For Organizations New to Predictive Analytics, Bet on a Process and Not on a Project
Successful projects using data mining algorithms often have returns that are well above normal business Returns on Investment. 200%, 500% or even 1000%+ ROIs are not unheard of. So why isn’t there a rush to do more Proof of Concept projects? Having designed and pitched dozens of these, I have concluded that it’s largely because of corporate politics. I’m not using the term “corporate politics” in a pejorative sense, but rather as a fact of life that results from normal business structures and organizational dynamics. So perhaps the question is not “how do we fight better to get funding for new data mining projects?”, but rather it’s “how do we lower the political risk associated with Proof of Concept projects using data mining algorithms and predictive analytic techniques?” We recently did a program with a client which did exactly that.
Think like a venture capitalist. Place a large number of small bets rather than one or two large bets. We recently led a predictive analytics summit workshop at a client’s headquarters where representatives of different functional areas of the corporation were invited to bring their own data for a day-and-a-half of intense data discovery using advanced data visualization and predictive analytics tools (Oracle Data Visualization Desktop and OBIEE 12c). There were roughly 40 participants from groups as diverse as sales, service, finance, operations, and marketing. Everyone sat at round tables organized by function in one large room (a table for sales, a table for finance, etc.). The participants were largely comprised of the business intelligence system’s “power users”. A few had engineering or technical backgrounds that gave them a leg up in statistics and analytics, but most were just smart people interested in their business and data analysis. We also had plenty of help on hand including representatives from the IT staff who could solve potential data connection or data structure issues, a couple data scientists who knew statistics cold, some experts in the software interface who could help with “where do I click?” questions, and some support staff who made sure that all the conference logistics were handled.
The results were incredible. One “discovered” insight provided the >1000% ROI that qualifies as a “home run”. The workshop participant made an appointment with his VP to share the insight and its visualization for the next business day (why wait?). In totality, more than 30 projects were identified as potentially significant areas for future work and development. Was it all sunshine and unicorns? Not at all. Some people struggled to get their data sets fully prepped for predictive analytic processes.
