luvv to help

Menu

Topic 3 of 8

Review And Governance Of Models

Learn Review And Governance Of Models for free with explanations, exercises, and a quick test (for Data Architect).

Published: January 18, 2026 | Updated: January 18, 2026

Table of Contents

Why this matters

Conceptual and logical data models guide platform design, integrations, and analytics. Without structured review and governance, teams ship conflicting definitions, introduce breaking changes, and increase compliance risk. As a Data Architect, you ensure models are correct, consistent, documented, and safely evolved.

  • Real tasks you will do: run model review sessions, maintain a glossary and data dictionary, approve change requests, version models, and record decisions.
  • Outcomes: fewer rework cycles, predictable releases, audit-ready documentation, and better cross-team alignment.

Concept explained simply

Review is a repeatable checkpoint to validate a model against business meaning, standards, and downstream needs. Governance is the agreed set of rules and roles that keep models consistent over time.

Mental model

Think of governance as the traffic system for models:

  • Rules: standards for naming, keys, data types, and privacy.
  • Signals: stage gates like propose → review → approve → publish.
  • Licenses: roles and RACI (who decides, who contributes).
  • Recorders: catalogs, decision logs, and version history.
  • Maps: diagrams and dictionaries that everyone can read.

Governance framework you can apply today

1) Roles and decision rights (RACI)

  • Responsible: model author(s) and steward(s).
  • Accountable: Data Architecture lead (final approver).
  • Consulted: domain SMEs, security/privacy, analytics, app engineers.
  • Informed: downstream owners and support teams.

2) Standards to adopt

  • Naming: singular entity names (Customer), clear attribute names (customer_id).
  • Keys and relationships: defined cardinality, natural vs. surrogate keys, nullability rules.
  • Data types: canonical types and length/precision rules.
  • Privacy: classification (Public/Internal/Sensitive/Restricted) and handling patterns (masking, hashing, minimization).
  • Documentation: definition, examples, source of truth, steward, lineage note.

3) Lifecycle

  1. Propose (create change request with rationale and impact).
  2. Review (peer + cross-functional checks using the checklist).
  3. Approve or revise (document decision and rationale).
  4. Publish (versioned model, dictionary, and change log).
  5. Implement (coordinate with engineering; add quality checks).
  6. Monitor (usage, defects, and data quality signals).
  7. Retire (deprecate with a sunset plan).

4) Versioning

  • Use semantic versioning: MAJOR.MINOR.PATCH.
  • MAJOR: breaking changes (rename entity, split entity, remove attribute).
  • MINOR: backward-compatible additions (new optional attribute, new relationship that does not break existing queries).
  • PATCH: corrections to definitions, typos, docs-only updates.

5) Change control essentials

  • Template fields: problem, business value, scope, impact analysis, privacy/security, migration plan, test plan, rollout plan, owner, target date.
  • Backward compatibility: prefer additive changes; provide aliases, views, or mapping tables; set deprecation windows.
  • Decision record: what changed, why, options considered, approval, date.

6) Review criteria (use this checklist)

  • Business meaning is correct and agreed.
  • No duplicate or conflicting entities/attributes.
  • Naming follows standards and is unambiguous.
  • Keys, relationships, cardinality, and nullability are explicit.
  • Privacy classification and handling are defined.
  • Traceability: source, lineage note, and steward are documented.
  • Change impact and migration plan are credible and time-bound.

Worked examples

Example 1: Resolving term conflicts (Customer vs. Client)

See decision flow
  1. Identify conflict: Sales uses Client; Support uses Customer.
  2. Consult glossary: propose canonical term Customer; record Client as synonym.
  3. Review names: entity Customer; attribute customer_id; no duplicate Client entity.
  4. Versioning: MINOR if only naming in docs; MAJOR if physical names change.
  5. Publish: update dictionary, add synonym mapping, notify impacted teams.

Example 2: Adding a sensitive attribute (national_id)

See governance steps
  1. Classify as Restricted (PII). Define purpose and minimal retention.
  2. Decide storage pattern: hashed_national_id for joins; masked views for read.
  3. Access control: limit to approved roles; log access.
  4. Versioning: MINOR if optional and additive; MAJOR if it forces schema changes downstream.
  5. Docs: add definition, handling rules, steward, and risk note.

