Data quality: definition, value and limits
Data quality Updated
Definition
Data quality describes how fit data is for its purpose: whether it is complete, correct, current, consistent and free of contradiction. In automation it is the foundation a flow works on. If the data is poor, even a clean flow produces wrong results. Data quality is thus the quiet precondition for any reliable process automation.
Why the Term Matters
Automation amplifies what the data holds, for better and for worse. A duplicate problem or a wrong field does not shrink through automation, it is reproduced faster and more often. In the mid-market projects often fail not on the flow but on the data foundation. Whoever takes the term seriously measures quality before rollout and builds checks into the flow instead of relying on assumptions.
When It Is Relevant
- A flow makes decisions based on existing data.
- Master data sits in several systems and drifts apart.
- Errors in the data would have expensive or legal consequences.
- Before a rollout it is unclear how good the data foundation is.
When Not
When a flow processes no data but only triggers a fixed action, data quality hardly matters. And where the data comes from a single, controlled source and is already maintained, no dedicated quality step is needed. Then the effort stays small.
Industries That Use It
- Retail and wholesale: keep article and price master data consistent across systems before it is used automatically.
- Sales: clean duplicates and outdated contacts in the CRM so automation does not act on wrong records.
- Finance: check posting data for completeness and consistency before a flow processes it.
- Manufacturing: unify bills of material and supplier data so ERP-adjacent flows run stably.
Typical Use Cases
- Measure data quality before a rollout and record it as a baseline.
- Detect and merge duplicates before a flow works on them.
- Check mandatory fields and formats automatically on intake.
- Reconcile master data across systems and report deviations.
- Route faulty records out of the automation path into a review list.
Decision Guide
| Signal | Address data quality actively | Alternative |
|---|---|---|
| Flow decides on a data basis | measure and check quality | a single, maintained source |
| Master data in several systems | reconciliation plus duplicate check | define one leading source |
| Errors would be costly | a check step in the flow | pure action without data use |
| Quality unclear before rollout | record a baseline first | no rollout on assumption |
Example
A wholesaler wants to process orders automatically but has article master data in two systems with deviations. Before rollout, data quality is measured as a baseline, duplicates are merged and a leading source is defined. In the running flow a step checks mandatory fields and reports deviations. So automation works on a reliable foundation. This describes the method, not an asserted client metric.
Service Connection
We check and improve the data foundation at the start of every build, whether process automation for the mid-market or an RPA build. The framing comes from the process automation consulting.
Sources
This framing describes common practice for handling data quality in automation projects. Concrete checks, metrics and data protection depend on the case, the systems and the tools and must be checked case by case.