TDWI instructor Chris Adamson defines dimensional data modeling as “the nexus of a holistic approach to managing business intelligence, analytics, and governance programs. Used at a program level to define the scope of projects, the dimensional model makes possible data marts and dashboards that reflect analytics insights, analytics that link directly to business objectives, performance dashboards that can drill to OLAP data, and master data that is consistent across these functions.”
More simply, dimensional data modeling is an approach to data warehouse design that organizes information to simplify end user queries rather than data ingestion. It uses the concepts of facts (things that can be measured) and dimensions (the context for those facts) to organize data for maximum accessibility. Dimensional data modeling evolved from the practice of data marts in data warehouse architecture and is a structured way to model and report against data stores.