What is predictive analytics? Transforming data into future insights

Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. The science of predictive analytics can generate future insights with a significant degree of precision. With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast future trends and behaviors.

Predictive analytics has captured the support of wide range of organizations, with a global market projected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 percent between 2016 and 2022, according to a 2017 report issued by Zion Market Research.

Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning and assorted mathematical processes. Organizations use predictive analytics to sift through current and historical data to detect trends and forecast events and conditions that should occur at a specific time, based on supplied parameters.

With predictive analytics, organizations can find and exploit patterns contained within data in order to detect risks and opportunities. Models can be designed, for instance, to discover relationships between various behavior factors. Such models enable the assessment of either the promise or risk presented by a particular set of conditions, guiding informed decision-making across various categories of supply chain and procurement events.

Over the centuries, peering into the future has always had three fundamental goals: money, fame and power. Predictive analytics doesn’t really change the reasons why people want to know what’s going to happen next week, next month or next year, it just makes looking into the future more accurate and reliable than previous tools.

Money: Predictive analytics can help adopters find ways to save and earn money. Retailers often use predictive models to forecast inventory requirements, manage shipping schedules and configure store layouts to maximize sales. Airlines frequently use predictive analytics to set ticket prices reflecting past travel trends. Hotels, restaurants and other hospitality industry players can use the technology to forecast the number of guests on any given night in order to maximize occupancy and revenue.

Fame: No business ever succeeded by fading into obscurity. By optimizing marketing campaigns with predictive analytics, organizations can generate new customer responses or purchases, as well as promote cross-sell opportunities. Predictive models can help businesses attract, retain and nurture their most valued customers.

Power: Predictive analytics can be used to detect and halt various types of criminal behavior before any serious damage is inflected. By using predictive analytics to study user behaviors and actions, an organization can detect activities that are out of the ordinary, ranging from credit card fraud to corporate spying to cyberattacks.

The difference between conventional analytics and predictive analytics is simple and straightforward. Whereas traditional analytics generally focuses on insights impacting the here and now, predictive analytics aims to allow users to gaze into the near- and long-term future to pinpoint likely trends and upcoming behaviors.

While getting started in predictive analytics isn’t exactly a snap, it’s a task that virtually any business can handle as long as one remains committed to the approach and is willing to invest the time and funds necessary to get the project moving. Beginning with a limited-scale pilot project in a critical business area is an excellent way to cap start-up costs while minimizing the time before financial rewards begin rolling in.