The field of analytics has evolved considerably over the years, moving from rudimentary dashboards to highly sophisticated analyses—including prescriptive analytics, which enables businesses to make better decisions, mitigate future risk, and gain a competitive advantage. This ebook, based on the latest ZD/TechRepublic special feature
, offers a detailed look at what prescriptive analytics is and how companies have been putting it to work.
From the ebook:
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.
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 rearview 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.”