By Pete Stiglich
Explores how conceptual modeling improves data quality by forcing clarity on business concepts, definitions, and the rules that connect them. By identifying entities, attributes, and relationships at the business level, teams can spot ambiguity, duplication, and missing rules that later manifest as inconsistent or invalid data. The piece emphasizes that quality isn’t only profiling and cleansing; it is also preventing defects through better upstream understanding, documentation, and agreement on what data represents and how it should behave across processes.
Disclaimer
Links to third-party articles and resources are provided for informational purposes only. Data Principles, LLC does not claim ownership of, nor imply endorsement by, the referenced organizations.
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.
Join Our Data Community
At Data Principles, we believe in making data powerful and accessible. Get monthly insights, practical advice, and company updates delivered straight to your inbox. Subscribe and be part of the journey!
