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…
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…
The Holy Grail of Analytics?
By Pete Stiglich The end goal of analytics is to be able to make data-driven decisions — sooner rather than later. Of course, it is…
Empowering Data-Driven Decisions through Effective BI Training
By Jeremiah Willett Data Principles is a proud partner of Qlik, a leading analytics, data integration, and AI company. Qlik Sense Cloud is designed for self-service…
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…
Qlik’s Extremely Powerful and Easy-to-use Key Driver Analysis capability
By Pete Stiglich If you are a Qlik Sense Cloud customer (and if not, let me know and we can get you started!), the “Key…
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…
Key insights on source data for healthcare analytics & exchanges
By Pete Stiglich Explains why integrating healthcare datasets is hard when metadata is incomplete—especially as data exchange accelerates. The post proposes creating a standard ‘what…
