Prescriptive analytics is nothing short of automating your business. But there’s lots of hard work, and many stages you need to go through before you get there.

The first rule of prescriptive analytics is that you do not talk about prescriptive analytics—not before you’ve paid your dues in descriptive, diagnostic, and predictive analytics. That’s not to say prescriptive analytics is not real, or does not have benefits. It does. But getting there won’t come at the push of a button.

To see why, and what you need to do, we start by revisiting what prescriptive analytics is and go through a journey in the realm of analytics with a little help from the Gartners and Forresters of this world.

Analytics is the use of data, and techniques to analyze data, to get better insights and eventually make better decisions. Analyst firm Gartner introduced an analytics maturity model to reflect the fact that not all analytics techniques are born equal, and there is a progression in what you can achieve.

There is a chain of evolution in analytics, ranging from descriptive to diagnostic to predictive, and culminating with prescriptive, according to Gartner’s classification.

Many organizations are still in the descriptive stage, utilizing more or less traditional business intelligence (BI) approaches: Get all your data together and use visualization to obtain quick views on what has happened.

Diagnostic analytics is about figuring out why an event happened and uses techniques such as drill-down, data discovery, data mining, and correlations. Most analytics frameworks have been incorporating such features in their offerings.

Where things get really interesting is when using predictive analytics to project what will happen. Typically this is done by using existing data to train predictive machine learning (ML) models.

Prescriptive analytics is the final stage in the analytics evolutionary path, with the ultimate goal being to provide ways of making certain outcomes happen.

In other words, predictive analytics tells us the likelihood of something happening, given current status on the basis of interpreting the data we have. Prescriptive analytics goes one step beyond and tells us what we need to do to make something happen. Prescriptive analytics draws a path on how to go from where we are to where we want to go. Let’s pick an example to see this in practice.

A water utility company may start out its journey in analytics by deploying sensors in its distribution network and laying out the data infrastructure needed to feed that data to the appropriate databases–operational and analytical.

By making sure that data is sent to the operational database at all times, and replicated to the analytical database, the company will be able to see the status of its network in real time. This is descriptive analytics.

By connecting the analytical database to a software solution for analytics, and accumulating data over time, the company will be able to revisit data referring to incidents in its network. This way it may be able to figure, for example, that a broken pipe incident was due to increased consumption in the area. This is diagnostic analytics.

By accumulating data and analyzing incidents over time, patterns may begin to emerge. For example, the company subject matter experts and data analysts may be able to identify that when atmospheric pressure and temperature exceed certain thresholds, a broken pipe incident is likely to occur.

Read the full article on ZDNet