If we’re going to make better decisions and take the right actions, we’re going to have to use analytics. For too long, managers have relied on their intuition or their “golden gut” to make decisions. For too long, important calls have been based not on data, but on the experience and unaided judgment of the decision-maker. Our research suggests that 40 percent of major decisions are based not on facts, but on the manager’s gut.*
Sometimes intuitive and experience-based decisions work out well, but they can go astray: businesses often price products and services based on their hunch about what the market will bear, not on actual data detailing what consumers have been willing to pay under similar circumstances in the past; managers often hire people based on intuition, not on an analysis of the skills and personality traits that predict an employee’s high performance; supply chain managers often maintain a comfortable level of inventory, rather than a data-determined optimal level; baseball scouts often zoom in on players who “look the part,” not on those with the skills that—according to analytics—win games.
Consider this first-hand report from an executive with a software company. When asked about a recent sales seminar put on by the company, he replied:
“It went fine. We had attendees from 110 companies who received a keynote presentation designed to instill confidence in our company’s future and to encourage cross-selling, and all 12 technical sessions were well-received.”
That would be enough for many companies. “I guess no decision needs to be made other than to continue holding these seminars at a pace of 12 per quarter around the country,” he reflected. But after further thought, he voiced some disquieting questions about what the sales seminar did not reveal:
- How many attendees were existing customers and how many were prospects?
- Were there attendees from every customer within the geographic area?
- Were there attendees from every prospect in the geographic area?
- Which attendees were high-growth prospects?
- How many attendees also attended the company’s annual conference?
His company simply didn’t know the answer to these questions. It had never bothered to gather and analyze the data. The executive considered his company to be more analytical than most, but, at least with regard to these seminars, it has a long way to go. As this anecdote suggests, even relatively smart and sophisticated companies are missing opportunities to put analytics to work to profit from better decisions.
The Analytical DELTA
Companies that continue to manage on auto-pilot—by having sales seminars because that’s what’s always been done, for example—are not competing as effectively as they could be. Often overlooked is that becoming more analytical is not solely the responsibility of a manager, it’s an essential concern for the entire organization.
What does it take to put the focus on analytics across the organization? What capabilities and assets do you need in order to succeed with analytics initiatives? Below are the success factors, which we group under the acronym DELTA—the Greek letter (depicted as a triangle) 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.
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:
- Major analytics applications, those that really improve performance and competitiveness, invariably touch multiple parts of the enterprise.
- If your applications are cross-functional, it doesn’t make sense to manage your key resources—data, analysts, and technology—locally.
Without an enterprise perspective, chances are you’ll have many small analytical initiatives but few, if any, significant ones.
Organizations that really are capitalizing 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.
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 real 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.
The final element, 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 business people to appreciate and apply them. 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 basic resources of data or analysts aren’t ready. You also can overspend on getting one element—typically data—ready, and then have it sit dormant because none of the other ingredients are in place.
Getting Started and Building Momentum
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, think about progress in terms of five stages:
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.
Stage 3: 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. 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.
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.
*Accenture survey of 254 U.S. managers—see Most U.S. Companies Say Business Analytics Still Future Goal, Not Present Reality, Accenture press release, Dec. 11, 2008.