They say knowledge is power. Analytical data is the knowledge that gives companies the power to leverage accumulated data to gain insights into how to optimize the business, better understand customers’ needs, and even make automated decisions by training machine learning (ML) models.

The analytical models (OLAP) and operational models (OLTP) serve different types of consumers, enable the implementation of different kinds of use cases, and are therefore designed following other design principles.

Operational models are built around the various entities from the system’s business domain, implementing their lifecycles and orchestrating their interactions with one another. These models, depicted in Figure 16-1, are serving operational systems and hence have to be optimized to support real-time business transactions.

Figure 16-1. A relational database schema describing the relationships between entities in an operational model

Analytical models are designed to provide different insights into the operational sys‐ tems. Instead of implementing real-time transactions, an analytical model aims to provide insights into the performance of business activities and, more importantly, how the business can optimize its operations to achieve greater value.

From a data structure perspective, OLAP models ignore the individual business enti‐ ties and instead focus on business activities by modeling fact tables and dimension tables. We’ll take a closer look at each of these tables next.