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What is a Requirements-Friendly Data Dictionary — and Why Your MR4DevOps Process Should Include One

A practical guide to separating system-managed data requirements from functional requirements using two custom Azure DevOps Work Item Types.

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Why a Requirements-Friendly Data Dictionary Matters

Most teams describe system-managed data inside functional requirements — scattering data rules across user stories, use cases, and requirement statements. An RFDD pulls that information into two purpose-built Work Item Types so it can be reused, reviewed, and traced.

Fewer, More Consistent Functional Requirements

Documenting Records and Record Fields in an RFDD eliminates entire categories of requirements that traditionally repeat across user stories — like “Customer records need a unique identifier” or “A Purchase Order must have at least one Line Item.”
Stakeholder-Friendly Terminology

SMEs already think in terms of records and fields, not entities and attributes. The RFDD uses the language stakeholders use every day — without dictating storage design or implementation.

Reusable Data Requirements

Like a project glossary, RFDD entries become a single source of truth. Business analysts reference (not re-write) Records and Record Fields when eliciting new functional requirements, keeping vocabulary consistent across the whole solution.
Completeness Built In

Property-specific help text guides BAs and SMEs through validation, derivation, archiving, privacy, auditability, and more — so every Record and Field is documented to the same standard.

Le saviez-vous ?

A typical functional requirement set contains 30–40% data-related statements that could be replaced with reusable RFDD entries. By managing Records and Record Fields as separate Work Item Types in Azure DevOps, teams report shorter elicitation cycles, fewer review iterations, and far cleaner traceability between data and the capabilities that use it.

Répartition

The Two RFDD Work Item Types

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