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…
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…
Meta Data Management and Migration to ICD-10
By Pete Stiglich Discusses why the transition from ICD‑9 to ICD‑10 is high-stakes and complex for providers and payers: code mappings are often not 1:1,…
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…
Realizing Optimal Value from Cloud Migration
By Pete Stiglich Focuses on how cloud migrations deliver the most value when they are coupled with architectural modernization rather than simple ‘lift and shift.’…
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…
