DATA GOVERNANCE THAT PEOPLE ACTUALLY FOLLOW

Quick Answer: NUUN Digital designs and implements data governance programs that operating teams adopt — catalog, lineage, quality monitoring, stewardship, and policy enforcement. Built for regulated industries (financial services, healthcare, public sector) and data-intensive enterprises. Governance that's a roadblock gets bypassed; ours doesn't.

WHAT WE DELIVER

Governance framework. Roles, responsibilities, and decision rights.
Data catalog implementation. Atlan, Collibra, Alation, or open-source alternatives.
Data quality program. Monitoring, alerting, and remediation workflows.
Stewardship operating model. Who owns what, who approves what, when.
Policy design. Access, retention, consent, and compliance per jurisdiction.
Regulatory mapping. PIPEDA, GDPR, CCPA, HIPAA, Quebec Law 25, and industry-specific rules.

HOW WE DO IT

Map the current state. Who owns what data today, what controls exist.
Design for the risk profile. Over-governing slows; under-governing fails. Match controls to risk.
Implement with lightweight tools. Catalog and quality platforms that integrate with existing stack.
Install stewardship. People, processes, and operating rhythms that keep governance alive.
Measure governance effectiveness. Adoption, issue-resolution time, and audit-readiness.

WHEN IT FITS

Regulated industries with audit and compliance requirements.
Post-incident governance rebuild (data breach, compliance failure).
Enterprise scaling past the point where ad-hoc governance holds.
Pre-AI governance prep (AI systems demand cleaner data provenance).

SELECTED WORK

Financial services client — Governance + catalog rollout → audit-finding resolution time down [X]%. Read case →
Healthcare client — HIPAA-compliant governance framework → passed [X] external audits. Read case →

SOURCES & FURTHER READING

Data Management practice
Data architecture and modeling
Privacy policy reference
PIPEDA — https://www.priv.gc.ca/
NIST Data Governance — https://www.nist.gov/

Frequently asked.

Do we need a data catalog?

If you have more than a few dozen analysts or a complex data landscape, yes. Below that scale, lightweight documentation (dbt docs, wiki) may suffice. We recommend based on organizational scale and risk profile.

Which catalog platform should we use?

Atlan for modern, usability-focused implementations; Collibra for enterprise-heavyweight needs; Alation for data-democratization focus; open-source (DataHub, OpenMetadata) for cost-sensitive or highly customized scopes.

How do you drive adoption?

By making governance useful, not just required. Catalogs that help analysts find data faster get used. Governance meetings without outcomes get bypassed. Adoption is a design problem.

How do you handle consent and privacy in governance?

Consent signals flow through the governance layer — what can be used where, for whom, under which policy. Privacy-preserving techniques (tokenization, masking, differential privacy) applied where appropriate.

What about AI governance?

Data governance is foundational to AI governance. AI-specific governance (model risk, bias, monitoring) builds on data governance. We deliver both; see [AI & Digital Transformation](/services/ai-digital-transformation/).