Information governance (IG) is the strategy, accountability, and control system for how an organization collects, classifies, uses, protects, shares, retains, and disposes of information across its entire lifecycle. It is:
- Scope‑wide: Covers structured data, unstructured content, model artifacts, code, dashboards, and records (including legal/records management and privacy).
- Lifecycle‑aware: From intake and creation → active use → archival → retention/disposition and legal holds.
- Outcome‑driven: Balances value (insights, automation, personalization) with risk (security, privacy, ethics, legal/regulatory).
Where data governance focuses on data as an asset, information governance focuses on information as a liability and an asset—linking value creation with lawful, ethical, and secure handling.
“Isn’t information governance the same as data governance?”
Short answer: no. Data governance manages data as an asset—definitions, ownership, quality, lineage, and access. Information governance is broader: it’s the enterprise strategy and accountability for information in any form—structured data, documents, messages, logs, model artifacts, code, and dashboards—covering why you keep it, how you classify and use it lawfully and ethically, how long you retain it, and how you protect and dispose of it.
Put simply, IG sets the rules of the game (purpose, classification, retention, privacy, security), and the domain disciplines—data governance, AI governance, application governance, and analytics governance—apply those rules within their workflows and tools.
If you’ve ever tried to improve model quality, de‑risk a new app, or rein in dashboard sprawl and still felt like you were fixing symptoms instead of causes, you’ve met the absence of information governance. It’s the backbone that makes the other disciplines pull in the same direction.
This post lays out a practical, non‑theoretical way to make that backbone real: what information governance is, how it relates to the other governance disciplines, and how to run them as a single operating model—without slowing down delivery.
How IG Integrates With Other Governance Disciplines
Think of IG as the constitution. The other four domains are branches that implement it.
1) Data Governance (DG)
- What it adds: Ownership & stewardship, metadata/catalog, lineage, quality rules, mastering, access controls.
- How IG connects: IG defines classification, lawful purpose, retention, and cross‑border rules; DG implements them at the dataset/table/field level and proves compliance with lineage and quality evidence.
2) AI Governance (AIG)
- What it adds: Model lifecycle standards (design → train → validate → deploy → monitor → retire), risk controls (bias, privacy, robustness), documentation (model cards, datasheets), human‑in‑the‑loop.
- How IG connects: IG sets the boundaries (permitted/forbidden uses, sensitive features, consent limits, explainability thresholds, data minimization). AIG enforces them in ML pipelines, registries, and monitoring (drift, incidents).
3) Application Governance (AppGov)
- What it adds: SDLC controls (requirements → design → test → release), threat modeling, SAST/DAST/OSS compliance, secrets & key management, environment segregation, change control.
- How IG connects: IG ensures apps handle information per classification & retention rules (e.g., no PII in logs, masking in non‑prod, retention timers, lawful‑purpose checks), and that app features using AI or data follow the same policies.
4) Analytics Governance (AnGov)
- What it adds: Metric definitions & a semantic layer, report certification, reproducible notebooks, PII in visualizations, sharing/audience controls, auditability.
- How IG connects: IG requires transparency, minimum aggregation for sensitive domains, and approved purposes; AnGov enforces consistent metrics and prevents “rogue” analytics from leaking sensitive data.
