Start with the intended answer
Describe the answer or action the integration must support. Then identify the records needed to produce it. This prevents a common mistake: gathering large amounts of information without deciding what makes a source useful.
For a customer-service search, useful information may include approved guidance and product records. Internal commentary, duplicated drafts and obsolete attachments may create confusion unless their status is clear.
Check ownership and permission
Every important source needs an owner who can explain its purpose, access rules and update process. Ownership does not mean that one person edits every record. It means someone can answer questions about accuracy, retention and appropriate use.
Map the users and systems that need access. Apply the least access needed for the task. A model or integration should not receive a whole repository merely because a smaller collection has not been prepared.
Look for quality issues
Common problems include missing fields, inconsistent names, duplicate records, conflicting versions and unclear dates. These issues may be manageable for a person who knows the context but difficult for an automated system.
Do not hide uncertainty. Mark records that need review, define which source takes precedence and make the age or status of information visible where that matters. A clear limitation is easier to govern than an apparently complete but unreliable answer.
Prepare unstructured material
Documents, email and notes may contain valuable information, but they do not arrive in a consistent format. Before using them, consider whether text can be extracted, whether tables retain their relationships and whether attachments contain personal or confidential material.
Search and retrieval also depend on useful labels. Titles, dates, departments and document status can help a system return the right material. A generic folder name provides little context.
Test retrieval, not just generation
If an AI system uses business information, assess whether it can locate the right source and distinguish it from nearby but incorrect material. Review examples that are straightforward, incomplete and ambiguous. Check whether the result points a user to supporting evidence.
Keep test material representative of the actual workflow without exposing unnecessary personal information. Test access boundaries as well as answer quality: users should not receive records simply because a search can find them.
Keep the information moving responsibly
Record where information enters the integration, where it is transformed and where outputs are stored. Decide how corrections travel through the system. A corrected source should not leave an old answer permanently circulating without a way to identify it.
Data readiness is an ongoing operational responsibility. Pair this assessment with the governance guide before selecting a supplier or enabling access.