Understanding Descriptive vs. Predictive vs. Prescriptive Analytics

descriptive vs predictive vs prescriptive analytics

Data analytics is at the center of corporate strategy today. A recent survey from analytics software firm MicroStrategy reveals just under 95% of professionals believe “data and analytics are important to their business growth and digital transformation.” While it’s clear people see data as an important part of the equation, there’s more depth to the issue than that.

Thanks to the advent of advanced analytics, there are now more sophisticated tools out there for helping organizations get the most from their data. These allow for enterprises to glean far more valuable information than they could have gotten from analytics in the past. When it comes to advanced analytics, it’s important to understand the differences between descriptive, predictive, and prescriptive analytics.

What Is Descriptive Analytics?

Think about what it means to describe something. It’s like telling a story or recounting events. That’s exactly what you’re doing when you use descriptive analytics—using data to paint a picture of what happened already.

There are several ways descriptive analytics can prove valuable to organizations. Real-time analytics is one broad kind of descriptive analytics that has clear utility for enterprises of all kinds. For instance, a logistics company can track shipping volume in real-time to determine where to prioritize resources. Descriptive analytics also work over the longer term, as they can point toward seasonal trends, or just show any kind of historical pattern.

Another element of descriptive analytics is diagnostic analytics, which basically takes things to the next level. Where descriptive analytics tells you there are more packages being sent than normal, diagnostic analytics can tell you it’s due to a regional influx due to a little-known holiday. Being able to harness descriptive analytics is the first step to extracting value from your data. But there’s still much more that can be done beyond that.

What Is Predictive Analytics?

While diagnostic analytics is basically using technology to parse through data far faster than it would be possible to do by hand, predictive analytics takes things to another level. This is because predictive analytics does more than just tell you what happened. It can actually give you a forecast of what’s to come.

This predictive power is one powerful ability enterprises are harnessing thanks to advanced analytics. These are types of analytics programs that use cutting-edge technology to provide more comprehensive capabilities. Predictive analytics is a prime example of advanced analytics, as it utilizes machine learning to give insights about the future based on past data.

Predictive analytics provides another layer of material for making corporate decisions. There’s a clear advantage to being able to model future outcomes this way. Though, there are still even deeper forms of analytics.

What Is Prescriptive Analytics?

As diagnostic analytics enhances descriptive analytics, prescriptive analytics does the same for predictive analytics. Although that sentence might read like a bit of a mouthful, there’s truth to the analogy. Prescriptive analytics not only maps out the future, but it also offers suggestions for how to deal with it.

Much like how your doctor prescribes your medicine based on illness, prescriptive analytics recommends actions based on quantifiable inputs. One of the great benefits of prescriptive analytics is it allows business leaders to drill down into the specifics of making alternative decisions. You’ll want to have as much information at your disposal as possible before making a major move that could have massive financial implications. Enterprises that need to regularly manage large decision-based risk profiles need to consider the benefits of adopting prescriptive analytics into their toolboxes.

Of course, each individual platform and provider of these analytics types are going to be different. It’s important for organizations to take stock of what they need from business intelligence tools before going ahead with a purchase. It’s also essential to model return on investment before pulling the trigger on anything. With the better platforms, however, there are ample opportunities for getting strong ROI from all these types of analytics.

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