Let’s assume your organization covers the three major areas of what experts call as ‘foundation of meaningful analytics’ – Big data, a data warehouse, and the most modern BI tool. Researchers and business analysts perform various analytics – statistics, math, logics, experiments, simulations, artificial intelligence; you name it – to draw insightful patterns, behaviors and conclusions. Now what? How can organizations put these historic data and analytics for business optimization?
This is where the relatively new and often misinterpreted concept called Prescriptive analytics becomes significant. A company called Ayata (meaning ‘future’ in Sanskrit) holds the trademark for the (capitalized) term Prescriptive Analytics. A lot of people think of it as a continuation of Descriptive Analytics or just another term for Predictive Analytics. While descriptive analytics provides insights into what has happened and predictive analytics forecasts what might happen in the future, prescriptive analytics determines the best solution to a specific problem, given the known factors. In short, it uses a combination of both descriptive and predictive analytics and much more.
To analyze this further, let’s take a credit risk management company. One of the major challenges banks and credit organizations face is the collection of debt. Using statistical data and machine learning, these organizations created successful strategies for optimized debt management. Prescriptive analytics helps the credit risk department to understand the debtor’s repayment pattern, the date or week they are more likely to pay the installment, identifying customers who need to be reminded on the payment deadline, the mode of communication that is best suited for them etc. In recent times, banks and other such financial institutions have even access to details such as monthly salary, pay day etc. to optimize credit management further.
Another example of prescriptive analytics would be in the logistics and supply chain industry. A supplier can optimize his business and inventory management by tracking various retailer records, identifying short supplies etc. The what-if analysis helps to balance supply – demand ratio and a suitable analytical model will close the loopholes in the business operations to produce maximum output.
To conclude, prescriptive analytics offers a myriad of benefits to business. If unleashed the full potential, it can optimize the enterprise performance, identify new trends that could become vital to growth, predict the behavior and impact of various factors, and empower tactical, strategic and business decision–making. Organizations can mitigate a future risk, act on a potential opportunity and continually process real-time data to produce the most accurate decisions. The truth is, organizations are yet to leverage the full potential of analytics. But with technologies such as IoT, artificial intelligence etc. organizations will soon discover a whole new dimension of data processing and business analytics.