Example 3: Splitting Order into OrderHeader and OrderLine

See impact analysis and rollout
  1. Reason: normalize repeating line items; improve performance.
  2. Modeling: OrderHeader( order_id PK ), OrderLine( order_id FK, line_number PK ).
  3. Breaking change: requires MAJOR version. Provide compatibility view order_v1 for a deprecation window.
  4. Migration: backfill OrderLine from existing array field; validate counts and totals.
  5. Decision log: alternatives considered (nested vs. normalized), chosen approach, risks, mitigation.

Exercises you can do now

Complete these two exercises. Then compare your work with the solutions below.

Exercise 1: Run a mock model review

Scenario: A new entity Subscription(customer_id, plan_code, start_date, end_date, status) is proposed. Use the review checklist to find issues and propose fixes.

  • Deliverable: a short decision note listing at least five review findings and your recommendations.

Exercise 2: Create a change request and version plan

Scenario: You want to add marketing_opt_in to Customer.

  • Deliverable: a change request with purpose, privacy classification, default/backfill plan, rollout steps, and semantic version update. State whether it is MAJOR/MINOR and why.
Checklist to self-evaluate your exercise outputs
  • You referenced naming, keys, relationships, and privacy classification.
  • Your impact analysis mentions at least two downstream consumers.
  • You proposed a version number and deprecation or rollout window.
  • You captured a clear decision and the rationale.

Common mistakes and how to self-check

  • Skipping stakeholder consultation. Self-check: Did you involve both producers and consumers of the data?
  • Renaming without aliases. Self-check: Is there a compatibility view or synonym during the transition?
  • Vague definitions. Self-check: Does each entity/attribute have a precise business meaning with an example?
  • Ignoring privacy early. Self-check: Is the classification set and handling pattern documented?
  • No impact analysis. Self-check: Which pipelines, reports, and APIs are affected, and how will they be tested?

Practical projects

  • Model governance starter kit: create a one-page standard, a checklist, and a decision log template. Pilot on one domain.
  • Glossary and dictionary build: document 30 key entities and attributes with definitions, stewards, and privacy tags.
  • Versioned release: take an existing model change, produce MAJOR/MINOR release notes, compatibility views, and a migration guide.

Who this is for

  • Data Architects and Senior Data Engineers owning cross-team models.
  • Analytics Engineers and Domain Stewards contributing definitions.

Prerequisites

  • Basic conceptual/logical modeling (entities, relationships, keys).
  • Familiarity with data privacy concepts and data lifecycle.

Learning path

  • First: solidify conceptual modeling and glossary practices.
  • Then: introduce governance standards and checklists.
  • Next: practice change control, versioning, and decision records.

Mini challenge

Challenge prompt

Your analytics team wants to deprecate attribute user_age and replace it with birth_date for accuracy. Draft a two-paragraph decision record: include the problem, options considered, decision, versioning, and rollout plan with a 60-day deprecation window.

Next steps

  • Apply the checklist on one real domain and record at least one decision this week.
  • Schedule a 30-minute review with cross-functional stakeholders to validate a change proposal.
  • Take the quick test below to reinforce knowledge.

Progress and access note

The quick test and exercises are available to everyone for free. If you log in, your progress and results will be saved automatically.

Practice Exercises

2 exercises to complete

Instructions

Review the proposed entity Subscription(customer_id, plan_code, start_date, end_date, status). Using the governance checklist, identify at least five issues or clarifications (e.g., naming, keys, relationships, privacy, lifecycle). Propose concrete fixes and document a short decision.

  • Consider keys and uniqueness (what identifies a subscription?).
  • Define status values and allowed transitions.
  • Clarify date semantics (inclusive/exclusive).
  • Privacy classification for customer_id.
  • Downstream impact (billing, reporting).
Expected Output
A bullet list of ≥5 review findings with recommended actions, plus a brief decision note including version impact and owner.

Review And Governance Of Models — Quick Test

Test your knowledge with 10 questions. Pass with 70% or higher.

10 questions70% to pass
Handling Master Data ConceptsDomain Modeling And Boundaries

Have questions about Review And Governance Of Models?

AI Assistant

Ask questions about this tool