English to Greek Other IT (Information Technology) management, big data

Even after making a considerable investment in a new pricing tool, one airline found that the productivity of its revenue-management analysts was still below expectations. The problem? The tool was too complex to be useful. A different problem arose at a health insurer: doctors rejected a Web application designed to nudge them toward more cost-effective treatments. The doctors said they would use it only if it offered, for certain illnesses, treatment options they considered important for maintaining the trust of patients.

Problems like these arise when companies neglect a third element of big-data planning: engaging the organization. As we said when describing the basic elements of a big-data plan, the process starts with the creation of analytic models that frontline managers can understand. The models should be linked to easy-to-use decision-support tools—call them killer tools—and to processes that let managers apply their own experience and judgment to the outputs of models. While a few analytic approaches (such as basic sales forecasting) are automatic and require limited frontline engagement, the lion’s share will fail without strong managerial support.

The aforementioned airline redesigned the software interface of its pricing tool to include only 10 to 15 rule-driven archetypes covering the competitive and capacity-utilization situations on major routes. Similarly, at a retailer, a red flag alerts merchandise buyers when a competitor’s Internet site prices goods below the retailer’s levels and allows the buyers to decide on a response. At another retailer, managers now have tablet displays predicting the number of store clerks needed each hour of the day given historical sales data, the weather outlook, and planned special promotions.

But planning for the creation of such worker-friendly tools is just the beginning. It’s also important to focus on the new organizational skills needed for effective implementation. Far too many companies believe that 95 percent of their data and analytics investments should be in data and modeling. But unless they develop the skills and training of frontline managers, many of whom don’t have strong analytics backgrounds, those investments won’t deliver. A good rule of thumb for planning purposes is a 50–50 ratio of data and modeling to training.

Part of that investment may go toward installing “bimodal” managers who both understand the business well and have a sufficient knowledge of how to use data and tools to make better, more analytics-infused decisions. Where this skill set exists, managers will of course want to draw on it. Companies may also have to create incentives that pull key business players with analytic strengths into data-leadership roles and then encourage the cross-pollination of ideas among departments. One parcel-freight company found pockets of analytical talent trapped in siloed units and united these employees in a centralized hub that contracts out its services across the organization.

When a plan is in place, execution becomes easier: integrating data, initiating pilot projects, and creating new tools and training efforts occur in the context of a clear vision for driving business value—a vision that’s unlikely to run into funding problems or organizational opposition. Over time, of course, the initial plan will get adjusted. Indeed, one key benefit of big data and analytics is that you can learn things about your business that you simply could not see 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 into a more dynamic one that takes place continually and involves a broader set of constituents.4 Data and analytics plans are also too important to be left on a shelf. But that’s tomorrow’s problem; right now, such plans aren’t even being created. The sooner executives change that, the more likely they are to make data a real source of competitive advantage for their organizations.