Advances in technologies like artificial intelligence (AI) and machine learning are generating more data than ever before. And as more organizations pursue digital transformation initiatives, the more data they will have on their hands.
Companies aren't generating data for data's sake, though. This data can help them make critical business decisions. However, to do so, the data must be successfully analyzed and interpreted, which is why the demand for data scientists has skyrocketed in recent years, and why data scientist is the most promising job of 2019.
SEE: Data analytics: A guide for business leaders (free PDF) (TechRepublic)
While data scientists are helpful in interpreting the data, the real value lies in data analytics software. For data scientists to be useful, they must be equipped with the correct data analytics tools and programs.
Organizations can analyze their data in a number of ways, according to Carlie Idoine, senior director and analyst for Gartner. The four main types of analytics all serve different purposes, leveraging the data in different ways, she said.
"The first two are descriptive and diagnostic, which are more rear-view mirror looking; they tell you what happened and why it happened. The other two types are predictive—which, obviously, based on the name is what will happen next—and prescriptive, which is a set of analytical capabilities that actually specify a course of action. The last two are more forward-thinking."
Prescriptive analytics is the final stage in the business analytics process, and the most recent. It's not to be confused with predictive analytics, which it often is, said Jim Hare, vice president analyst at Gartner.
"Predictive addresses the question of what is likely to happen. It relies on techniques like predictive modeling, regression analysis, forecasting, and more," Hare said. "Whereas prescriptive analytics addresses the question of what should be done. What can we do to make a sort of thing happen? It relies on techniques such as graph analysis, simulation, optimization techniques, and recommendation engines."
To help businesses understand this latest step in business analytics, here is everything an organization should know when considering employing prescriptive analytics solutions.
How prescriptive analytics helps make business decisions
Prescriptive analytics equips organizations with their next steps. While other analytics can pinpoint what an organization's issues are, prescriptive analytics prescribes organizations' solutions, said Mike Gualtieri, vice president and principal analyst at Forrester.
"It's actually all about the business decision," Idoine said. "There are a couple ways to think of prescriptive analytics. One is based on particular rules: What decisions do we make? The other is based on optimization. For example, looking at someone's credit risk and determining what the optimal price is that I should charge them for insurance. Or if I can predict equipment failure I can then use prescriptive analytics to schedule preemptive maintenance."
Recommendations given by prescriptive analytics programs may or may not require a human to be involved, Hare said, leaving humans available for other lucrative tasks.
"In some cases, the prescriptive recommendation is something the human can look at and evaluate, meaning there's also some background information as to what the key drivers were in influencing that recommendation," Hare said. "In other cases, it may not even require a human in the loop. You could use the output from the prescriptive analytics solution to automatically take corrective action or kick off the business process. Especially in cases where it's time critical; it's life or death; it's something where you can't wait for a human to make a decision."
Most popular vendors
Some of the most well-known prescriptive analytics platforms include AIMMS, Decision Lens, FICO, Gurobi Optimization, SAS Institute, River Logic, and Sparkling Logic, according to Hare, but there is a slew of other vendors in the market.
Both Gualtieri and Idoine mentioned IBM, a company that has long been at the forefront of digital transformation initiatives, including analytics solutions, cloud migration, and quantum computing. The company actually coined the term prescriptive analytics in a 2010 article for Analytics Magazine.
How to choose a platform
With hundreds of prescriptive analytics providers on the market, organizations may have trouble deciding which one is best. However, the decision comes down to the business' needs, Idoine said.
"More advanced analytic platforms that have historically focused more on machine learning, and predictive analytics are starting to include some of the prescriptive capabilities within those platforms," she said. Instead of looking for new tools, companies should look at the programs or vendors they currently work with and see if they have prescriptive analytics capabilities.
If companies need to look for a new vendor or start with an analytics vendor, there are a few key components to consider, Idoine said.
"Make sure that they're looking at the front end piece. Can I get the data I need and can I get that data in the [right] format? Then does [the platform] give me the breadth and depth of analytics capability I need specifically for my business problem? Thirdly, does it give me a way to easily embed and then manage [the data] over time?"
For more, check out TechRepublic's prescriptive analytics cheat sheet.
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