A Practical Crosswalk (What Integrates With What)
Policy → Control → Evidence is the backbone pattern. Below is a compact mapping you can adopt:
| Category | IG Policy/Standard | Data Gov Implementation | AI Gov Implementation | App Gov Implementation | Analytics Gov Implementation |
|---|---|---|---|---|---|
| Classification | Information classification standard | Tag data assets; propagate via lineage | Mark training/serving datasets; block sensitive features | Enforce no PII in logs; mask in non‑prod | Enforce minimum aggregation; watermark reports |
| Lawful Purpose | Acceptable use & lawful basis | Purpose tags on datasets; sharing approvals | Use‑case registry & approvals; model cards include lawful basis | Feature toggles tied to purpose; consent checks | Certified dashboards list approved audiences/purposes |
| Privacy | Privacy & data minimization | Column‑level policies, masking | Sensitive feature review; differential privacy (when needed) | Secrets mgmt; data in transit/at rest | Pseudonymize where possible; PII linting for visuals |
| Retention | Retention schedule & legal hold | Dataset retention/disposal jobs | Model & dataset version retention | Log retention & scrubbing | Report/archive retention; link to records mgmt |
| Security | Access control, least privilege | Role‑based access; entitlements tied to owners | Model registry permissions; signed artifacts | SDLC gates, SAST/DAST, dependency scanning | Share controls; viewer roles & audit logs |
| Quality | Fitness for purpose | DQ rules, SLAs, incident mgmt | Performance/robustness SLAs; drift alerts | Release criteria; reliability SLOs | Metric definitions and tests; cert workflow |
| Transparency | Accountability & explainability | Data source lineage | Model cards; explanation coverage targets | User‑facing notices for AI features | Metric catalog; report certification badges |
| Monitoring | Continuous assurance | DQ pass rate, lineage coverage | Bias/drift/attack monitoring; incident runbooks | App telemetry; security events | Dashboard usage, refresh, PII checks |
Artifacts That Make IG Real (and Lightweight)
- Information Governance Policy (2–3 pages): Principles, scope, decision rights, exceptions.
- Classification & Handling Standard: Data classes (e.g., Public, Internal, Confidential, Restricted) with required controls per class.
- Retention Schedule: Key record categories, durations, systems of record, legal hold procedure.
- Acceptable AI Use Standard: Approved/blocked AI use cases; sensitive attributes; human oversight.
- Analytics Standard: Definitions of certified content; required documentation; sharing rules.
- Control Library: 20–30 named controls with IDs (e.g., IG‑CLF‑01 Classification Tagging; IG‑RET‑02 Retention Jobs; IG‑AI‑05 Bias Testing).
- Decision Rights (RACI): Who proposes, who approves, who operates, who assures.
- Exceptions Process: Time‑bound, risk‑based, logged; reviewed monthly.
Keep each artifact short; automate enforcement where possible (“policy as code”).
Roles and Decision Rights
- Information Owner (Business): Accountable for lawful purpose, retention, sharing approvals.
- Data Steward (Domain): Responsible for classification, metadata, quality rules, lineage.
- Model Owner (Product/ML): Responsible for model cards, validation, monitoring, rollback.
- Application Owner (Eng): Responsible for SDLC controls, secrets, logs, environment segregation.
- Analytics Lead (BI): Responsible for metric definitions, certification, audience controls.
- Information Security & Privacy: Consulted/Approver for controls, exceptions, incidents.
- Records/Legal: Approver for retention, legal hold, eDiscovery.
- IG Council: Tie‑breaker and policy steward; reviews metrics and exceptions.
The “Starter” Control Set (Minimal, High‑Leverage)
- IG‑CLF‑01: Every asset must have a classification label.
- IG‑PUR‑02: Every dataset/model/app/report must declare a lawful purpose.
- IG‑ACC‑03: Access must be role‑based and time‑bound for Restricted data.
- IG‑LOG‑04: No PII in application logs (enforced by blocklist tests).
- IG‑RET‑05: Retention jobs run and report evidence monthly.
- IG‑LIN‑06: Lineage must capture upstream/downstream for certified assets.
- IG‑DQ‑07: Critical datasets have at least three quality rules with SLAs.
- IG‑AI‑08: Models affecting people require bias testing and a model card.
- IG‑AI‑09: Models must have rollback plans and monitored drift thresholds.
- IG‑SDLC‑10: All releases pass static/dep scans and secret scanning.
- IG‑TDM‑11: Non‑prod uses masked/synthetic data for Restricted classes.
- IG‑ANA‑12: Certified dashboards use governed metric definitions.
- IG‑SHR‑13: External sharing requires owner approval & purpose check.
- IG‑PII‑14: Prompt/output PII checks for generative AI features.
- IG‑HITL‑15: High‑risk AI decisions include human‑in‑the‑loop.
