Why this matters
As an AI Product Manager, you decide what data powers your models and how it is used. Clear data governance and ownership reduce risk, speed up approvals, and keep models reliable. You will be asked to identify data owners, set access rules, prove compliance, and respond to incidents or user requests. Good governance turns these into repeatable routines instead of last-minute fire drills.
- Ship faster: pre-agreed policies and owners cut sign-off time.
- Lower risk: avoid misuse, leaks, and non-compliance.
- Better models: consistent, high-quality, well-documented data.
Concept explained simply
Data governance is the set of people, rules, and processes that make data usable, safe, compliant, and ethical. Data ownership tells you who is ultimately accountable for a dataset or data domain.
Think of each dataset like a shop: the Owner holds the business license and sets policies; the Steward is the shop manager who keeps things running (quality, documentation, access); Producers stock the shelves (ingest/generate data); Consumers are your teams that use the data; Security/Privacy are your safety inspectors. Everyone knows their job, so the shop can open on time.
Mental model: The Data Product Contract
A practical way to reason about governance is a "Data Product Contract" that answers:
- What gets in: sources, schema, quality thresholds.
- Who can touch it: roles, approvals, logging.
- What it can become: allowed uses, constraints.
- When it must be deleted: retention, disposal triggers.
If it is not in the contract, it is not allowed by default.
Key components you should know
- Ownership and stewardship: accountable owner; operational steward.
- Data inventory and classification: what data exists and its sensitivity (e.g., public, internal, confidential, personal data).
- Access control: least-privilege, approval flow, logging.
- Privacy and compliance: purpose limitation, minimization, user consent preferences, handling user requests.
- Data quality and lineage: accuracy, completeness, timeliness; where data came from and how it changed.
- Retention and deletion: time limits, deletion workflows, backups.
- Risk management and ethics: bias, harmful use, re-identification risk, misuse scenarios.
Worked examples
Example 1: Ownership for a personalization dataset
Scenario: You maintain a feature store built from web and app events. Marketing and Recommendations teams use it.
- Owner: Head of Growth (business outcomes, compliance accountability).
- Steward: Analytics Engineering lead (schema, documentation, access approvals).
- Access: Analysts and Data Scientists via project-scoped roles; production services via service accounts.
- Privacy: honors user consent for personalization; events without consent excluded.
- Retention: raw events 12 months; features 6 months; model outputs 90 days.
Result: Faster approvals and fewer debates, because roles, consent logic, and retention are pre-defined.
Example 2: Vendor enrichment data
Scenario: You purchase firmographic enrichment for B2B leads.
- Owner: Sales Ops.
- Steward: Data Platform PM.
- License guardrails: allowed for internal analytics and model training; redistribution prohibited.
- Access: Sales, RevOps, Data Science; training sets track source field-level lineage.
- Retention: as per license (e.g., refresh quarterly; delete upon contract end).
Result: Your training pipeline checks license flags before including fields, preventing accidental misuse.
Example 3: Support transcripts for an LLM assistant
Scenario: You train an assistant on support chat transcripts.
- Owner: Head of Support.
- Steward: NLP Lead.
- Minimization: remove payment data, redact emails/phone numbers prior to training.
- Purpose: quality improvement and assistance suggestions; no use for advertising.
- User requests: enable deletion of a user's transcripts from training cache in the next retrain cycle.
Result: Clear redaction and retraining procedure reduces privacy risk and user complaints.
Step-by-step: Create a lightweight Data Governance Canvas
Define business purpose and what is in scope (datasets, features, outputs).
List fields and sensitivity (e.g., personal data, confidential, public).
Assign Owner, Steward, Producers, Consumers, Privacy/Security approvers (RACI).
Define who can access, approval workflow, least-privilege roles, and logging.
Set acceptance thresholds and track source-to-feature lineage.
State lawful basis/purpose limits, consent handling, and user request process.
Encryption, masking, environment segregation, key management.
