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Data Readiness

What Good Data Readiness Looks Like Before a Transformation

Data readiness is the point where an organization can explain the condition of its data, the risks that matter, the decisions still required, and the work needed to reach the target state.

Readiness is more than a data-quality score

A single score can provide useful direction, but readiness requires context. Leaders need to understand which business objects are affected, which rules are failing, which issues are material, who owns them, and what happens if they remain unresolved.

The questions leaders should ask

Are critical objects identified? Are key fields populated and valid? Are duplicates understood? Are business definitions aligned? Are transformation rules known? Can the results be validated by the business? These questions create a practical readiness picture.

Turn assessment into a plan

A readiness assessment should produce actions, not just findings. High-impact issues should have owners, priorities, target dates, remediation approaches, and validation criteria. This turns data visibility into program control.

Axiums perspective

Readiness is about confidence. A program should know where its data is strong, where it is exposed, and what must happen before the next major transformation milestone.

Key takeaways
  • Assess readiness against business-critical objects.
  • Combine quality results with ownership and remediation status.
  • Make unresolved data risks visible to program leadership.
  • Use readiness evidence to support milestone decisions.