As people use the application, it keeps track of the actions users perform such as viewing, creating, and modifying content (documents, blog posts, user profiles, and more). The analytics data is stored according to a data model that's commonly used for other data warehousing and applications. Through this standard model, you can more easily retrieve and analyze data you need.
In addition to this real-time activity collection, there are also batch jobs that run at night, populating the data store with supplemental data. That data includes the names of content and users associated with these activity records. This process of batch loading is known as an "extract, transform, and load" (ETL) process.
The final result is a data store (sometimes referred to as a "data mart" or "data warehouse"). To this data store, you can connect other reporting tools to generate visual reports. You can also connect this store to an OLAP product to achieve sophisticated exploration and analysis of the data.