By Pete Stiglich
Describes how organizations reduce confusion and redundancy by rationalizing data and resolving semantic differences across systems. The article’s theme is that integration problems often stem from inconsistent definitions, overlapping datasets, and ambiguous naming. Semantic resolution involves identifying when two attributes represent the same concept (or not), documenting meaning, and mapping to enterprise standards so downstream analytics and services are consistent. The piece positions this work as foundational for mergers, migrations, and enterprise architecture programs: by cleaning up semantics and rationalizing sources, teams reduce rework, simplify integration pipelines, and improve data quality and trust.
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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.
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