Predictive analytics offers companies actionable insights based on data. It delivers estimates about the likelihood of a future outcome. Companies are using these statistics to get a forecast of the future developments. Moreover, the foundation of predictive analytics is based on probabilities. Prescriptive analytics uses techniques and tools such as business rules, algorithms, machine learning (ML) and computational modelling procedures. This kind of technique is applied against input from many different data sets including historical and transactional data, real-time data feeds, and Big Data. The businesses and enterprises that are already using some sort of descriptive analytics tools and solutions are better positioned for predictive and /or prescriptive analytics solutions adoption. The presence of historical data to forecast from and run algorithms makes them use these solutions more effectively. The demand for business intelligence has been on the rise, in recent years, with enterprises and organizations planning to enhance productivity and increase sales by adopting automated solutions. Therefore, BI tools have witnessed a tremendous rise in its adoption across various industries around the world. Additionally, predictive and prescriptive analytics is helping in boosting the demand for business analytics among the BI professionals, as Big Data is becoming the focus of analytics processes that are being leveraged not only by the big enterprises, but also by the small and medium-sized businesses alike. Moreover, the development of better visualization tools, as well as incorporation of NLP and voice input capabilities have further added to use ease of use.

1. INTRODUCTION 

2. RESEARCH METHODOLOGY

3. EXECUTIVE SUMMARY 

4. MARKET INSIGHTS 

5. MARKET DYNAMICS 

6. MARKET SEGMENTATION 

7. KEY VENDOR PROFILES 

8. INVESTMENT ANALYSIS 9. FUTURE OUTLOOK