Analytics Maturity & Data Governance Audit

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1. <section> <h2>Why an Analytics Maturity & Data Governance Audit?</h2> <p>Most analytics problems aren’t solved by another dashboard or a new tool. They begin with unclear ownership, inconsistent data, and decision processes that don’t link to measurement. This audit gives you a practical, evidence-led view of where your analytics practice helps — and where it gets in the way — so you can prioritize realistic improvements without chasing shiny technology.</p> <h3>What this resource will help you do</h3> <ul> <li>See your analytics strengths and risks across practical domains like data quality, ownership, tooling, skills, and decision workflows.</li> <li>Collect evidence, score consistently, and translate findings into prioritized, time-boxed actions.</li> <li>Create a repeatable cadence for measuring progress so improvements are visible and sustained.</li> </ul> <h3>Who should use this</h3> <p>This audit is useful for small and mid-size organizations, service teams, operations managers, analytics leads, and cross-functional improvement teams. You don’t need a dedicated data governance office to start — you need honest evidence, a few accountable people, and a commitment to follow up.</p> <h3>Quick way to get value now</h3> <p>1) Run the included audit form with a small cross-functional team. 2) Use the guide to interpret scores and pick 2–4 highest-impact actions. 3) Run a 30-minute action sprint to assign owners and timelines. That sequence turns diagnosis into immediate, measurable change.</p> <h3>How this resource is organized</h3> <p>Use the interactive audit tool to collect scores and evidence, consult the maturity reference for what each level looks like in practice, follow the how-to guide to create a prioritized roadmap, and use the quick checklist when you need a rapid team alignment session.</p> </section>
2. { "FormType": "InteractiveForm", "Title": "Analytics Maturity & Data Governance Audit", "IntroductionHtml": "<p>Use this structured form to score current capabilities and record concise evidence. Run it with a 2–6 person cross-functional group: operations, analytics, product or service owner, and IT/security if available. Aim for evidence-backed scores (notes or examples). When complete, use the guide to interpret results and build a prioritized improvement plan.</p>", "SubmitLabel": "Save Audit", "SuccessMessage": "Audit saved. Use the how-to guide to interpret scores and create a prioritization roadmap.", "DataType": "AnalyticsMaturityAudit", "SchemaVersion": 1, "Fields": [ { "Key": "organization", "Label": "Organization / team name", "HelpText": "Team, unit, or org this audit covers", "FieldType": "text", "IsRequired": true, "Placeholder": "e.g., Customer Success, Plant 3, Finance", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "assessor", "Label": "Assessor name", "HelpText": "Primary person completing this form", "FieldType": "text", "IsRequired": true, "Placeholder": "First Last", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "assessment_date", "Label": "Assessment date", "HelpText": "YYYY-MM-DD", "FieldType": "text", "IsRequired": true, "Placeholder": "2026-07-31", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "data_quality_score", "Label": "Data quality: How reliable and consistent is core data? (1=poor, 5=trusted)", "HelpText": "Consider completeness, accuracy, timeliness, and reconciliation practices", "FieldType": "scale", "IsRequired": true, "Placeholder": "", "Minimum": 1.0, "Maximum": 5.0, "Options": [] }, { "Key": "data_quality_evidence", "Label": "Evidence / examples for data quality score", "HelpText": "Short notes: known gaps, sample issues, reconciliation steps", "FieldType": "textarea", "IsRequired": false, "Placeholder": "e.g., 'Customer email missing in 12% of records; nightly ETL drops rows when X condition occurs'", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "metadata_catalog_score", "Label": "Metadata & cataloging: Are datasets discoverable, documented, and labeled?", "HelpText": "Include data dictionary availability, lineage notes, and findability", "FieldType": "scale", "IsRequired": true, "Placeholder": "", "Minimum": 1.0, "Maximum": 5.0, "Options": [] }, { "Key": "tooling_infrastructure_score", "Label": "Tooling & infrastructure: Are tools reliable, integrated, and fit-for-purpose?", "HelpText": "Consider reporting platforms, ETL, storage, and integration quality", "FieldType": "scale", "IsRequired": true, "Placeholder": "", "Minimum": 1.0, "Maximum": 5.0, "Options": [] }, { "Key": "ownership_roles_score", "Label": "Ownership & roles: Are responsibilities for data and analytics clear and practiced?", "HelpText": "Look for data owners, stewards, and accountable decision roles", "FieldType": "scale", "IsRequired": true, "Placeholder": "", "Minimum": 1.0, "Maximum": 5.0, "Options": [] }, { "Key": "governance_policies_score", "Label": "Governance & policies: Are standards, access rules, and change controls in place?", "HelpText": "Include retention, access, approval, and versioning practices", "FieldType": "scale", "IsRequired": true, "Placeholder": "", "Minimum": 1.0, "Maximum": 5.0, "Options": [] }, { "Key": "skills_competency_score", "Label": "Skills & competency: Does the team have the skills to analyze, model, and translate data to decisions?", "HelpText": "Consider analysts, translators, and decision-makers' comfort with data", "FieldType": "scale", "IsRequired": true, "Placeholder": "", "Minimum": 1.0, "Maximum": 5.0, "Options": [] }, { "Key": "analytics_workflow_score", "Label": "Analytics workflow & decisioning: Do analytics outputs link to clear decisions and follow-up?", "HelpText": "Look for decision owners, cadences, experiments, and learning loops", "FieldType": "scale", "IsRequired": true, "Placeholder": "", "Minimum": 1.0, "Maximum": 5.0, "Options": [] }, { "Key": "security_privacy_score", "Label": "Security & privacy: Are controls in place to protect data and comply with rules?", "HelpText": "Consider access controls, anonymization, and compliance", "FieldType": "scale", "IsRequired": true, "Placeholder": "", "Minimum": 1.0, "Maximum": 5.0, "Options": [] }, { "Key": "monitoring_observability_score", "Label": "Monitoring & observability: Are data pipelines and models monitored for failures and drift?", "HelpText": "Include alerts, SLAs, and routine checks", "FieldType": "scale", "IsRequired": true, "Placeholder": "", "Minimum": 1.0, "Maximum": 5.0, "Options": [] }, { "Key": "use_cases_impact_score", "Label": "Use-case alignment & impact: Do analytics projects deliver measurable business or operational outcomes?", "HelpText": "Look for metrics, experiments, and tracked outcomes", "FieldType": "scale", "IsRequired": true, "Placeholder": "", "Minimum": 1.0, "Maximum": 5.0, "Options": [] }, { "Key": "top_3_gaps", "Label": "Top 3 gaps (brief)", "HelpText": "From the scores and evidence, list the three most important gaps to address", "FieldType": "textarea", "IsRequired": false, "Placeholder": "e.g., 'No data owner for transactions; ETL fails nightly; no KPI owners'", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "suggested_first_actions", "Label": "Suggested first actions and owners", "HelpText": "Short, time-boxed actions (owner, 30/60/90 days)", "FieldType": "textarea", "IsRequired": false, "Placeholder": "e.g., 'Assign data steward for orders — Sam — 30 days'", "Minimum": null, "Maximum": null, "Options": [] } ] }