Centium Advisors - Data & AI Governance Assessment

Evaluate your organization's Data Governance maturity, identify critical gaps, and receive a prioritized roadmap — in minutes, not months.

Quick Diagnostic

Basic Assessment

A rapid diagnostic covering all governance dimensions at a strategic level. Ideal for an initial pulse-check before executive discussions.

⏱ 5 minutes 📋 12 questions 📊 Instant scorecard
🔒 Comprehensive

Deep Assessment

A thorough evaluation across all capability dimensions with weighted scoring, detailed gap analysis, and a prioritized 90-day remediation roadmap.

⏱ 20–25 minutes 📋 42 questions 📊 Full report + AI readiness
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Section 1 of 6
Section 1 of 6

Strategy & Leadership

How governance connects to business objectives and executive commitment

1. Does your organization have a clearly defined data governance vision linked to measurable business outcomes?
2. Is there active executive sponsorship (CIO, CDO, or equivalent) for data governance?
Section 2 of 6

People & Ownership

Clarity of roles, accountability, and organizational structure

3. Are data ownership roles (owners, stewards, custodians) formally defined and actively filled?
4. Is there a functioning cross-functional governance council or committee?
Section 3 of 6

Process & Workflows

Standardization and operational integration of governance activities

5. Are data lifecycle processes (creation, quality checks, certification, use, retirement) defined and followed?
6. Is there a documented process for data issue resolution with clear routing and SLAs?
Section 4 of 6

Technology & Automation

Tooling maturity for discovery, lineage, quality, and enforcement

7. Does your organization have automated data discovery and a searchable data catalog?
8. Is end-to-end data lineage available and actively used for impact analysis?
Section 5 of 6

Policy & Compliance

Enforceable standards, classification, and regulatory alignment

9. Are data classification policies (public, internal, confidential, restricted) defined and enforced?
10. How prepared is your organization for regulatory audits (GDPR, HIPAA, SOX, EU AI Act)?
Section 6 of 6

AI Governance Readiness

Controls, monitoring, and accountability for AI systems

11. Does your organization have an inventory of all AI models, agents, and applications in use?
12. Are AI outputs monitored for bias, drift, and explainability?
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Section 1 of 7
Section 1 of 7 — Strategy & Leadership

Strategic Alignment & Executive Commitment

Assessing how deeply governance is embedded in organizational strategy

1. Is there a documented data governance strategy with explicit links to business outcomes?
2. Is governance performance reported to the board or senior leadership with defined KPIs?
3. Is there a dedicated budget for data governance initiatives?
4. How well does your governance program handle change management and cultural adoption?
5. Is governance scope clearly defined with tiered coverage based on data criticality?
6. Is governance ROI measured and communicated to justify continued investment?
Section 2 of 7 — People & Ownership

Roles, Accountability & Organizational Structure

Evaluating ownership and accountability distribution

7. Are Data Owners formally assigned for all critical data domains?
8. Are Data Stewards actively managing day-to-day quality and policy adherence?
9. Does the organization use a federated governance model?
10. Are governance roles supported with training and skill development?
11. Do data producers actively participate in governance?
12. Is there executive-level accountability for data governance outcomes?
Section 3 of 7 — Process & Workflows

Operational Processes & Lifecycle Management

How governance operates day-to-day across the data lifecycle

13. Is there a defined data lifecycle with checkpoints from creation to retirement?
14. Is lineage-driven impact analysis performed before changes to critical data?
15. How quickly can data issues be detected and resolved (MTTR)?
16. Is there a standardized data access request and approval process?
17. Are governance policies versioned with formal review cycles?
18. How effectively are policy exceptions handled?
Section 4 of 7 — Technology & Automation

Tooling, Automation & Technical Infrastructure

The technology foundation enabling governance at scale

19. Is data asset discovery automated across your entire data estate?
20. Does your infrastructure support column-level lineage end-to-end?
21. Is data quality continuously monitored with automated alerting?
22. Are access controls automated, role-based, and context-sensitive?
23. Is governance embedded in tools teams use daily (SQL editors, BI, notebooks)?
24. Does your metadata infrastructure support active metadata (event-driven, real-time)?
Section 5 of 7 — Policy & Compliance

Enforceable Standards & Regulatory Readiness

Rules, classifications, and compliance posture

25. Is data classified by sensitivity with appropriate handling rules?
26. Are data quality standards defined with measurable benchmarks?
27. Are retention and disposal rules defined and enforced?
28. Are privacy requirements mapped to specific data assets with controls?
29. Are policies implemented as machine-readable code (policy-as-code)?
30. How audit-ready is your organization for regulatory examinations?
Section 6 of 7 — Data Quality

Quality Management & Reliability

How data quality is measured, maintained, and improved

31. Are quality dimensions (accuracy, completeness, consistency, timeliness) measured systematically?
32. Is root-cause analysis performed when quality issues are detected?
33. How much time do users spend searching for and cleaning data?
34. Are quality gates in place before data enters critical systems (dashboards, AI)?
35. Does leadership trust the data used for strategic decisions?
36. Is the cost of poor data quality quantified?
Section 7 of 7 — AI Governance

AI Governance & Responsible AI Readiness

Controls, monitoring, and accountability for AI systems

37. Is there a complete inventory of all AI models, agents, and applications?
38. Are AI systems classified by risk level with proportional controls?
39. Is there end-to-end model lineage from training data to outputs?
40. Are AI outputs monitored for bias and fairness?
41. Is performance drift detected when live data diverges from training?
42. Are prompts, AI inputs, and model access governed with version control?
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Assessment Results

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Capability Dimension Scores

🚨 Priority Gap Analysis

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