Completeness, consistency, timeliness and traceability: the minimum controls for reliable automation.
Quality depends on use
Data is not good in absolute terms; it is adequate or inadequate for a decision. A monthly field may support strategic reporting and fail for a daily alert. Evaluation starts with the use case and cost of error.
Define which decisions use the data, how often they occur and which delay is acceptable.
Dimensions to verify
Completeness measures required values, consistency aligns concepts across sources, timeliness measures delay, accuracy compares data with evidence, and traceability exposes origin and transformations.
Set a threshold and owner for each dimension. Without ownership, controls detect problems but do not resolve them.
Profiling and exceptions
Analyze a recent sample and a historical one. Look for missing values, duplicates, format changes and unusual spikes. Separate data errors from business exceptions: a duplicate order and a reopened order require different handling.
Keep versioned quality rules with the pipeline. When a source changes, controls should fail before unreliable data reaches the AI output.
Go or no-go
A pilot can begin when critical fields meet thresholds and insufficient cases have a fallback. There is no need to clean the entire data estate, only the selected scope.
Monitor drift, exceptions and manual corrections. Quality is a continuous operating capability, not a one-time task.
Next step
Before choosing a tool, assess the process with the Process Readiness assessment. For complex initiatives, explore the AI Process Intelligence method and ControlRoom use cases.
Frequently asked questions
Must all historical data be cleaned?
No. Start with the scope and time horizon required by the use case.
Who owns data quality?
Business owns meaning; technical owners implement controls and traceability.