3 Things Are Holding Back Your Analytics, and Technology Isn’t One of Them
During the past decade, business analytics platforms have evolved from supporting IT and finance functions to enabling business users across the enterprise. But many firms find themselves struggling to take advantage of its promise. We’ve found three main obstacles to realizing analytics’ full value, and all of them are related to people, not technology: the organization’s structure, culture, and approach to problem solving.
Structurally, analytics departments can range between two opposite but equally challenging extremes. On the one hand are data science groups that are too independent of the business. These tend to produce impressive and complex models that prove few actionable insights.
Consider the experience of one retail financial services firm. There, the analytics function was comprised of employees who used specialized software packages exclusively and specified complicated functional forms whenever possible. At the same time, the group eschewed traditional business norms such as checking in with clients, presenting results graphically, explaining analytic results in the context of the business, and connecting complex findings to conventional wisdom. The result was an isolated department that business partners viewed as unresponsive, unreliable, and not to be trusted with critical initiatives.
On the other hand, analysts who are too deeply embedded in business functions tend to be biased toward the status quo or leadership’s thinking. At a leading rental car agency, for instance, we watched fleet team analysts present intelligence purportedly showing that the fleet should skew toward newer cars. Lower maintenance costs more than compensated for the higher depreciation costs, they said. This aligned with the fleet vice president’s preference for a younger fleet.
But it turned out that the analysts had selected a biased sample of older cars with higher-than-average maintenance costs among cars of the same age. An analysis of an unbiased sample (or the entire population) would have yielded a different result. (Of course there might have been other motivations to keep a younger fleet—customer satisfaction and brand perception, to name two—but cost reduction was not one of them.)
Culturally, organizations that are too data-driven (yes, they exist) will blindly follow the implications of flawed models even if they defy common sense or run counter to business goals. That’s what happened at a financial services firm where management was mulling a change to its commission structure. They wanted to switch the basis of its salesforce compensation from raw results to performance relative to the potential of each salesperson’s market.
In response, analysts developed an admirable data envelopment model. The model simultaneously compared sales of different types of products with local demographic and financial statistics to come up with a single efficiency measure for each salesperson relative to their peers. Indeed, this seemed to have made compensation more equitable. But it reduced the compensation of salespeople who were less efficient but ultimately more valuable—causing them to defect to competitors.
Alternatively, organizations that rely too heavily on gut instinct resist adjusting their assumptions even when the data clearly indicates that those assumptions are wrong. The aforementioned rental car agency, for example, was extremely reluctant to change course even after discovering that the data didn’t support their cost reduction claims.