By Pete Stiglich
Introduces the Fact Qualifier Matrix (FQM) as a technique for validating that dimension conformance and grain are consistent across multiple fact tables. It shows how mapping facts to shared dimensions supports coherent reporting and prevents ‘apples to oranges’ comparisons. The article also describes an expanded FQM that links business questions to the facts and dimensions needed to answer them, making it useful for requirements, training, and model reviews. The outcome is clearer analytics design and fewer surprises during BI development.
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Pete Stiglich: Trusted Expert in Data Architecture & Modeling
Pete has over 30 years of data architecture, data management, and analytics experience, most of that time as a consultant in industries such as government, finance, healthcare, insurance, and more. He is an industry thought leader in data architecture and data modeling and has developed and taught many courses on these topics. Pete enjoys helping clients solve complex data problems, leveraging proven approaches such as “Modeling the business before modeling the solution” which provides a benefit to clients that many IT professionals miss.
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