Data readiness is about suitability for a specific task, not collecting as much information as possible. Begin with the problem you want to solve and work backward to the information the solution needs.
1. Map the required information
List the records, documents and systems relevant to the proposed use case. Identify owners, update frequency and the permissions required to access each source.
2. Check quality and coverage
Review representative examples for missing values, duplicates, inconsistent formats and outdated information. Check whether the available data represents the situations the system will encounter.
3. Set access and handling rules
Keep access limited to the people and systems that need it. Decide how sensitive information will be handled, retained and removed, and involve the appropriate data owners.
4. Test before scaling
Use a small, representative dataset to evaluate the workflow. Document limitations and monitor data quality as the system moves from a pilot to everyday use.