Data Modeling Standards

What it is

Data modeling standards are the organization-wide conventions — naming, typing, structuring — that keep data consistent across every team and pipeline, rather than each team inventing its own conventions independently.

Key points

• Conformed dimensions: shared dimension definitions (e.g. one canonical "customer" table) used consistently across every fact table and team, avoiding the same real-world entity being modeled three different, incompatible ways.
• Naming conventions: consistent column/table naming (e.g. always created_at, never a mix of created/created_date/createdAt) sounds trivial but is what makes self-service analytics actually usable at scale.
• Slowly changing dimensions: standardizing how historical attribute changes are tracked (overwrite vs. versioned rows) so "what was this customer's segment on this date" is answerable consistently.
• Without enforced standards, decentralized teams' data models drift apart, and the cost shows up later as expensive reconciliation work across systems that were never designed to reconcile.