Companies are familiar with the idea of descriptive analytics—using data about past decisions to improve business processes—and the promise of predictive analytics, which focuses on determining likely outcome and response based on currently available information. Somewhere between these two, is an emerging force called prescriptive analytics—a recent Information Age article calls “the next stage of analytics maturity in which you make decisions based not only on individual predictions, but also using an aggregated view of predicted relationships.”
The result? Recommendations to make the best decision in order to maximize future opportunity.
The challenge? As with any prescription: finding the right dose.
Faster Reaction
According to CIO, predictive tools are the next step in analytics. With data scientists spending three-quarters of their time prepping data sets and just one quarter performing analyses, it’s easy to get bogged down as the sheer volume of information ramps up. And since the goal of predictive solutions is to give insight on both long-term and immediate business opportunities, there is a real need for better infrastructure and integration spending—data must be collected, verified, and analyzed in near real-time to have prescriptive value.
Striking a Balance
A February 10th article from Forbes, meanwhile, discusses the newest frontier for predictive analytics: automation. In fact, industry expert Tom Davenport has suggested that automated analytics is the natural extension of a prescriptive model, potentially eliminating the need for human interaction. Here, finding the right dose means giving automated processes the freedom to handle basic analytics tasks, while reserving higher-level decision making for human experts. It’s a trial-and-error process to some extent, since companies won’t know where the ideal balance lies until they’ve tested a few data sets and implemented the results.
Generic versus Brand-Name
The final key to proper dosage?
Making the choice between building in-house expertise and opting for an outside expert’s help. According to Gartner analyst Lisa Kart, “When people are getting started in new areas, it makes a lot of sense to call in subject matter experts.” She notes that the most important factor in success is working closely with decision-makers, but trying to do everything in-house may be a daunting task.
Bottom line? Prescriptive analytics are the next logical step in leveraging optimal value out of Big Data. By investing in speed, striking a balance between human supervision, and automation along with in-house versus outsourcing, it certainly seems more plausible for organizations to tap into a new kind of business intelligence.
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