Prerequisite: None
Course Outline
Dimensional data is a core component of modern business intelligence and analytics programs and is the standard for data mart implementations. Dimensional models capture the metrics that describe business processes throughout the enterprise, enabling BI dashboards, reports, and analysis, while also providing granular data and important business context for ML and AI solutions. Virtually anyone involved in business intelligence, analytics, and data architecture needs to have fundamental knowledge of the pathway from business questions to business analytics. This course traces that pathway.
The course begins with a comparison of third normal form (3NF) and dimensional data organization and provides an example of business questions not readily answered using more traditional data modeling structures. It then illustrates the steps to design analytics solutions, starting from business questions and progressing through logical and physical designs that support effective business analysis. These steps encompass techniques to capture business questions, represent them as a business solution, translate them into a technology solution, and deliver them to those who need information.
You Will Learn:
- Concepts of dimensional data modeling
- The relationship between business metrics and dimensional data
- Similarities and differences between dimensional and normalized data models
- Requirements-gathering techniques for business metrics and dimensional data
- How to build a logical dimensional model
- How to translate a logical dimensional model to a star schema design
- How dimensional data is used to deliver BI and analytics capabilities
Geared To:
- Data architects
- Data mart developers
- Data modelers
- Business analysts
- Business intelligence and data warehouse program and project managers
- Requirements analysts