1 Data Audit. 47 Data Quality Issues Found. 3 of Them Were Costing €200K/Year.

Data quality issues are usually invisible until they become expensive. The client in this post had a functioning business — 80+ enterprise customers, €12M ARR, a data team of four. Nobody was alarmed about data quality. It wasn't a crisis. It was a background hum that everyone had learned to work around.

The audit took 5 days. It found 47 issues across seven data systems. Three of those issues had a calculable financial cost: €142,000 in unbilled usage, €38,000 in duplicate payments made to vendors, and €21,000 in support costs driven by incorrect data surfaced to customers. €201,000 per year, hidden in systems that looked like they were working.

What the Audit Covered

The data audit was systematic, not exploratory. We mapped every data source — production database, CRM, billing platform, analytics warehouse, support system, financial reporting system, and product event stream — and ran a standardized assessment against five dimensions: completeness (are all expected records present?), consistency (do the same concepts use the same definitions across systems?), accuracy (do values reflect reality?), timeliness (how current is the data?), and lineage (can you trace where every value came from?).

47 issues found across those dimensions. Most were low to medium impact — duplicate records, stale reference data, inconsistent field naming conventions. The three high-cost issues were in accuracy and completeness.

The Full Distribution: What 47 Issues Looked Like

Issue Category	Count	Financial Impact	Fix Time
High-cost financial/billing issues	3	€201K/year	1–2 days total
Data consistency across systems	12	Operational friction	2–4 weeks
Stale reference data	9	Reporting inaccuracy	1 week
Schema documentation gaps	11	Development slowdown	Ongoing
Missing data lineage	8	Audit risk	2–3 weeks
Timeliness issues	4	Decision lag	1 week

"Every data audit we've run has found financial leakage. The magnitude varies — the smallest was €18K/year, the largest was €1.2M. The pattern is consistent: the issues are invisible because there's no system looking for them. The audit creates the system." — Gurupreet Singh, RevOps Manager, Indpro AB

The 5-Day Audit Methodology

Day 1: System inventory and access setup. Map every data system, establish read access, document the expected data flows and business rules. Day 2–3: Automated scan using data quality tooling (Great Expectations) combined with manual SQL queries for business logic validation. Day 4: Issue classification, financial impact estimation, prioritization by ROI of fix. Day 5: Report preparation, fix roadmap, stakeholder presentation.

The €200K discovery paid for many more audits than it cost. That's the consistent pattern — the audit is rarely the expensive part of data quality work. The expensive part is what you don't know before you run it.

Frequently Asked Questions

Q: How long does a data audit typically take for a mid-size SaaS company?

A focused data audit covering 5–8 data systems takes 5–10 business days for a mid-size SaaS company (50–200 employees, €5M–€50M ARR). The timeline depends primarily on the number of systems, data access complexity, and documentation quality. We start with the systems most likely to have financial impact — billing, payments, and customer-facing data — and expand from there.

Q: What's the typical financial return on a data audit?

Across our engagements, the median financial discovery in a data audit is 8–12× the cost of the audit itself. The range is wide — some audits find modest operational improvements; others find significant revenue leakage. The consistent finding is that companies with multiple data systems and no formal data quality process almost always have at least one high-cost issue that the audit surfaces.

Q: How do you prevent these issues from recurring after they're fixed?

Fixing the immediate issues is the fast part. Prevention requires data quality monitoring — automated checks that run continuously and alert when data violates expected rules. We implement Great Expectations or dbt tests as part of the fix process, so the same issues can't silently recur. The monitoring setup is typically a 1–2 week effort alongside the fix implementation.