Tools/Data Maturity Grader
Data Maturity Grader
The Data Maturity Grader is a free self-assessment that grades an organisation's data maturity across six factors: data quality, integration and access, architecture and platform, governance and lineage, analytics and BI, and AI-readiness. It returns a maturity score out of 100, a level, and the areas to fix first.
Rate your operation
Score each of the six factors from “Not started” to “Leading.”
Data quality
How accurate, complete and consistent your data is.
Integration and access
How well data is connected across systems and reachable when needed.
Architecture and platform
Whether you have a modern warehouse or lakehouse foundation.
Governance and lineage
Whether you have ownership, cataloguing, lineage and quality controls.
Analytics and BI
How well the business turns data into decisions.
AI-readiness
Whether your data can support AI and machine-learning use cases.
Frequently Asked Questions
The Data Maturity Grader is a free self-assessment that grades an organisation's data maturity across six factors: data quality, integration and access, architecture and platform, governance and lineage, analytics and BI, and AI-readiness. It returns a maturity score out of 100, a level, and the areas to fix first.
Yes. The Data Maturity Grader is a free, no-signup self-assessment. You answer six quick questions and your responses stay in your browser.
About two minutes. You rate your operation on six factors from "Not started" to "Leading" and get an instant score out of 100 with a readiness level and focus areas.
You rate each of the six factors from "Not started" (0) to "Leading" (4). The tool averages your ratings into a score out of 100, maps it to a readiness band, and highlights your weakest factors to work on first.
No. It is a self-assessment for guidance, not a formal audit. For a deeper, tailored assessment and a concrete roadmap, you can book a free call with our team.
It is for data, analytics and engineering leaders who want an honest read on their data foundation and a clear view of what to fix before it caps their analytics and AI ambitions.
Six factors: data quality, integration and access, architecture and platform, governance and lineage, analytics and BI, and AI-readiness. Weak data quality and governance are the most common blockers.