Define retention timeline, deletion triggers, and backup policies.
What gets measured (access, drift, incidents) and who reviews when.
Where docs live; how consumers onboard; change notification process.
Copy-paste Canvas template
Data Product: [Name] Purpose: [Business outcome] Scope: [Datasets/features/outputs] Classification: [Field -> sensitivity] Owner / Steward: [Names/roles] Producers / Consumers: [Teams] Access: [Roles, approvals, logging] Privacy: [Purpose, consent, minimization] Quality: [SLOs, tests] Lineage: [Sources -> transforms -> outputs] Security: [Controls] Retention: [Timelines, deletion] Monitoring: [Metrics, review cadence] Change mgmt: [How updates are communicated]
Exercises
Do these to cement the skill. Everyone can complete the exercises; if you sign in, your progress will be saved.
Exercise 1 (ex1) — Draft a RACI for a training dataset
Create a one-page RACI for a behavioral events dataset used in model training.
- List the key decisions (schema changes, access approvals, retention updates, incident response, user request handling).
- Assign Responsible, Accountable, Consulted, Informed for each decision.
Exercise 2 (ex2) — Access decision matrix
Design a role-based access matrix for a feature table with PII, derived segments, and model outputs.
- Define roles (e.g., DS-Research, DS-Prod, Analyst, Support, ServiceAccount-Prod).
- Specify access level per field group (none, masked, aggregate-only, full).
- Add approval and logging requirements.
Exercise 3 (ex3) — Retention plan and deletion workflow
Propose retention for raw logs, curated features, and model outputs. Document the deletion workflow and how backups are handled.
Checklist: what good looks like
- One clear Owner and one Steward are named.
- Least-privilege roles defined and documented.
- Consent and purpose limits documented at dataset level.
- Retention windows and deletion triggers are explicit.
- Lineage recorded from source to model output.
- Access requests require approval and are logged.
- Incident and user request playbooks exist.
Common mistakes and how to self-check
- Mistake: No single accountable owner. Self-check: Can you name one decision-maker who can approve or stop a change?
- Mistake: Over-broad access. Self-check: Can every person justify why they need each field today?
- Mistake: Vague purpose. Self-check: Can you explain the exact allowed uses of this dataset in one sentence?
- Mistake: Indefinite retention. Self-check: Do you have a date or condition that triggers deletion?
- Mistake: Missing lineage. Self-check: Can you trace any model feature back to its source field?
- Mistake: Ignoring user requests. Self-check: Is there a documented route to exclude or delete a user’s data in retraining?
- Mistake: License blind spots for vendor data. Self-check: Are use, share, and retention limits copied into your data product contract?
Who this is for
- AI Product Managers responsible for data-powered features.
- Data/ML Product Managers and Tech Leads coordinating data use.
- Analytics Engineering and MLOps partners who operationalize policies.
Prerequisites
- Basic ML lifecycle understanding (ingest → features → train → deploy → monitor).
- Familiarity with personal data concepts (PII, consent, minimization, pseudonymization).
- Comfort with role-based access control and environments (dev/staging/prod).
Learning path
- Start: Draft the Data Governance Canvas for one high-value dataset.
- Then: Create a RACI and access matrix; review with Legal/Security.
- Next: Add retention and deletion workflows; test on a staging copy.
- Finally: Roll out approval and logging, and schedule quarterly reviews.
Practical projects
- Implement a consent-aware data pipeline that excludes events without required consent and logs decisions.
- Add field-level lineage to a feature store and surface it in documentation.
- Automate a retention job with sample deletion reports reviewed monthly.
Mini challenge
In one page, define governance and ownership for using customer support transcripts to train an LLM: set purpose limits, redaction, access roles, owner/steward, retention, and user request handling. Aim for clarity that a new teammate could follow without extra context.
Quick Test
Everyone can take the test. If you sign in, your progress and score will be saved to your learning profile.