Many organisations have invested heavily in data quality dashboards. They measure completeness, accuracy, consistency and timeliness, consolidate the results into a score, and report the outcome to management.
A dataset might achieve a quality score of 98.7%. The dashboard is green. The trend is improving. Management receives a degree of reassurance.
But what does that score actually tell us?
Does it mean the data is suitable for its intended purpose? That regulatory reporting obligations are being met? That material exceptions have been resolved? Or simply that 98.7% of the records passed the checks somebody decided to implement?
A data quality score measures the outcome of a set of tests. It does not, by itself, demonstrate that data risk is effectively controlled.
The problem with aggregate data quality scores
Consider a financial institution preparing a regulatory return containing information about 100,000 customer accounts.
Its data quality assessment shows that 99% of the records meet the defined validation rules.
On the surface, that appears to be a strong result.
But suppose the remaining 1% contains the institution’s largest exposures. Or that a critical classification field is populated but incorrectly derived. Or that a particular product population was excluded from the dataset before the quality checks were performed.
In each scenario, the headline score could remain impressive while a material reporting risk goes undetected.
The examples are hypothetical, but they illustrate a fundamental limitation: the significance of a data quality issue depends on its business impact, not simply the number of records affected.
Measurement and control are different disciplines
Data quality measurement helps an organisation identify characteristics of its information. Effective data quality controls go further: they help prevent, detect, investigate and resolve issues that could affect business outcomes.
For example, a completeness metric might identify that a mandatory attribute is missing from 2% of records.
A control framework should establish what happens next:
- Which business process or system produced the missing data?
- Who is accountable for investigating and resolving the issue?
- Does the exception affect a regulatory obligation or management decision?
- Is the affected population material, even if the number of records is small?
- What compensating controls or escalation procedures apply?
- How is remediation verified, documented and monitored?
Without these mechanisms, the organisation may have excellent visibility of its data problems but limited ability to manage them.
Five characteristics of effective data quality controls
1. Clear ownership and accountability
Every material data quality issue needs an accountable owner with the authority to investigate its cause and coordinate remediation.
A dashboard that identifies exceptions without assigning responsibility is primarily a monitoring tool. It does not establish accountability for the underlying risk.
2. Controls aligned to business requirements
Validation rules should reflect how information is used, not merely whether it satisfies a technical format.
A date field can be correctly formatted but represent the wrong reporting period. A product code can be valid but map to an incorrect regulatory classification.
Effective validation requires business definitions, authoritative sources, reporting rules and acceptance criteria to be understood and documented.
3. Materiality and risk-based thresholds
Not all data exceptions carry the same consequences.
Controls should distinguish between minor data defects and issues that could materially alter financial, risk or regulatory reporting outcomes.
This may require assessing affected values, products, customer segments, legal entities or regulatory reporting fields, rather than relying exclusively on record counts.
4. Traceability and reconciliation
When a reported figure appears incorrect, an organisation should be able to investigate how it was produced.
That involves understanding the source data, transformations, business rules, adjustments and reconciliations applied before the information reached its destination.
Data lineage and reconciliation provide critical evidence when investigating differences between operational systems, reporting datasets and final submissions.
5. Exception management and evidence of resolution
Detecting an exception is only part of the control process.
Organisations also need mechanisms to record exceptions, assess their impact, assign remediation, approve treatment decisions and verify that corrective actions have worked.
Where an exception remains unresolved, the residual risk should be visible to the appropriate decision-makers.
What this means for regulatory reporting
Regulatory reporting provides a useful example because the final reported figures are often produced from multiple systems, business definitions and transformation processes.
An organisation might achieve high data quality scores in individual source systems while still producing incorrect reporting outcomes due to inconsistent mapping rules, missing populations or inappropriate aggregation logic.
This is why controls need to operate across the reporting lifecycle, from source data through transformation, reconciliation, exception management and final reporting review.
APRA’s CPG 235 — Managing Data Risk discusses dimensions of data quality including accuracy, completeness, consistency, timeliness and fitness for use. It also addresses the importance of validation, monitoring and controls around data publication.
Similarly, the Basel Committee’s BCBS 239 principles emphasise governance, accuracy, completeness and reliable risk data aggregation and reporting.
These frameworks reinforce the broader principle that reliable reporting requires more than measuring the apparent quality of individual datasets.
A better conversation for management
Instead of asking only whether the data quality score has improved, management should ask:
- Which critical business and regulatory outcomes depend on this data?
- Are the validation rules sufficiently comprehensive to identify material risks?
- Which exceptions remain unresolved, and what is their potential impact?
- Can we trace material reported values to their authoritative sources?
- Who owns the controls, and what evidence demonstrates that they operate effectively?
These questions shift the discussion from a single performance indicator towards the effectiveness of the underlying control environment.
From quality reporting to controlled outcomes
Data quality dashboards have an important role. They help identify trends, prioritise investigation and provide visibility across complex data environments.
But their value depends on the strength of the processes supporting them.
A useful data quality programme should connect measurement with accountability, business impact, traceability, remediation and assurance.
A high data quality score should provide confidence in the effectiveness of the controls behind it — not substitute for those controls.
The objective is not to produce the greenest dashboard. It is to establish whether the organisation can rely on its information when making decisions, managing risk and fulfilling its reporting obligations.
Strengthening data governance and regulatory reporting
Acrogen works with organisations to connect regulatory requirements, data quality, reporting controls and practical technology delivery.