Analytics Maturity Self-Assessment (Lean Audit)

A guided, scored self-assessment that collects structured ratings and evidence across 8 maturity dimensions, stores responses for later review, and creates an exportable one-page leadership summary and prioritized next steps.

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Analytics Maturity Self-Assessment (Lean Audit)

Welcome

This lean audit helps teams quickly surface the highest-impact gaps in analytics maturity so you can prioritize improvements that reduce risk and accelerate value. It collects a scored rating and brief evidence for each dimension, stores your responses, and produces a concise one-page summary you can share with stakeholders.

How to use this form

For each dimension, choose the score that best matches your current state and paste or describe a short piece of evidence (systems, reports, examples, people, or metrics) that supports the score. Scores are on a 0–4 maturity scale (definitions below). Answer honestly — the assessment is most useful when it highlights meaningful constraints, not to make things look perfect.

Scoring rubric

  • 0 — None: No capability, inconsistent or absent practices.
  • 1 — Ad hoc: Occasional activity, individual heroic effort, limited repeatability.
  • 2 — Repeatable: Basic processes and tooling exist; results are inconsistent across teams.
  • 3 — Defined: Clear ownership, standardized processes, reliable data and tooling across most use cases.
  • 4 — Optimized: Proactive, automated pipelines, governed metadata, integrated decision workflows, continuous improvement and measurement.

Outcome

After you submit, the assessment saves structured responses. Use the saved data to create a heatmap, export a one-page leadership summary, or generate prioritized next steps. (See Capability Notes for automated visualization and export improvements.)

Name of the business unit, site, or team being assessed.
Person completing the assessment.
Date of assessment in YYYY-MM-DD format.
Quality and coverage of measurement systems, sensorization, logging, event tracking. 0–4 where 4 = complete, reliable instrumentation driving decisions.
1.0 10.0
List key sources, example dashboards or logs, or a short note about gaps (e.g., 'only 2 lines instrumented; no timestamp sync').
Reliability, repeatability, and observability of ETL/ELT, streaming, and batch pipelines.
1.0 10.0
Examples of successful or failing pipelines, SLAs, failure rate, or monitoring alerts.
Presence of data catalog, lineage, clear dataset definitions, and discoverability for analysts and users.
1.0 10.0
Link to catalog, examples of dataset documentation, or notes about missing/unclear datasets.
Defined ownership, policies, access controls, data quality rules, and stewardship practices.
1.0 10.0
Examples of owners, policies, quality dashboards, or unresolved access issues.
Use of statistical models, ML pipelines, model governance, and productionization of predictions.
1.0 10.0
List models in production, model ownership, validation practices, or gaps such as 'no model monitoring'.
How well analytics and models are integrated into operational or decision workflows (alerts, actions, automations).
1.0 10.0
Examples of alerts, decision rules, or teams that routinely use analytics outputs.
Data literacy, training, cross-functional collaboration, and leadership support for data-driven decisions.
1.0 10.0
Notes on training programs, examples of decisions using data, or barriers like 'analytics siloed in central team'.
Quality and alignment of KPIs, review cadence, and how performance measurement drives improvement.
1.0 10.0
Link or list of core KPIs, dashboards used in reviews, or missing metrics that matter.
Choose how urgent it is for this team to invest in analytics maturity.
Based on the lowest-scoring dimensions, list up to three concrete next steps the team could take. Examples: 'Fix timestamp sync across sensors', 'Establish dataset owner', 'Add pipeline monitoring and retries'.
Context, blockers, or links to supporting documents.
You can explore this tool now. Sign in or create an account to save your responses and return to them later.
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