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…
The Conceptual Data Model – Key to Integration
By Pete Stiglich Argues that integration succeeds when organizations align on shared business meaning, not just field mappings. A conceptual data model becomes the semantic…
Know your keys!
By Pete Stiglich One question I ask of data architects and data engineers that I’m interviewing is “What is the most important thing to know…
The Power of Dimension Placeholder Records
By Pete Stiglich In a dimensional model, how do you handle missing or invalid values in a fact table source record that are used to…
Normalization of Knowledge
By Donavon Gooldy Normalization is NOT an engineering exercise. The central principle that underlies its rules is that data redundancy, whether repeating groups of attributes or…
What is Data?
By Donavon Gooldy A leader of one of the consulting firms I’ve worked for in my career answered our title question by stating that “Data…
Conceptual Modeling to Enhance Data Quality
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…
Start Your BI Journey with the Data Model
By Jeremiah Willett Data Principles is currently building a training course for a client: Introduction to Power BI. One of the most important topics in…
Conceptual Data Models and Relationships
By Pete Stiglich Explains how conceptual data models (CDMs) capture core business concepts and the meaning of relationships before you get into implementation details. It…
A Wish List for Data Modeling Technology
By Pete Stiglich A practitioner’s “wish list” for what modern data-modeling tools should do better: support collaboration, manage definitions and metadata as first‑class artifacts, and…
