Business analytics that are focused on improving operations, using data mining, data aggregation and other processes to get an in-depth analysis of all key operational areas of a business.

The use of software to improve daily business operations belongs to the realm of operational analytics. This type of analytics explores real-time, or close to real-time, data to suggest ways to optimize existing business activities and to locate possible bottlenecks, leaks in efficiency, logistical and other problems. Examples of operational analytics include predicting when machinery requires maintenance, uncovering fraud, understanding a decline in online orders in an e-commerce application, and tracking inventory.

While predictive analytics look to show what will happen in the future, operational analytics report on current, daily situations. For this reason, it is important that the data reported on is as real-time as possible. Data that has to go through an ETL (extract, transform and load into a data warehouse) process will likely be too old to provide valuable analytics for timely decisions.

Operational analytics are further improved when embedded within the software used daily by the organization, where it can be quickly and easily accessed. A corollary to this is that, because the results from operational analytics impact daily business processes, this type of analytics benefits from the input of users of all levels within an organization – from factory line inspectors to customer service managers to the C-suite.