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When an automated report contains incorrect data, treat it as a data-quality incident if it could affect decisions, customers, operations, or compliance. First limit reliance on the affected output, preserve evidence, and assign an owner. Then establish the error’s scope and cause, make and validate a correction, notify the people who need to act, and strengthen controls to catch similar errors.
The right response depends on what the report is used for, who has received it, and the applicable industry rules and jurisdiction. There is no single notification deadline or fix that applies to every business.
What should you do first when an automated report is wrong?
Start by determining whether anyone is still relying on the report and what could happen if they do. Do not assume that changing a figure in the report is enough: the error may be in the source data, a transformation, a business rule, or the reporting configuration. A correction made at the wrong layer can hide the symptom without fixing the cause—or create another data problem.
Contain the risk without destroying the evidence
Identify the report and version, the date range it covers, the affected fields, and the decisions or processes that use it. Establish who received it, whether it was submitted externally, and whether it is still being used. If relying on it could cause material harm, the accountable owner should decide whether to pause its use, label it as under review, or qualify the information while facts are checked. This is a risk-based operational choice, not a universal legal instruction to stop every report.
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Preserve the original report and relevant source data, configuration, processing records, and change history. Keep enough information to reconstruct what was produced and how. If you overwrite the only copy or make undocumented edits, you may lose evidence needed to understand the incident and determine which downstream decisions are affected.
Open a record and give someone responsibility
Log the issue through your organization’s incident or data-quality process, then assign an owner with authority to coordinate investigation and remediation. The person who spotted the discrepancy can contribute evidence, but responsibility should not depend on that individual alone. Data, reporting, operations, and compliance teams may each need to assess a different part of the problem.
Record the detection time; report name and version; affected fields and period; how the discrepancy was found; known recipients or decisions; actions taken to contain risk; current owner; and the next review point. NHS England Digital recommends using incident-reporting procedures for data-quality issues, with a dedicated category and defined escalation and review routes where available. That is provider guidance for its own data-quality framework, not a general legal mandate for every business. The UK Government Data Quality Framework also recommends categorizing risks by severity and reporting the most serious to senior managers.
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Trace the problem through the reporting chain rather than starting with an assumption about which system failed. An automated report is an output of multiple inputs and rules; an apparently wrong total might reflect bad input data, a changed definition, an incomplete extract, a transformation defect, or an issue in the report itself. More than one cause may be involved.
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Trace each layer of the reporting chain
- Source: Check the underlying records and the period they cover. Look for missing, duplicated, late, or misclassified data.
- Transfer and transformation: Review imports, migrations, filters, joins, calculations, mappings, and other processing steps that turn source records into report fields.
- Rules and configuration: Confirm that the report’s business definitions, thresholds, date logic, and settings are the ones intended for this use.
- Recent changes: Check for changes to data, processes, systems, configuration, or supplier inputs that coincide with the first incorrect output.
- Distribution and use: Establish which versions were generated, where they went, and which decisions or submissions may have relied on them.
Compare the output with an independent source, expected totals, or a reliable reference where one is available. Reconciliation can show whether records are missing or totals disagree; profiling can help reveal unusual patterns; an expert reasonableness review can identify results that are implausible even when the system accepts them. APRA CPG 235 describes these as data-risk practices for regulated entities in its Australian prudential context.
Decide whether this is isolated or systemic
A one-off import or migration error may affect a limited set of records. A repeated process defect, incorrect business rule, or supplier problem can affect multiple report periods or users. Check prior outputs and comparable reports to establish whether the error is recurring, when it began, and what data or populations are in scope. Avoid declaring an issue fixed merely because one rerun looks right; the cause and boundaries of the defect should be understood well enough to identify other affected outputs.
The UK Government Data Quality Framework puts the point plainly: “Only when you understand the root cause of an error can you take effective action to address the problem.” That principle matters because direct edits to low-quality data can introduce additional problems if the reason for the error is not understood.
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How should you correct the report and verify the fix?
Correct the underlying cause under the organization’s normal access and change controls. Depending on what the investigation finds, that may mean correcting source records, repairing a transformation, changing a report rule or configuration, or addressing a process that repeatedly supplies bad data. A manual adjustment to a displayed total is not a dependable substitute for a root-cause fix unless the organization has a controlled, documented process for that adjustment.
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- Document the proposed fix. Record which data, logic, configuration, or process will change, why it addresses the cause, and which outputs may need to be regenerated.
- Apply it with appropriate controls. Use the organization’s change and access controls, and preserve a record of what changed and when.
- Rerun the relevant checks. Validate the affected fields and totals against source records or a reliable reference. Use reconciliation, profiling, control totals, or an independent review as appropriate to the report’s risk.
- Review the corrected output before relying on it. Confirm that the correction addresses the error without creating implausible values, missing records, or new inconsistencies.
- Assess earlier versions. Identify past reports that contain the same defect and determine whether decisions, communications, or submissions based on them need correction.
APRA’s data-issue management guidance includes resolution and a return to business-as-usual as part of issue handling. In practice, “business as usual” should follow validation, not merely the technical completion of a code or data change.
Who needs to know about the error?
Tell relevant internal users what was affected, what changed, what remains uncertain, and whether they should replace, disregard, or qualify an earlier report. Be specific enough for recipients to identify the version or period in question and take the right action. For example, a user who has already made a decision from an earlier output needs to know that the output changed and whether the decision should be reviewed.
The UK Government Data Quality Framework says users should understand data strengths and limitations and be told about relevant problem areas and improvements. For systems involving data or AI, the UK Government Data and AI Ethics Framework recommends mechanisms that let affected people contest incorrect outcomes and seek redress, particularly where decisions are automated.
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How do you choose the right level of response?
Use the report’s purpose and the consequences of an error to set the response’s urgency and depth. The considerations below help distinguish a contained data-quality issue from one that may require broader escalation or remediation.
| Consideration | Questions to answer | Why it matters |
|---|---|---|
| Impact and criticality | What decisions or services depend on the report? How much data or how many people may be affected? | A report supporting a consequential decision warrants more careful assessment than an output with limited operational effect. |
| Scope and cause | Is the problem limited to an input, migration, or period, or does it point to a recurring system, process, or supplier issue? | The fix and the search for other affected outputs depend on whether the defect is isolated or systemic. |
| Urgency and exposure | Is the report still in use? Has it reached customers, management, or a regulator? | Ongoing reliance and external distribution affect containment, escalation, and communication decisions. |
| Remediation risk and cost | Should you correct data, amend a process or architecture, add validation, or accept and monitor a lower-priority risk? | Choose a response that addresses the cause proportionately without introducing greater risk. |
| Verification strength | Can totals, reconciliation, profiling, or an independent review demonstrate that the corrected output is fit for its purpose? | The evidence needed before reuse should match the report’s importance and the potential consequences of another error. |
Escalate based on materiality and applicable rules, using the organization’s defined routes. Regulatory guidance should be applied within its stated scope: APRA CPG 235 addresses regulated entities in Australia, while FCA Handbook MAR 9 applies to a defined category of UK data reporting services providers. Neither establishes a universal response process for every business.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What controls help prevent the same error from returning?
Choose checks that reflect the report’s risk, the way its data is produced, and the kinds of error the investigation uncovered. A single generic validation rule will not catch every problem. APRA CPG 235 identifies several types of checks that can be combined where appropriate:
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- Presence and completeness checks to find missing fields or records.
- Currency and consistency checks to test whether values are current and agree across related data.
- Control totals and record counts to detect missing, duplicated, or unexpectedly changed volumes.
- Reconciliation against source records or another reliable reference.
- Benchmarking and profiling to detect unusual values or patterns.
- Expert reasonableness review to assess whether the output makes sense for its intended use.
Define who owns each control, what it covers, and how often it runs. Assign clear responsibility for detecting, investigating, reporting, and escalating anomalies. Monitoring only helps if alerts reach someone able to assess them, so set a timely investigation route for flagged issues.
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Document the results of data-quality assessments and track them over time. Set improvement goals and repeat measurements to see whether remediation worked; a control that was added but never reviewed does not show whether error rates or recurring weaknesses have improved. The UK Government Data Quality Framework recommends recording assessments, addressing root causes, communicating limitations, and repeating measurement.
What should the incident record and follow-up include?
A concise, complete record makes it easier to coordinate the response and learn from it. Keep the incident record focused on facts and decisions rather than conclusions that have not yet been verified.
- Report name, version, date range, affected fields, and detection time.
- How the discrepancy was identified and the evidence preserved for review.
- Known recipients, decisions, services, or submissions that may be affected.
- Containment decision, its owner, and any limits placed on report use.
- Investigation findings, including known scope, root cause, and unresolved uncertainty.
- Correction made, change-control record, validation performed, and resulting report version.
- Internal or external communications and any actions recipients were asked to take.
- Preventive controls, accountable owners, review timing, and follow-up measurements.
Close the issue only when the correction has been checked, affected users have the information they need, and follow-up work has an owner. If the root cause or full scope remains uncertain, record that uncertainty and the monitoring or review needed rather than presenting the matter as conclusively resolved.
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