Modern technology is rapidly changing businesses, and data mining and analytics now play important roles in business strategies. Business analytics (BA) employs a combination of skills, technologies and practices to examine organizational data and performance and make decisions.
Predictive and prescriptive analytics are two important types of business analytic strategies that businesses utilize worldwide. Predictive analytics finds potential outcomes regarding consumer behaviors, tool use and organizational changes. Prescriptive analytics utilizes predicted outcomes to generate specific options and solutions. The market for predictive and prescriptive analytic tools is projected to grow at a compound annual growth rate (CAGR) of more than 20% by 2026.
When used together, both predictive and prescriptive analytics can help business leaders create strong, effective business strategies that inform their present and future success.
Predictive analytics uses historical data and modeling techniques to predict the likelihood of specific events. For example, in 2004, Walmart used data mining to understand the buying habits of its consumer base at certain points in time. They found strawberry Pop-Tarts and beers sold at seven times their average rate right before hurricanes hit. Naturally, Walmart used this prediction as an opportunity to stock its shelves with these items before another hurricane. However, while predictive analytical tools can be helpful like this, predictive analytics simply inform business leaders of what might happen in a given situation, not what to do when that situation occurs.
This is where prescriptive analytics comes in. Prescriptive analytic tools develop business and organization models and validate those models against current and historical data. Prescriptive analytics are used to determine the optimal decisions for a business according to predefined criteria, such as profitability and turnover. With prescriptive analytics, business leaders can see multiple potential options and their respective potential outcomes.
Amazon is a prime example of prescriptive analytics in action. Amazon patented “anticipatory shipping,” a process through which the retail giant would move products in the direction of specific consumers before they purchased them. Using machine-learning, Amazon can predict what customers are most likely to buy and when, and then ship it to their location before they buy it. Prescriptive analytics suggested that lost revenue on rejected items would be more than covered by purchases customers ultimately kept.
The Best of Both Worlds
When used together, predictive and prescriptive analytics become formidable resources for businesses to predict outcomes, identify potential scenarios and make targeted decisions to achieve desired outcomes. The aviation industry, for example, utilizes a sophisticated blend of predictive and prescriptive analytics to improve reliability, support safety and decrease costs. Take Skybus’s recent adoption of Skywise, an open data platform designed to handle integrations of commercial and operational systems and process large volumes of data for users to prepare, aggregate and analyze. This application allows airlines and suppliers to access predictive and prescriptive tools together to predict and create solutions for major aviation issues such as maintenance problems, technical delays, fuel efficiency, part replacement and operational costs.
While businesses utilize predictive analytics to identify potential outcomes, prescriptive analytics allows them to achieve desired outcomes based on contingent factors. When used together, these tools hold the potential to change how business is done overall.
Learn more about Lamar University’s online Certificate in Business Analytics program.