Business Analytics, as the name implies, is the application of analytics in business. It is utilized in a myriad of business processes like Marketing,Risk Assessment,Fraud Detection,CRM, Customer Loyalty,Operations, HR, etc.

Many industries such as Financial Services (Banks, Credit Cards, Loans, Insurance etc.), Retail, Telecom, Healthcare, Consumer goods, Manufacturing, Sports, Hotels, Airlines and indeed any industry where large amounts of data is generated utilize Business Analytics.

Business Analytics has evolved recently from other, perhaps more familiar terms like Big Data Analytics, Data Mining, Knowledge Discovery, Business Intelligence (BI), Data Warehousing, etc.

At the bottom of all these is the fact that the amount of data being produced in the world is increasing so fast that, according to some, 90% of the data that exists today was created in the last few years. Sophisticated statistical tools are required to deal with such vast quantities of data. The development of most statistical techniques was, until recently, based on elegant theory and analytical methods that worked quite well on the modest amounts of data being analyzed. The increased power and lower cost of computers have allowed the development of new techniques.

The current Business Analytics methodologies can be classified broadly as below:

Descriptive Analytics This traditional business intelligence (BI) analytics methodology reports what has happened or what is happening now! It is reactive in nature.
Predictive Analytics This analytics methodology utilizes a variety of techniques from statistics, modeling, machine learning and data mining to analyze current and historical facts to make predictions about future events. It assumes that the best predictor of future performance is past performance. The accuracy and usability of results depends greatly on level of data analysis and quality of assumptions. It is proactive in nature.
Prescriptive Analytics This analytics methodology uses array of optimization, simulation and project scheduling techniques to identify actions that will produce best results while operating within resource limitations and tight restriction, generate real prescriptive direction from static and streaming data(including big data), and suggest decision options to take advantage of predictions by anticipating what will happen, when it will happen and why it will happen. It is proactive in nature.
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