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Dual-Consumer Data Model

A dual-consumer data model is designed to serve both human readers (dashboards, reports) and machine consumers (AI agents, automated analytics) from the same governed definitions and computation paths — so every consumer produces the same number from the same logic.

Why This Matters

Most BI was designed for humans only. Dashboards work because people navigate inconsistency through institutional knowledge and reconciliation. AI agents cannot do this. They need deterministic definitions in the model. Without them, AI produces answers that require manual validation — creating shadow processes that defeat the purpose of the investment. The fix is embedding business logic (KPI definitions, cost allocation rules, variance decomposition, hierarchy roll-ups) into the model itself, not into dashboards or people’s heads.

The term is new. The requirement it names is now consensus — KPMG, GoodData, and AtScale all describe it; none have labelled it.

Where This Fits

Anchors the intersection of Data Governance & AI Readiness and Reporting Infrastructure . The design principle that makes AI usable in finance.

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