- IG‑EVD‑16: Control evidence stored with immutable timestamps.
- IG‑EXC‑17: Exceptions are time‑bound, risk‑rated, and reviewed.
- IG‑INC‑18: Incident response playbooks exist for data/AI/app/analytics.
- IG‑TRN‑19: Annual training proportionate to role and data class.
- IG‑AUD‑20: Quarterly IG council reviews metrics & exceptions.
Operating Model: From Idea to Evidence
1) Intake
A single front door for new data sources, AI use cases, applications, and analytics. Capture purpose, data classes, users, and risks.
2) Triage & Review
- Low‑risk, standard patterns auto‑approve with controls baked into templates.
- Higher‑risk (e.g., Restricted PII, high‑impact AI) routes to privacy/security/legal and the model risk reviewer.
3) Build With Guardrails
- Templates for pipelines, notebooks, dashboards, and apps that pre‑wire classification, logging policies, quality tests, and secrets management.
- For AI: registered datasets, reproducible training, evaluation tests, model cards.
4) Release & Certify
- App releases pass SDLC gates.
- Models meet validation thresholds and bias checks with a rollback plan.
- Reports pass metric checks and certification.
5) Monitor & Assure
- Dashboards for DQ pass rate, lineage coverage, model drift, app security posture, report usage and PII checks.
- Incidents handled with common runbooks; post‑incident reviews feed back into controls.
6) Evidence & Audits
- Control evidence (logs, reports, approvals) retained and searchable.
- Quarterly council reviews metrics and exceptions; iterate the control set.
Metrics That Matter (Per Domain)
- IG (overall): % assets classified; % with declared purpose; # exceptions open/closed; audit readiness score.
- Data: DQ pass rate for critical datasets; lineage coverage %; time‑to‑approve data sharing requests.
- AI: % models with cards; # bias/drift alerts and median time‑to‑mitigate; % high‑risk AI with HITL.
- Apps: % releases passing security scans; secrets exposure incidents; non‑prod masking coverage %.
- Analytics: % certified dashboards; metric definition reuse rate; PII lint warnings per 100 reports.
Keep 6–10 KPIs visible; automate collection from your platforms.
Applied Scenario: One Business Capability, Many Disciplines
Capability: Customer 360 personalization with reports and an in‑app recommendation model.
- Information Governance: Classifies customer data as Restricted, defines lawful purposes (service & marketing with consent), sets 2‑year retention for certain interaction logs, requires HITL for adverse‑impact decisions.
- Data Governance: Uses a catalog/lineage to mark sources, Data Vault (DV 2.0) entities, and downstream marts; enforces masking in non‑prod; sets three DQ rules (unique customer ID, valid consent flag, email format).
- AI Governance: Model card documents training data, performance by segment, and bias tests; drift monitor set on consent mix and click‑through; rollback plan defined.
- Application Governance: App integrates secrets via a vault; no PII in logs; feature flag disables recommendations if consent drops below threshold.
- Analytics Governance: Certified dashboards use approved metrics; audience restricted to the CRM team; reports show only aggregated metrics for external sharing.
All five deliverables are traceable back to the same IG controls and produce the same kind of evidence.
Common Pitfalls (and How to Avoid Them)
- Too much paper, not enough automation. Keep policies short and convert them to guardrails in templates and CI/CD.
- Governance as a blocker. Pre‑approve standard patterns; reserve reviews for genuinely high‑risk items.
- Orphaned ownership. Publish owners for critical assets and make ownership a prerequisite for access.
- One‑off controls per team. Centralize the control library; decentralize the implementation via templates.
Final Thought
Information governance isn’t a new bureaucracy. It’s the common operating system that makes data governance, AI governance, application governance, and analytics governance interoperable—so you can move faster and safer. Start small: a short policy, a handful of high‑leverage controls, and guardrails in your delivery templates. Then iterate. The payoff is cumulative and real: fewer surprises, faster approvals, clearer accountability, and better outcomes.
This is not legal advice. Adapt to your regulatory context.
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