Data analytics is the science of analyzing raw data to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Data analytics is the science of analyzing raw data to make conclusions about that information.
Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategically-guided decisions.
The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. 
Various approaches to data analytics include looking at what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), or what should be done next (prescriptive analytics).
Data analytics relies on a variety of software tools ranging from spreadsheets, data visualization, and reporting tools, data mining programs, or open-source languages for the greatest data manipulation.
 

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business. A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services. 

Data analytics has been adopted by several sectors, such as the travel and hospitality industry, where turnarounds can be quick. This industry can collect customer data and figure out where the problems, if any, lie and how to fix them. Healthcare is another sector that combines the use of high volumes of structured and unstructured data and data analytics can help in making quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers.