Data Quality Management: Rules, Owners and Issue Workflows

A rule catalogue, repeatable measurements and a workflow for issues, corrections and retests. Scope is bounded to selected data products and accepted with business and operating teams.

An audit assesses an agreed dataset at a documented point in time. Data quality management establishes recurring rules, responsibilities and improvement workflows; an audit can provide initial findings.

We prioritise data by its use, known errors and potential impact. Starting with a bounded data product enables testable results without representing the whole estate as assessed.

Business owners describe valid values and relationships using concrete cases. These become checks with a measurement population, exceptions and expected results.

Depending on use, completeness, timeliness or agreement between sources may matter. Each selected characteristic receives a concrete measurement rule; an overall score must not obscure significant individual problems.

Plausible formats alone are insufficient. We identify suitable references or business-reviewed cases and label values whose accuracy cannot be confirmed with the available evidence.

Business owners are accountable for meaning and accepted limitations. Technical teams manage implementation, with approval, cover and escalation explicitly documented.

Checks produce results with data timestamp, rule version and measurement scope. Notifications receive an owner and handling rule so a dashboard does not become an unattended issue archive.

We document which deviation affects which use and what response is required. Thresholds are tested in the pilot and approved by business owners rather than imposed uniformly across datasets.

A correction needs a justified rule, approval and traceable change. We test it on agreed cases and then check affected uses; automated bulk changes are not the default without those prerequisites.

We trace findings to capture, delivery or processing and distinguish confirmed causes from hypotheses. Actions receive owners; a cosmetic correction to an output value does not automatically complete the investigation.

Business identity and suitable comparison attributes are clarified first. Potential duplicates are assessed for incorrect-match risk; similarity alone does not justify automatic merging.

For selected interfaces, we define whether anomalous data is held, labelled or forwarded. Recovery, exceptions and ownership form part of workflow acceptance.

We identify the documents or content involved and suitable criteria for their intended use. Automated extraction and classification are evaluated against reviewed examples, with uncertainty remaining visible in the result.

Assessment follows the intended application and required attributes. Data errors, time boundaries and potential biases are documented; good input data alone does not establish reliable analysis or model output.

AI can provide indications or proposals for an agreed case, but their quality requires testing. Correction and approval decisions remain governed; a model proposal does not automatically become a confirmed fact.

Applicable requirements are mapped to the specific data process with accountable specialists. We document agreed controls and evidence; the service is not offered as blanket confirmation of complete regulatory conformity.

We test required rules, interfaces, permissions and workloads in the intended environment. Measurements retain their conditions; buying a tool or completing a small pilot does not guarantee unlimited scaling.

Selected incidents, rework and handling times establish the baseline. Changes and project effort are compared transparently; general loss or return rates are not evidence for your organisation.

Owners practise reporting, correction, retesting and rule changes. They receive documentation and open actions with responsibilities; a single error-free measurement does not replace this handover.