This workflow automates data maturity evaluation to measure how well an organization uses data to create value by capturing assessment data through forms or APIs, processing and scoring responses using n8n logic and transformation nodes, and evaluating six critical data maturity domains. It generates a structured maturity report with clear, actionable feedback and visualized HTML/CSS results that stakeholders can use to prioritize improvements.
Organizations that measure data maturity can:
The assessment covers six core dimensions:
Data_Strategy_Governance
Alignment of data strategy with business goals, governance frameworks, roles, and policies.
Data_Quality_Integrity
Accuracy, completeness, validation, and reliability of data used for reporting and analytics.
Data_Driven_Decision_Intelligence
Adoption of analytics, reporting, and decision-support processes (including predictive capabilities).
Data_Management_Operations
Operational practices for storage, pipeline reliability, integration, and automation.
Data_Ethics_Privacy
Policies and controls for privacy, ethical use, consent, and regulatory compliance.
AI_Maturity_Assessment
Maturity of AI adoption: use cases, model governance, monitoring, and operationalization.
25 targeted questions designed to cover all six dimensions.
Questions are distributed so each dimension is assessed by multiple items (some sections receive 4–5 questions each), ensuring balanced coverage and statistical reliability.
Scoring and levels: Each answer contributes to a numeric score; aggregated scores produce per-dimension averages and an overall maturity level (e.g., Needs Improvement / Good / Excellent).
Per-dimension assessments with clear descriptions and colour-coded styling.
Overall maturity score and recommended next steps.
An HTML email-friendly report that merges template and assessment data for stakeholder distribution.