Until now, businesses have used data analytics tools to make sense of where we’ve been, and even predict where we’re going. But what if we could use big data to tell us how to best to get there?

Since data analytics products for both descriptive and predictive applications began to proliferate, software developers have had their eye on the prize: prescriptive analytics—parsing data in ways that will offer the best solutions.

At its most basic, prescriptive analytics finds the best course of action for a given situation. It’s the third and final (so far) phase of business analytics.

In some ways, prescriptive analytics is not that dissimilar to predictive analytics, which has generated some debate in the tech and business communities about its precise definition. But prescriptive analytics goes beyond forecasting options to suggest actions and their potential outcome.

One example of the use of prescriptive analytics is in autonomous vehicles. A self-driving car must make millions of calculations based on analyzed data to decide when to turn, change lanes, and so on.

We’re also starting to see the application of prescriptive analytics in other industries, such as the oil and gas industry and healthcare. As the technology progresses, we’ll see prescriptive functions and decision optimization solutions creeping into more widely adopted analytics tools in 2020.

Prescriptive analytics in business

The oil and gas industry is already starting to see the usefulness of prescriptive analytics. With information drawn from analytics tools, both predictive and prescriptive, the sector is assessing supply, demand, pricing and impacts as they change.

Prescriptive analytics tools such as image processing, machine learning, and integrated analysis of unstructured data can be used to find and produce petroleum. By analyzing images from well logs, seismic reports, video and image feeds, as well as audio recordings, text notes, and numeric information, machine learning and computer vision can recognize patterns and figure out where to drill—and where not to. 

Beyond that, prescriptive analytics can create a holistic, integrated view of data collected from in-field production equipment, making failures of pumps and other machinery easier to predict, while also prescribing mitigating actions to cut down on production losses.

In healthcare, prescriptive analytics can not only suggest the best course of action for patients or providers but also compare the outcomes of multiple “what if” scenarios.

IBM’s Product Marketing Manager, Sajan Kuttappa, describes a scenario in which a health insurer sees a pattern that shows “a significant portion of its diabetic patient population also suffers from retinopathy.” The insurer then uses predictive analytics to estimate the probability of an increase in ophthalmology claims during the next plan year, then prescriptive analytics to model out the cost impact. The cost impact is based on whether average ophthalmology reimbursement rates increase, decrease, or remain the same. A course of action is then recommended.

In retail and Consumer Packaged Goods (CPG) companies, prescriptive analytics is a useful communications tool.

As Guy Yehiav writes at business2community.com, prescriptive analytics can be used to bridge the communication gap between e-commerce and brick-and-mortar operations. Drawing on insights from prescriptive analytics, a retailer can alert both its in-store and e-commerce channels to each other’s significant sales trends. By providing a retailer with info from online sales, such as a list of items commonly purchased together online but rarely bought together in stores, like yoga mats and water bottles, merchandisers can place these items closer together on store shelves.

Analytics and beyond

Information gleaned from prescriptive analytics can also be used to eliminate bias in different retail functions and identify cross-training opportunities across retail operations.

It’s still early days, and we are just beginning to see all the uses of prescriptive analytics. But as we collect more and more data, the challenge will be not only to analyze and predict based on this information but to find new and better solutions. Prescriptive analytics is bound to be a valuable tool in this new era of data collection and analysis.