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WWT Research • Maturity Model
• 22 minute read

Data Maturity Model

Maps five levels of data maturity, from fragmented and ungoverned to an autonomous enterprise where AI agents operate as accountable participants, and shows what's safe to attempt with AI at each stage.

 Unlocking AI Success with WWT's Data Maturity Model

Stored data is overhead. Activated data, governed and trusted, is the foundation everything else gets built on.

Most organizations know their data isn't fully ready for AI. Few can say exactly where the gap sits or what to fix first. WWT's Data Maturity Model gives data and AI leaders a five-level framework for answering that question, along with a level-by-level view of which AI and agentic use cases are safe to pursue at each stage.

 What is a data maturity model?

A data maturity model measures how governed, trusted, discoverable and activation-ready an organization's data actually is, not just how much of it exists. WWT's version separates two related questions: how developed an organization's data capabilities are today, and what AI use cases that level of readiness can safely support. The strongest sequence runs from data maturity to AI readiness to business value. Skipping straight to the last one is usually where the gap gets found the hard way.

 The five levels

The full report walks through each level in depth: characteristics, AI readiness implications, example use cases and the specific steps to reach the next stage.

  1. Fragmented: Data sits siloed across systems, with unclear ownership and undocumented governance.
  2. Connected: Key sources are integrated and early governance practices exist, but trust still gets negotiated case by case.
  3. Governed: A formal data operating model is in place. Data shifts from accumulated overhead to a managed enterprise asset.
  4. Activation-ready: Data products are published and certified. Narrowly scoped data agents can be introduced safely.
  5. Autonomous enterprise: Data readiness becomes a continuously improving capability, and agents act as accountable participants rather than external automation layers.

 What you'll get access to

  • A level-by-level breakdown of characteristics, AI readiness implications and example use cases for all five stages
  • Governance guidance for agent identity, scoped credentials, audit logging and the emerging derived-data risk as agents gain more authority
  • A Center of Excellence charter template covering mandate, membership and operating cadence
  • A KPI framework for proving the business case at each maturity level, plus guidance on cost avoidance and compounding return
  • A crosswalk showing how this model aligns with WWT's AI Maturity Model and Agentic AI Maturity Model
Create a WWT account to read the full framework and find out exactly where your organization stands.

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