities. We wish you rapid movement to the full-steam-ahead path to becoming an analytical competitor. In chapter 7, we will explore ways to successfully manage an organization’s commitment to key people who are—or need to be—using analytics.