Data analytics is a broad term that encompasses many diverse types of data analysis.

Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things.

Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information.

This information can then be used to optimize processes to increase the overall efficiency of a business or system.

For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity.

Data analytics can do much more than point out bottlenecks in production.

Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game.

Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click. Some of the early days of modern data analytics are due to SQL.

Created in 1979, this computing language allows relational databases to be queried and resulting data sets to be more easily analyzed. SQL is still widely used today.

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 and by storing large amounts of data.

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.