What Does It Take for Analytics to Truly Become Pervasive?

With the end of the year coming up fast, I like to reflect on some of the year’s top trends and key messages. One that’s really stuck out to me this year is the emphasis that’s been placed on pervasive analytics or “analytics everywhere.” It’s been a major discussion point for both Gartner and Forrester, and the launch of GoodData Spectrum earlier this year was spurred by our own desire to make analytics more intuitive, in context and easily deployed throughout an organization. By making analytics pervasive, business-critical decisions can be easily made, thereby improving the bottom line. In spite of this, it seems to me that many people still aren’t grasping the importance of analytics becoming truly pervasive for the entire organization — from the C-suite to front-line employees.

What does “pervasive analytics” mean?

What I mean by “pervasive analytics” is analytics that’s used to support every business decision by every employee in every department in a given organization. It’s pretty self-explanatory, but it’s also a surprisingly difficult concept for most companies to grasp. There’s still this perception that analytics should be reserved for the C-suite or for a few select departments and processes, even though we know that extending the use of analytics to the rest of the organization can deliver 30% annual company growth rate and an estimated $1.8 trillion in earnings by 2021. But for companies to achieve these kinds of returns, they need to first put in the work.

Embed analytics in every process

First, companies need to commit to embedding analytics into all areas of the business, not just certain levels or departments. If the C-suite can find analytics useful for high-level strategic decisions, then so can front-line employees who are making tactical decisions on a daily — or hourly — basis. Start by thinking about what your business problems are or what business outcomes you’d like to achieve. Then find the processes or use cases where analytics can make an immediate improvement and get you closer to that business outcome. Maybe you’re an insurance company looking to more rapidly accept or reject a claim, or maybe your sales and marketing teams want to quickly determine what to offer a client who may be flagged as close to churning. By embedding analytics into those processes first, it’s easier to see how analytics can add value elsewhere and expand from there.

Ensure the analytics experience is seamless

Of course, pervasive analytics is about more than just access to analytics. Access alone can’t help users to understand what they’re looking at or figure out what to do with what they’re presented. For analytics to truly be considered pervasive, then it needs to be so seamlessly integrated into the workflow process that users don’t even realize that they’re using analytics.

This may sound counter-intuitive. How could users take action based on a certain insight or recommendation but not even realize that they’re doing so? But many analytics companies — GoodData included — are putting a tremendous amount of work into making analytics actionable, intuitive to use, and presented in context so the user’s work process is supported, rather than interrupted.

Over the next seven years, Nucleus Research predicts that 90% of business users will interact with analytics at least once per day but only 15% will realize it. It’s a mythical sounding point to reach. And Gartner agrees, predicting that analytics will become more pervasive and less visible in the near future.

Once we reach this point, I think we’ll see that analytics has finally become truly pervasive, its benefits extending to every level in an organization and to every department and business function. If you’re interested in preparing yourself or your company for this eventuality, I highly recommend listening to the webinar, The Future of Embedded Analytics is Contextual, Actionable and Pervasive: What You Need to Know to learn more about the next generation of predictive — and pervasive — analytics and analytical applications.