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…
The Semantic Web for Data Governance and Stewardship
By Pete Stiglich Semantic Web technologies can strengthen data governance and stewardship by improving how organizations understand and connect their data. Rather than focusing only…
Data Governance and Stewardship Organizations
By Pete Stiglich Provides an example organizational structure for implementing governance and stewardship in phases, tailored to culture, size, and data complexity. It focuses on…
Data Management Activities that are Critical to Trustworthy Data
By Pete Stiglich Identifies core data management practices that enable real trust in data: strong data governance with meaningful business participation, robust data architecture and…
Data model conversion: Conceptual design to logical design using an ER model
By Pete Stiglich Outlines common approaches for transforming a conceptual data model into a logical data model. One approach expands the CDM by identifying additional…
Data Models: The Key to Successful Data Migrations
By Pete Stiglich Describes why migrations often fail when teams treat source‑to‑target mapping as a mechanical exercise. It recommends using three key models: a business…
Data Rationalization and Semantic Resolution
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…
Database inferencing to get to trusted healthcare data
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,…
Dimensional Data Modeling Fact Qualifier Matrix
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.…
Enabling High Quality Analytics through a Data Validity Dimension
By Pete Stiglich Explains a practical dimensional-modeling pattern for handling imperfect source data without destroying analytical trust. Instead of rejecting records with invalid dates or…
