How to build a data-driven organization that outpaces competitors
The age of big data has opened possibilities for a data-driven organization like no other time in history. Organizations that have paved the way and tools that make powerful analysis easy make the move to a data-driven culture much simpler than it was just a decade ago. However, it still takes a focused effort to design the right organization if you hope to use your future analytic capability to drive a competitive advantage. Let’s look at the organizational design elements of a data-driven company that’s poised to outpace the competition.
It all starts with the corporate strategy, which is why it’s important for the CEO to own the transformation—he or she is the only one who’s accountable for your company‘s vision and purpose. Regardless of the products and services your company offers to its customers, the CEO must make it very clear that the future of the company will be powered by data.
It’s also important for the CEO to establish why the company needs to shift to a data-driven culture. One reason is universal to everyone in today’s global competitive environment (i.e., without some degree of analytic capability, there’s no way to be competitive), but there must be other compelling reasons why it’s imperative for your organization to be more analytic. The CEO must be crystal clear about what these reasons are before structuring the organization for success.
The ideal structure for a data-driven organization has a Chief Analytics Officer (CAO) or equivalent reporting to the CEO—this sends a strong message that big data analytics plays a strategic role in the organization. Remember that the CEO must retain ownership of big data analytics at the company, so the CAO is not a position for the CEO to delegate all accountability; instead, the CAO should be the chief advisor to the CEO and an integral member of the strategic team.
Under the CAO should be a robust organization of data science. An appropriate top-level split for a data science organization is qualitative and quantitative functions. Qualitative data science is where you house your capability for exploratory data analysis, while quantitative data science is where you build strength in testing hypotheses. This is the most effective way to empower the business units.
Good business processes are what brings great analytic capabilities to the business units. In addition to analytics, there will be several functions (Finance, HR, etc.) that sit under the CEO, and there will also be business units. Since the business units reside in a different area than the analytics function, you must build cross-functional (i.e., lateral) processes to bring the power of analytics where they can provide the most benefit.
