By W H Inmon Take a look at the subjects of data architecture and data modelling today. What do you find? A big mess. Some…
Necessity of Conceptual Data Modeling for Information Quality
By Pete Stiglich Explains that conceptual data modeling is essential for ensuring high data and information quality. It emphasizes that data quality depends not only…
Benefits of Conceptual Data Modeling
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…
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…
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…
Enterprise vs. Project-Level Conceptual Data Modeling
By Pete Stiglich Compares enterprise conceptual data models (covering broad domains and shared definitions) with project-level conceptual models (scoped to a specific initiative). It notes…
Performance Benefits of Surrogate Keys in Dimensional Models
By Pete Stiglich Explains why dimensional warehouses typically use surrogate keys in dimensions instead of relying on natural keys from source systems. Small numeric surrogate…
Steps to Convert Logical to Physical Data Models
By Pete Stiglich Explains how a logical data model is refined into a physical implementation. Typical steps include selecting DBMS-specific data types, applying naming abbreviations…
