Module 3 • 22 min
Data Readiness
Evaluate whether the data required for an automation is accurate, accessible, permissioned, and usable in context.Data Readiness Is More Than Data Quality
A dataset can be accurate and still be unusable if it is late, inaccessible, missing context, or governed by unclear permissions. Readiness asks whether the automation can rely on the data at the moment it needs to act.
Start with a specific workflow and trace the records, documents, fields, and decisions it depends on.
How to Apply This in Your Organization
Use data readiness to make one real implementation decision, not as a theoretical scorecard. Bring together the business owner, the people who perform the work, and the technical or data owners who understand the current constraints.
Start with the next candidate workflow, document the evidence available today, and name the missing condition that must be resolved before the organization commits to a pilot or implementation.
Five Checks
- Structure: Is the information consistently formatted?
- Accuracy: Is it reliable enough for the decision?
- Access: Can approved systems retrieve it when needed?
- Permission: Is use authorized and auditable?
- Context: Does the data include the information needed to interpret it correctly?
Worked Decision Scenario
A collections workflow needs account status, payment history, and customer communications. The data is accurate in separate systems, but one source is updated only overnight and another lacks an approved access path.
The team scopes the first use case around data that is timely and authorized, while creating a plan to address the remaining dependency. This prevents the automation from acting on incomplete context.
Apply it
Data Dependency Map
Choose one candidate workflow and list its three most important data inputs, including the current source and known constraint for each.
Saved locally for this review prototype. Your response will become private account data when the approved course backend is connected.