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Why Prescriptive Analytics Is the Future of Big Data
Big Data has ushered in an era of data analytics that is taking different forms, including prescriptive analytics. This type of business analytics helps you find the best approach for a specific circumstance. It is also considered the third or final part of business analytics, which also encompasses descriptive analytics and predictive analytics. Prescriptive analytics leverages predictive analytics and descriptive analytics to derive ideal outcomes or solutions from helping you solve business problems, and it is driving the future of Big Data. Here’s how:
Differences Between Prescriptive Analytics and Predictive Analytics
Raw data is plentiful in today’s digital age. Approximately 90 per cent of today’s online data represents a compilation of data that was generated in only a few years, and it is projected to grow rapidly. Consumers send billions of messages via instant messaging apps and social networking sites, such as Facebook and Twitter, and generate upwards of six billion on Google every day via their mobile devices and desktops. However, this raw data does not create value on its own. It must be processed in a way that delivers valuable insight to your enterprise for it to be resourceful. With raw data, you can identify patterns, build models based on these patterns and other known information and make data-driven projections to solve business problems.
Both prescriptive analytics and predictive analytics enable you to turn primary data into valuable insights. However, prescriptive analytics and predictive analytics differ in the type of insights you can leverage. Predictive analytics allows you to make future projections based on historical and present data. It enables you to use the known raw data and process it so that you can make predictions on the information you do not know. With prescriptive analytics, you not only can make sense of raw data but also use it to determine the actions to take now. It leverages machine learning, simulations, and mathematical optimisation to help enterprise leaders make better-informed, data-driven decisions. Prescriptive analytics also helps evolve decision-making logic to maintain or improve its effectiveness over time.
