Mar 15, 2022
Data quality that stands up in audit: how to measure, fix, and prove it
You can’t govern what you can’t trust. Yet many organizations still treat data quality as a technical afterthought — until an auditor, partner, or regulator asks for proof. Under the EU AI Act and GDPR, “trustworthy AI” starts with trustworthy data. Data quality (DQ) is not a nice-to-have; it’s an auditable control. And with a structured scorecard, you can not only measure it — but show evidence that it’s improving.
Leonardo Bornhäußer
CEO & Founder
1. The four signals of data quality
Most DQ frameworks sound abstract until you break them down into measurable signals. The four that matter most — and that every auditor will recognize — are:
Completeness – Do your datasets contain all required fields for a decision or model? Missing values can create blind spots.
Timeliness – How fresh is your data relative to the business process? Even a 24-hour delay can invalidate risk models.
Consistency – Do multiple systems agree on key facts (e.g., customer IDs, transaction timestamps)?
Lineage – Can you trace data back to its origin, transformations, and version? Under both GDPR and the AI Act (Article 10), lineage is a compliance requirement, not a luxury.
A practical data-quality assessment tracks these four signals continuously and flags deviations before they become audit findings.
2. Linking DQ to decisions and KPIs
Data quality isn’t abstract — it directly affects your KPIs.
If your churn prediction model has 15% missing customer feedback data, your decision on retention budget allocation is automatically skewed.
If your revenue dashboards lag by two days, your forecast accuracy suffers — and so does investor confidence.
Mapping data assets to business KPIs helps you explain why quality matters. Reviewers will expect to see which decisions depend on which data sources and how you monitor those dependencies.
When you can connect DQ scores to operational outcomes, you turn data governance from a compliance task into a business lever.
3. What auditors actually want to see
Auditors don’t expect perfection. They expect evidence of control — proof that data quality issues are known, tracked, and improving.
At minimum, you should be able to show:
A DQ scorecard with baselines and targets.
Exception reports that capture anomalies and remediation actions.
Ownership logs — who is responsible for each dataset and how issues are escalated.
This mirrors what the European Data Protection Board and ISO 8000 standards describe as “demonstrable data governance.”
If you can’t show these three artifacts, your auditor will assume you don’t have formal controls.
4. The 30-day DQ uplift plan
You don’t need a massive project to make progress. In 30 days, you can establish visible improvements and evidence of control.
Week 1: Inventory your critical datasets and assign ownership.
Week 2: Apply the four-signal scoring (completeness, timeliness, consistency, lineage).
Week 3: Fix the top 3 issues with the biggest downstream impact.
Week 4: Produce a before/after DQ scorecard and record the results.
Auditors and partners care less about your current score and more about your trajectory — that you’re actively monitoring, fixing, and proving it.
5. From compliance to credibility
High data quality isn’t just regulatory hygiene; it’s the foundation of decision trust.
Every AI model, KPI, and executive dashboard relies on the invisible work of DQ controls that most teams underestimate — until it’s too late.
By turning your DQ framework into an auditable, visual scorecard, you make your data governance defensible and your decisions credible.
👉 Get the Data Quality Scorecard Template (PDF) Make your data quality measurable — and ready for your next audit.