An MDM hub is not a place to copy every field from every system. This guide gives you a practical framework to decide what belongs in the hub, what does not, and what to do with the gray areas.

Trying to use one master data model for both operations and analytics creates performance, governance, and trust issues. This article explains why MDM needs separate operational and analytical models—and how to design them correctly.

Many master data hierarchies fail to serve business needs due to rigid, flawed designs. This post shows how to build flexible, effective hierarchies that truly support data-driven decisions.

Slowly changing dimensions (SCDs) are the key to making master data useful across both operations and analytics. This article explains all SCD types (0–6), compares MDM vs DW treatment, and shows how to implement change tracking using SQL Server, Informatica, and ADF.

Most teams do not plan a master data API. They build one after friction appears. This article explains when an API makes sense, what it should do, and who it really serves.

As organizations move to distributed architectures, customer, product, and reference data no longer live behind shared tables or implicit ownership. Identity must be explicit. Consistency becomes a design choice. Every shortcut taken in master data architecture shows up later as duplication, drift, or fragile integrations.

Centralization makes sense when a domain has clear ownership, limited contributors, strict controls, and low tolerance for inconsistency. Product data, pricing structures, or regulated reference data often meet these conditions. Centralization is also appropriate when latency must be minimal or when operational systems cannot reliably synchronize changes. Choosing centralization for a specific domain is not a failure of coexistence. It is a recognition of practical constraints.

Most teams hear terms like registry, consolidation, coexistence, and centralized MDM but never get a clear explanation of how they differ. This guide breaks down each architectural style in simple terms, shows where it fits, and highlights the tradeoffs that matter for your program, your systems, and your budget.

Naming conventions aren’t cosmetic. They shape how data is interpreted and shared across systems. This article shows how poor naming creates friction, and how clear, consistent standards improve master data quality, governance, and interoperability.