Models can be extremely simple, such as a single table with columns and rows. However, business intelligence almost always involves multiple tables of data, and most often involves multiple tables of data coming from multiple sources. Thus, the model becomes more complex as the various sources and tables of data must be combined into a cohesive whole. This is done by defining how each of the disparate sources of data relates to one another. As an example, let's say you have one data source that represents a customer's name, contact information, and perhaps size in revenue and/or the number of employees. This information might come from an organization's customer relationship management (CRM) system. The second source of data might be order information, which includes the customer's name, units purchased, and the price that was paid. This second source of data comes from the organization's enterprise resource planning (ERP) system. These two sources of data can be related to one another based on the name of the customer.

Some sources of data have prebuilt models. This includes traditional data warehouse technologies for structured data as well as analogous systems for performing analytics over unstructured data. The traditional data warehouse technology is generally built upon the online analytical processing (OLAP) technology and includes systems such as Microsoft's SQL Server Analysis Services (SSAS), Azure Analysis Services, Snowflake, Oracle's Essbase, AtScale cubes, SAP HANA and Business Warehouse servers, and Azure SQL Data Warehouse. With respect to unstructured data analysis, technologies such as Apache Spark, Databricks, and Azure Data Lake Storage are used.