Who this is for
- Data Architects who define governance and guardrails for data platforms.
- Data Engineers and Analytics Engineers who propose changes to pipelines, models, or schemas.
- Data Stewards and Product Owners who approve, document, and communicate data changes.
Prerequisites
- Basic understanding of data lifecycle: ingestion, transformation, storage, consumption.
- Familiarity with version control (e.g., Git) and change tickets (e.g., issue trackers).
- Awareness of data classifications (PII, sensitive, public) and access controls.
Why this matters
In real teams, changes to data assets can break dashboards, legal compliance, and ML models. A clear approval workflow ensures the right people review, risks are understood, and the change is traceable and reversible. As a Data Architect, you design this path so changes move fast without surprises.
- Real task: Approve a schema change to a customer table used by finance and marketing.
- Real task: Gate a pipeline change that touches PII to ensure DPO/legal review.
- Real task: Sunset a metric and communicate to 12 downstream dashboards.
Concept explained simply
An approval workflow is a structured sequence of states and reviews that a proposed change must pass before it goes live. It defines who can request changes, who reviews them, which evidence is required, how risk is assessed, and how to proceed or roll back.
Mental model
Imagine a gated hallway:
- Gate 1: Submission (what changes, why, risk level, impact)
- Gate 2: Automated checks (tests, linters, lineage impact)
- Gate 3: Human approvals (steward, owner, security/compliance)
- Gate 4: Pre-prod validation (QA, sample backfill)
- Gate 5: Controlled release (schedule, communication)
- Side doors: Exception/emergency path with added audit
Workflow blueprint
- Intake: Requester submits a change ticket with scope, reason, risk, affected assets, validation plan, and rollback plan.
- Classification: System or steward assigns change type and risk tier.
- Automated checks: CI runs tests, schema validators, lineage impact, data quality checks.
- Human approvals: Required approvers sign off based on risk tier and asset ownership.
- Pre-prod testing: Run in staging or with a shadow job; capture evidence.
- Release window: Schedule deployment and notify consumers.
- Post-release monitoring: Track KPIs and data quality for a defined window.
- Closure: Update catalog and docs; attach evidence and outcomes to the ticket.
Risk tiers (open for details)
- Low: Backward compatible changes (adding nullable columns, comments). Approvers: Asset Owner.
- Medium: Potentially breaking consumer queries (renames with aliases, type widening, new PII tagging). Approvers: Owner + Steward; compliance if PII.
- High: Breaking schemas, drop/rename without compatibility, privacy classification changes, batch window changes affecting SLAs. Approvers: Owner + Steward + Compliance/Security + Domain Lead.
Roles and RACI
- Requester (Responsible): Proposes change and provides evidence.
- Asset Owner (Accountable): Final sign-off for the asset.
- Data Steward (Consulted): Ensures definitions, catalog, lineage are correct.
- Compliance/Security (Consulted/Approver for sensitive data): Reviews privacy and controls.
- Consumers/Stakeholders (Informed): Receive release notes and timelines.
Required artifacts
- Impact analysis: upstream/downstream assets and owners.
- Test results: unit, integration, data quality checks.
- Backfill or migration plan with success criteria.
- Rollback plan with exact commands and triggers to execute it.
- Communication plan (who, what changes, when, action needed).
Worked examples
Example 1: Add nullable column to a warehouse table
- Type: Low risk, backward compatible (add column customer_tier, nullable).
- Checks: Schema linter passes; no breaking lineage detected.
- Approvals: Asset Owner.
- Pre-prod: Staging deploy, sample load; data profile OK.
- Release: Off-peak window, notify analytics team.
- Closure: Update catalog, add definition for customer_tier.
Example 2: Rename column used by finance reports
- Type: High risk (rename revenue to total_revenue).
- Checks: Lineage shows 7 downstream dashboards; unit tests updated; compatibility shim (view alias) proposed.
- Approvals: Owner + Steward + Domain Lead. Finance stakeholder informed and sign-off on timing.
- Pre-prod: Shadow view provides both names; QA verifies dashboards.
- Release: Two-step release (deploy shim, then update consumers, then remove old name in 2 weeks).
- Closure: Archive shim removal plan; post-release monitoring for broken queries.
Example 3: Tag column as PII and restrict access
- Type: Medium to High (privacy classification change on email).
- Checks: Data profiling confirms PII; access policies adjusted; logs configured.
- Approvals: Owner + Steward + Compliance/Security.
- Pre-prod: Test masking policy in staging; confirm role-based access works.
- Release: Communicate policy change and new roles required.
- Closure: Update catalog classification; attach policy evidence and approvals.
Exercises (hands-on)
Complete these exercises to design a robust approval workflow. A sample checklist is included to self-validate.
Exercise 1: Risk-tiered workflow
See details below and submit your answers in your notes or tool of choice.
Exercise 2: RACI and state machine
See details below and submit your answers in your notes or tool of choice.
Exercise checklist
- Defined at least 3 risk tiers with clear criteria.
- Mapped approvers per tier (Owner, Steward, Compliance/Security as needed).
- Listed required artifacts (impact analysis, tests, rollback, comms).
- Outlined states from Draft to Closed with exit criteria.
- Included emergency change path with audit and 24–48h retrospective review.
Common mistakes and how to self-check
- Missing rollback plan: Verify you have clear steps, triggers, and data restore points.
- Unclear ownership: Confirm asset owner and steward are named with contacts.
- Over-approval for low risk: Ensure low-risk changes have a fast path.
- Skipping staging: Check that every medium/high risk change shows pre-prod evidence.
- No communication plan: Ensure impacted teams and timelines are listed.
- Ignoring data quality: Confirm post-release monitoring KPIs are defined.
Self-check prompts
- If this goes wrong, how will we know within 30 minutes?
- Can I revert within one release window without data loss?
- Who will be surprised by this change? Add them to communications.
Practical projects
- Create a change-approval template (intake form) with required fields and attach it to your team workflow.
- Implement a staging validation checklist that engineers must fill before requesting approval.
- Set up a release notes format and send a mock announcement for a breaking change.
- Document a sample rollback playbook for a failed schema migration.
Learning path
- Start: Understand roles and risk tiers.
- Then: Define states and exit criteria (state machine).
- Next: Automate checks (tests, lineage) and evidence collection.
- Then: Add communication and post-release monitoring.
- Finally: Add exception handling (emergency path) and SLAs.
Mini challenge
Your team must deprecate metric gross_margin_v1 in 3 weeks while keeping dashboards stable. Draft: risk tier, approvers, compatibility plan (alias or semantic mapping), communication timeline, and rollback trigger. Keep it to 8–10 bullet points.
Next steps
- Adopt the intake template for all changes starting next sprint.
- Pilot the workflow on a low-risk change and iterate based on feedback.
- Schedule a 30-minute training with stewards and owners to align on roles and SLAs.
Quick Test
Everyone can take this test for free. Note: progress and scores are saved only for logged-in users.