Over the past five years, the world has witnessed an unprecedented explosion of digitized data, which is often referred to as “big data.” Its potential remains alluring—and largely untapped. The data opportunity is like a diamond mine, mostly littered with rocks and dirt, but with enough gems peeking out to attract those unafraid of the hard work needed to sift through it.
Unlocking the potential inherent in all this data, structured and unstructured, internal and external, demands that companies set clear goals for how the data will be used—whether for optimizing the supply chain, developing closer relationships with customers and partners, predicting and reacting quickly to market shifts, or identifying areas for operational improvements and better accuracy of reporting. And they must decide on how it is to be collected, analyzed, and acted on.
But setting such priorities is not enough. To succeed in this complex endeavor, companies must also define the right organizational structure for managing the data effort—one that can effectively align the demands of the business with the technological requirements needed to support those demands. That’s a tough challenge that requires unprecedented cooperation across traditional functional and business unit boundaries.
Given the cross-functional challenges, rich potential, and inherent dependency on complex technology, the question of who should own the data has become a hotly contested topic. In an online survey of more than 500 business intelligence professionals, respondents were evenly divided among three possible scenarios—that IT should take the lead, that the business should, or that a matrix organization should be created, bringing together the expertise of both (see Exhibit 1). (There is, of course, a fourth possibility—that the company has no clear data strategy at all—an arrangement to be found at far too many companies.)
1. IT should lead. The IT department seems an obvious choice for driving a company’s big data efforts. It is best suited to ensure compliance with enterprise architecture, consistency of tool selection, the proper use of technical resources, and overall operational efficiency. The IT-led setup has a number of potential pitfalls, however. All too often, IT organizations lack the level of familiarity with business strategy and operations required to prioritize and address critical business needs. As a result, companies can lose sight of the business motives driving the effort and instead get bogged down in trying to find the perfect technology solution.
2. The business should lead. Giving authority for data efforts to a functional business group such as finance or marketing can ensure a higher degree of alignment with business priorities and increase the likelihood that the needs of individual business units can be met quickly. But such teams may not understand—and thus may not be able to leverage—the IT assets and capabilities the company possesses or be sufficiently familiar with new data-related technologies. As a result, companies may find themselves building analytical systems that are not architecturally sound, that reproduce capabilities already existing elsewhere in the organization, or that meet the needs of just one business or functional unit.
3. The matrix. Both of the first two models have their advantages, and for some companies one of them might be the way to go—perhaps because of specific cultural or historical circumstances, or because of the nature of their industry. But most companies will find that the best approach combines the strengths of each into a matrix organization, whose senior leader represents both business and IT stakeholders and is responsible for developing data capabilities through a coherent company-wide strategy.
Under this arrangement, people from business functions and IT come together to define specific data capabilities required to achieve the company’s business goals. Those from the business function help with identifying requirements, prioritizing analytics requests from various business units, and formulating questions the data can help answer. Meanwhile, those from IT help define the most effective ways of sourcing the data, while ensuring its relevance, availability, accuracy, and adherence to an architectural vision. IT also takes on the role of thought leader and trusted advisor in determining whether a new tool or architectural enhancement is required to effectively accommodate new information requests.