Three Strong Use Cases to Back Prescriptive Analytics

The Hype Cycle of Emerging Technologies has prescriptive analytics on the top. Still, usage of prescriptive analytics is not a common sight, hence it is believed to take around 5-10 years more to become common in boardrooms across the world. Although its usage is increasing day by day, some people don’t have a clear idea about it while some are not even aware of the term. How it can be used and how it can be helpful to organizations are still some of the unanswered questions amongst many people. This article dives deep to provide the answers to such questions along with three strong use cases to back prescriptive analytics.

Role of prescriptive analytics

Many technology enthusiasts are seeing prescriptive analytics as the future of big data. This is so because of considering descriptive analytics as the foundation of Business Intelligence and predictive analytics as the basis of Big Data. The following example helps in explaining the difference between the three types of analytics.

As descriptive analytics deals with historical data, it can be compared with looking in the rear mirror while driving. Predictive analytics make use of this data to predict where to go. It is like navigation that provides information regarding how to drive and the time of arrival. And, prescriptive analytics has a self-driving car as an example. The self-driving car is well-versed with roots and can point the best route based on certain calculations. Thus, the difference between the three analytics can be clearly understood with this.

Use cases of Prescriptive analytics

The constantly evolving field of prescriptive analytics sees the development of more and more use cases being developed. Many organizations have shifted their focus on prescriptive analytics by now. They make use of the patented software. This patented software development company in India enable prediction of what is going to happen, when it will happen and when it will happen. It is fit for use in the scenarios having too many variables, options, constraints, and data sets. Such scenarios make it difficult for humans to evaluate everything without the use of technology. That’s why prescriptive analytics come into role play here. It is also useful when real-life experiments involve more risks or expenses.

Three Strong Use Cases

Although many use cases have marked their presence by now, the three strongest of them are as follows:

Optimization of Travel and Transportation: Travel industry consists of many large data sets which are the need for prescriptive analytics. Online travel websites, hotel websites, ticketing services etc. have moved to prescriptive analytics to determine customer perspectives, choices, route optimization, segmentation of customers based on data sets and other related data sources. This is done in order to optimize pricing and sales.

Oil Production through Fracking: use of fracking has experienced a substantial growth in recent years. Massive data sets are required to obtain the knowledge regarding where to frack, how to make the process safer and to optimize the process. This brings in the need of analysis work thus leading to use of prescriptive analytics.

Healthcare Industry

One of the widely spread sector, healthcare, makes use of almost all kind of technologies. And, prescriptive analytics has come to completely transform the way this sector operates. It deals with massive amounts of data sets to be analyzed. Better healthcare for less money will be possible because of the combination of various data sets of the sector. Thus, offering a lot of opportunities and services in the field.

The main role of prescriptive analytics is to understand the impact of future decisions and use the scenarios to determine the best outcome. Technologies like Machine Learning and Artificial Intelligence act as great ways to help prescriptive analysis in serving the purpose. It is because of prescriptive analytics that future opportunities are understood and grasped. The continuous updating of predictions along with the new data helps in to accomplish that.

This article is written by James Warner. He is a Business Intelligence Analyst with Excellent knowledge on Hadoop/Big data analysis at

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