Technology & Data Maturity Models: 13 Free Assessments

Models in this chapter

Data Management & Analytics24 Evaluate how well data is governed, managed, and used for decision-making.

AI Readiness24 Assess your organisation's readiness to adopt, deploy, and govern AI responsibly.

Cloud Maturity24 Evaluate cloud strategy, adoption, operations, and optimisation.

Software Development24 Assess software development practices across planning, coding, testing, and release.

IT Service Management24 Evaluate ITSM capability across incident, change, problem, and service management.

DevOps / CI-CD24 Assess DevOps culture, automation, and delivery pipeline maturity.

Infrastructure & Architecture24 Evaluate infrastructure management, architecture governance, and modernisation.

Automation / RPA24 Assess process automation strategy, implementation, and governance.

Accessibility (WCAG)24 Evaluate how well digital accessibility is embedded in design, development, and testing.

Data Governance24 Assess how well your organisation owns, defines, protects, and manages the quality of its data.

Observability & SRE24 Assess how well your organisation understands, measures, and improves the reliability of its services.

FinOps / Cloud Cost Management24 Assess how well your organisation understands, controls, and gets value from its cloud and technology spend.

MLOps24 Assess how reliably your organisation builds, deploys, monitors, and maintains machine learning models in production.

Why assess technology and data maturity?

Technology underpins almost every capability an organisation has, but more technology isn't the same as more mature technology. Mature technology organisations make deliberate choices, run reliable services, manage data as an asset, and deliver change safely and often. Immature ones accumulate tools, workarounds and technical debt faster than value.

The models in this domain focus on practices and outcomes rather than specific products. Is data trusted and owned? Can software be changed quickly without breaking things? Are cloud costs and risks actively managed? Is the organisation actually ready to use AI responsibly, or just experimenting? Can everyone, including disabled people, use what you build?

Where to start

For data and AI, start with Data Management & Analytics, then AI Readiness. AI initiatives rarely outperform the data foundations beneath them.
For engineering teams, use Software Development and DevOps / CI-CD together.
For IT operations and platforms, look at IT Service Management, Cloud Maturity and Infrastructure & Architecture.
For inclusive digital services, Accessibility (WCAG) assesses how accessibility is built into design, development and testing.
For trusted data and production AI, go deeper with Data Governance and MLOps.
For running services well, use Observability & SRE for reliability and FinOps / Cloud Cost Management for the cost of cloud.

For security-specific technology assessments, see the Cybersecurity domain.