Executives are beginning to appreciate that the returns from big data are real indeed. But that doesn't make moving forward any easier. The investment, both in dollars and management commitment, can be large. CIOs stress the need to entirely remake data architecture and applications. Outside vendors hawk the power of black-box models to crunch through unstructured data in search of cause and effect relationships. Business managers scratch their heads — while insisting on knowing upfront what the pay-off will be from the spending and the potentially disruptive organizational change.
The answer, simply put, is to develop a plan. It may sound obvious, but in our experience, most companies fail to put in the time required to create a simple plan for how data, analytics, front-line tools, and people can come together to create business value. The power of a plan is that it provides a common language that allows senior executives, technology professionals, data scientists, and managers to talk through where the greatest returns will come from, and more importantly, select the two or three places to get started.
There’s a parallel here with the history of strategic planning. Forty years ago, only a few companies developed well-thought-out strategic plans. Some of those pioneers achieved impressive results, and before long a wide range of organizations were harnessing the then-new planning tools and frameworks. Today hardly anyone sets off without some kind of strategic plan. So too, we believe developing a data and analytics plan soon will be seen by most executives as the essential first step toward harnessing big data.
A good strategic plan highlights the critical decisions, or “trade-offs,” that a company needs to make, and defines high priority initiatives: what businesses will get the most capital, whether to emphasize higher margins or faster growth, and what capabilities are needed to ensure strong performance. In these early days of big data and analytics planning, companies need to address analogous issues: choosing the internal and external data they will integrate, selecting from a long list of potential analytic models and tools the ones that will best support their business goals, and building the organizational capabilities needed to exploit this potential. Successfully wrestling with these planning tradeoffs requires a cross-cutting, strategic dialogue at the top of the company that will build high-level confidence in the plan, which should focus on three core elements:
Data. A blueprint for assembling and integrating data is essential. Companies are buried in information that’s frequently siloed by business unit or function. Some plans may highlight the need for massive reorganization of data architectures over time, as well as large investments in new data capabilities to incorporate information from outside the company such as social network conversations.
Analytic Models. Integrating data alone does not generate value. Advanced analytic models are needed to enable data-driven optimization (of employee schedules or shipping networks, for example), or prediction (for instance, about what customers will want or do based on buying histories, weather, or Web site behavior). Critically, a plan must identify where models will create additional business value, who will need to use them, and how to avoid inconsistencies and unnecessary proliferation as models are scaled up across the enterprise.
Tools. Modeling output will only be valuable if managers and, in many cases, front-line employees understand and use it. What’s needed are intuitive tools that integrate data into day-to-day processes and translate modeling output into tangible business actions. To animate this transformation companies will need new organizational capabilities. In the same way that some strategic plans fail to deliver because the organization lacks the skills to deliver on them, so big data plans can disappoint when they lack the right people and capabilities. Companies need a road map for assembling a talent pool of the right size and mix.
By assembling these three building blocks, companies can formulate an integrated big data plan — which, of course, will get adjusted as big data and analytics reveal business elements not visible before. Here, too, there may be a parallel with strategic planning, which over time has morphed in many organizations from a formal, annual, “by the book” process, to a more dynamic one that takes place continuously and involves a broader set of constituents. A data and analytics plan, in similar fashion, should engage the entire enterprise. The sooner executives understand this, the more likely they are to make data a real source of competitive advantage for their organizations.