Part One
WHAT DOES IT TAKE TO PUT ANALYTICS TO WORK in your business? What capabilities and assets do you need in order to succeed with analytics initiatives? The next five chapters describe the success factors. We group them under the acronym DELTA—the Greek letter (depicted as Δ or δ) that signifies “change” in an equation. Together they can change your business equation:
D for accessible, high-quality data
E for an enterprise orientation
L for analytical leadership
T for strategic targets
A for analysts
Why are these elements so important? First of all, good data is the prerequisite for everything analytical; it is “clean” in terms of accuracy and format. Customer data, for example, has a unique identifier for each customer, and customer names, addresses, and purchase histories are generally accurate. Its meaning and use are commonly understood. When drawn from several sources, it is integrated and consistent. It is accessible in data warehouses, or else easily found, filtered, and formatted on the fly. Perhaps most fundamentally, it represents and measures something new, something important, or something important in a new way. In chapter 2 we detail the essentials of data management for analytics.
Several of the challenges of data management are much easier to meet if the enterprise at large “owns” important data—as well as analytical software and talent—and management across the enterprise is motivated to cooperate on analytical initiatives. You might ask, “But we’re starting small, with a specific problem in a single business function—why would we need an enterprise perspective?” The short answer is that you won’t get far without one, for three reasons:
1. Major analytics applications, those that really improve performance and competitiveness, invariably touch multiple parts of the enterprise.
2. If your applications are cross-functional, it doesn’t make sense to manage your key resources—data, analysts, and technology—locally.
3. Without an enterprise perspective, chances are you’ll have many small analytical initiatives but few, if any, significant ones.
In chapter 3, we discuss how the definition of “enterprise” varies across organizations and how to manage key analytical resources at an enterprise level.
Organizations that really capitalize on analytics in their business decisions, processes, and customer relationships have a special kind of leadership. Their senior managers are not just committed to the success of specific analytical projects; they have a passion for managing by fact. Their long-term goal is not just to apply analytics in useful areas of the business, but to become more analytical in decision-making styles and methods across the enterprise. In chapter 4, we describe the key attributes of analytical leaders and what they do.
Even very analytically inclined leaders are not going to write blank checks to fund analytics generally. What really gets their attention is the potential return of employing analytics where it will make a substantial difference. An analytical target may be strong customer loyalty, highly efficient supply chain performance, more precise asset and risk management, or even hiring, motivating, and managing high-quality people. Companies need targets because they cannot be equally analytical about all aspects of their businesses, and analytical talent isn’t plentiful enough to cover all bases. In chapter 5 we describe what makes for good targets, and how to evaluate and choose realistic ones.
Analysts have two chief functions: they build and maintain models that help the business hit its analytical targets, and they bring analytics to the organization at large by enabling businesspeople to appreciate and apply them. In chapter 6 we describe the different types of analysts, the methods to assess and improve their capabilities, and the organizational forms that bring out their best. We draw on an extensive survey of analysts that reveals what they want from their jobs and employers.
You need all five elements working together. Lack of any one of the DELTA elements can be a roadblock to success, providing fodder for the naysayers, the “this will never work around here” crowd. A missing element will lead to delay and wasted effort, so if you are better positioned in one element, try to leverage that strength to generate interest in bringing the others along. If some DELTA elements are too far ahead of others, it can lead to frustration, as when leadership sees targets and wants results, but the data or the analysts aren’t ready. You can also overspend on getting one element—typically data—ready, and then have it sit dormant because none of the other ingredients are in place.
Thus, to make real progress, you’ve got to move forward with all five DELTA elements in rough proportion. But organizations have very different starting points, different mixes of capability, and different rates of progress with analytics. To help you sort all this out and to plan and manage your development of analytical capabilities, we developed a five-stage model of progress (which we also described in Competing on Analytics):
• Stage 1: Analytically Impaired. The organization lacks one or several of the prerequisites for serious analytical work, such as data, analytical skills, or senior management interest.
• Stage 2: Localized Analytics. There are pockets of analytical activity within the organization, but they are not coordinated or focused on strategic targets.
• Stage3: Analytical Aspirations. The organization envisions a more analytical future, has established analytical capabilities, and has a few significant initiatives under way, but progress is slow—often because some critical DELTA factor has been too difficult to implement.
• Stage 4: Analytical Companies. The organization has the needed human and technological resources, applies analytics regularly, and realizes benefits across the business. But its strategic focus is not grounded in analytics, and it hasn’t turned analytics to competitive advantage.
• Stage 5: Analytical Competitors. The organization routinely uses analytics as a distinctive business capability. It takes an enterprise-wide approach, has committed and involved leadership, and has achieved large-scale results. It portrays itself both internally and externally as an analytical competitor.
We are not suggesting that becoming an “analytical competitor” is appropriate or necessary for all organizations, but most organizations will at least want to become more analytical, and move up a stage or two. In the next five chapters, we describe how each of the DELTA elements evolves across the stages. Data, for example, moves from poor to usable, to consolidated, to integrated, to innovative. Leadership moves from none to local, to aware, to supportive, to passionate.
For easy reference, in the appendix we combine this model of progress with the DELTA success factors in a table that portrays the conditions at each stage of progress. It can serve as a high-level assessment tool for your analytical capability, a map for locating where you are and where you need to go next.
Use the information and tools in these five chapters (and the appendix) to orient yourself analytically—assess your capabilities, add new capabilities, set realistic goals, get the pieces in place for analytical business initiatives, and proceed with confidence. Getting the pieces in place is especially important if your business is just starting to employ analytics in significant ways. Early success builds momentum for continued success.