I’ve written before on the importance of people sharing data as broadly as possible in an organization. A company may have hundreds, or even thousands, of people with “analyst” in their job titles, but if you keep access to data centralized, you’re also keeping the capacity for innovation centralized — and limited.
At the same time, simply opening up the data floodgates to your workforce, without a solid strategy, will create more confusion than success. The better approach is to package up the complexity into accessible tools — analytical applications — to leverage the most valuable asset of all in your workforce: curiosity. In other words, business analysts need an environment to follow that spark of curiosity on data issues (“Hmm, there’s a spike over here … an aberration over there. Let’s experiment a little to figure out why.) without having to know all the complex processes happening underneath the application.
Expanding Insight Across the Organization
Such an “Analytical Application Platform” — as the Northwestern Kellogg School of Management’s Mohan Sawhney and I call it in our Sentient Enterprise model for big data agility — is a self-serve, on-demand environment for users to follow their hunches. Put more pragmatically, as the chief data officer at a major international bank recently told me, analytical apps help “people to stop submitting tickets and get them to start thinking more analytically on their own. A business manager needs to understand the levers to pull, but not necessarily know all the engineering.”
This executive is absolutely right. And in the process of building analytical apps, we’re not just expanding data access — we’re also expanding the company’s seedbed for analytic insight and value. When designed and implemented correctly, analytic applications can foster curiosity in the user community and provide a framework for that curiosity to lead to scalable solutions.
This framework holds up even in the face of tough and demanding business challenges. I know of cases where analytical applications have taken CRM sentiment data — not all of it positive — to fuel analytic solutions allowing companies to anticipate consumer concerns more proactively and thereby boost overall customer satisfaction. In fact, critical business challenges are often where analytics leaders find valuable internal partners — lighthouse customers, as I’ve come to call them — who welcome the assistance to create solutions and, ultimately, showcase for the rest of the company the value of agile analytics.
Supporting Analytic Success
Keep in mind that, just as we shouldn’t simply open up the data floodgates to your company without a strategy, neither should we simply crank out analytical apps without some support systems in place. While it’s important to keep bureaucracy to a minimum, analytical apps do need to operate within a certain organizational framework.
The banking executive I mentioned, for instance, has a system for “insight governance.” This involves deploying relationship managers to gauge analytics needs within the organization and coordinate quick development of the apps, as well as super users within the various company divisions to help ensure ongoing and self-service functioning of those apps. Advanced data scientists are always available to help when roadblocks or deeper analytic challenges arise; the beauty of the system is the workload for these experts is naturally skewed toward these tougher challenges, where they’re needed most!
You’ll likely find that some departments and job functions may be more receptive than others: Mobile and online divisions, for instance, tend to be familiar already with the value of data. Marketing departments also typically lend themselves to analytic solutions to help with intensive, time-boxed product campaigns that often have clear measures of success. Adoption might be a bit more difficult, however, for an HR, legal or physical asset management department. But even in these areas, the value of analytics becomes clear once you demonstrate, for example, digital analysis of server usage shows how to optimize load more efficiently and save the company money.
By now, it’s probably clear that fostering both access and curiosity around data has to do not just with technology, but also corporate culture. Fortunately, the insight governance measures I mentioned earlier can help people share expertise more easily within the company. Another important way to promote culture is to recruit talent who are data-literate from their previous training or work experience and already “get” the value of analytics. All of these steps, together with cutting-edge technology, contribute to an environment of analytic curiosity that can help any given insight grow from interesting finding to a significant trend to that big cost-saving or profit-generating solution!
For more on the Sentient Enterprise, watch my video overview on our website.