Requirements Analysis Documentation

Requirements Analysis turns stakeholder needs into clear, testable, and traceable specifications for the database life cycle (DBLC). The outputs below guide logical/physical design, development, testing, and change control.

1. Requirements Specification (SRS)
   Consolidates functional behavior (what data and operations are required) and non-functional qualities (performance, security, availability, compliance). Includes scope, assumptions, constraints, acceptance criteria, and success measures.
2. Business Rules Catalog
   Validations, constraints, policies, and decision logic that the database must enforce (e.g., CHECKs, FK rules, RBAC-driven access).
3. Data Requirements
   Candidate entities, attributes, domains, keys, integrity rules, and naming standards; becomes the basis for the ERD and data dictionary.
4. User Stories / Use Cases
   Concise, role-centered descriptions with acceptance criteria (who needs what data and why); drive user view definitions and test cases.
5. Data Flow Diagram (DFD) or Equivalent
   Context/levelled views of where data originates, how it moves, which processes transform it, and where it’s stored. Optional for small, single-subject systems; recommended for multi-database or cross-team flows.
6. Entity-Relationship Diagram (ERD)
   Structural model of entities, attributes, and relationships; inputs to logical schema design.
7. User View Specifications
   Role-scoped, task-focused stored queries (views) including columns, filters, joins, and calculated fields required by the audience.
8. Data Dictionary & Glossary
   Canonical definitions, data types, domains, and reference codes; reduces ambiguity and supports governance.
9. Requirements Traceability Matrix (RTM)
   Maps each requirement to design elements (ERD, constraints, views), test cases, and releases; enables impact analysis and audits.

Artifact	Purpose	Downstream Use
SRS	Authoritative scope & testable requirements	Design decisions, test plans, sign-off
Business Rules	Policies & constraints	CHECK/FK rules, RBAC, triggers
Data Requirements	Entities/attributes/domains	ERD, data dictionary
DFD	Flows, interfaces, stores	Integration points, user views
ERD	Structure & relationships	Logical/physical schema
User Views	Role-focused access	Stored queries, calculated fields
RTM	Trace & impact analysis	Change control, audit readiness

Document these items before exiting Requirements Analysis:

1. Preliminary list of business objects (by subject), their attributes, and relationships.
2. Business rules with proposed enforcement (constraints, views, RBAC, application logic).
3. Assessment of future needs (growth, new channels, regulatory change).
4. User view specifications (including required calculations and filters).
5. DFD/context diagram if multiple systems/datastores or nontrivial handoffs exist.

• Versioning & Traceability: Keep numbered revisions; link SRS ↔ ERD ↔ views ↔ test cases in the RTM.
• Clarity: Prefer concise, testable statements over narrative prose.
• Sign-off: Record stakeholder approvals and decision logs for scope control.
• Living Docs: Update artifacts when requirements change; reflect deltas in the RTM.

When historic/legacy data is involved, choose an approach based on business and compliance needs:

• Archive Only: Retain read-only copies for record-keeping; document retention and access paths.
• Migrate: Profile → map → transform → validate → load → reconcile; plan cutover, backout, and sign-off.
• Integrate: Use ETL/ELT or data virtualization; consider CDC for ongoing sync.

Address data quality, PII handling, lineage, and reconciliation criteria in the SRS and RTM.

Effective Requirements Analysis produces concise, traceable artifacts that de-risk design and build. Capture only what teams will use, keep it testable, and maintain traceability from requirement to schema, views, and tests.