By Pete Stiglich
Highlights how conceptual modeling fills gaps between requirements and solution design by forcing teams to ask probing questions, uncover exceptions, and document business rules before implementation. It recommends complementing top‑down interviews with bottom‑up profiling of existing data sources to confirm reality. The article warns that skipping conceptual modeling can lead to incorrect relationships in later models (e.g., 1:M vs M:M), which can force data workarounds and reduce trust. Done early, a CDM improves scope control and project predictability.
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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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