Building frontline capabilities
The sophisticated analytics solutions that statisticians and scientists devise must be embedded in frontline tools so simple and engaging that managers and frontline employees will be eager to use them daily. The scale and scope of this adoption effort — which must also involve formal training, on-the-job coaching, and metrics that clearly define progress — shouldn’t be downplayed. In our experience, many companies spend 90 percent of their investment on building models and only 10 percent on frontline usage, when, in fact, closer to half of the analytics investment should go to the front lines.
Here, again, we have seen plenty of cases where no one on the top team assumed responsibility for sustained ground-level change. Lacking senior accountability and engagement, one financial-services company weathered several waves of analytics investment and interest only to have efforts fizzle when training and adoption fell short. Dismayed, business-unit leaders then took charge, investing in ongoing training sessions for managers and end users, pushing for the constant refinement of analytics tools, and tracking tool usage with new metrics. Over time, thanks to the consistent application of analytics, the transformation effort gained the hoped-for momentum.
Putting leadership capacity where it’s needed
As companies size up these challenges, most will concede that they need to add executive capacity. But that leaves unanswered important decisions about where, exactly, new roles will be located and how new lines of authority will be drawn. As we’ll outline below, our experience shows that companies can make a strong case for leading their data-analytics strategies and talent centrally or even for establishing a formal data-analytics center of excellence. However, frontline activities (mobilizing resources, building capabilities) will need to take place at the business-unit or functional level, for two reasons. First, the priorities for using data analytics to increase revenues and productivity will differ by business. Second, and just as important, companies best catalyze frontline change when they connect it with core operations and management priorities and reinforce it with clear metrics and targets.
Beyond this bias for pushing frontline mobilization responsibility to business units, there is no single prescription for where and how a company should add leadership capacity. Given the relative immaturity of data-analytics applications, that shouldn’t be surprising. Yet as leaders review their options, they needn’t fly blind. Pushing for answers to three key questions, in our experience, brings strategic clarity to the needed organizational changes:
- Will a central customer or operational database be used across business units?
- Is there a compelling need to build substantial analytics resources internally to retain talent and build proprietary assets and advantages?
- Within each business unit, can the current functional executives handle the change-management challenge or should the company dedicate new executive capacity specifically for the data-analytics change effort?
We’ll illustrate the importance of these issues through examples of companies that have addressed them in different ways.