By Pete Stiglich
Describes building a data-quality rule repository for a healthcare EDW program focused on ‘trusted data.’ The author explains storing configurable quality rules, metadata about datasets, and measurement results, then needing a form of ‘inferencing’ in a relational database when semantic-web tooling wasn’t an option. A key use case is inheriting data quality rules from a parent dataset to lower-level subsets (e.g., canonical message types used across services). The post connects governance, measurement, and rule inheritance to making trust verifiable rather than aspirational.
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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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