Why the Cost Is Always Underestimated
The IBM estimate that poor data quality costs the US economy $3.1 trillion annually gets cited regularly — and usually dismissed as too large to relate to. But the cost of poor data quality in any specific organization is very real and very calculable.
The underestimation happens because the costs are distributed: a few hours here, a wrong decision there, some wasted marketing spend, some staff time on correction and verification. No single event is catastrophic. The aggregate is significant.
The Direct Cost Categories
Rework time: How many hours per week do your employees spend finding, correcting, and re-entering data? Multiply by loaded labor cost. For a business with 20 employees each spending 30 minutes per day on data-related rework, that's 50 hours per week × $50/hour = $2,500 per week = $130,000 per year.
Sohovi gives you the data quality picture you need to make the case for fixing it — and to track improvement over time.
Failed deliveries and communications: What's your hard bounce rate on email campaigns? What's your returned mail rate on direct mail? Each failed communication has a measurable cost in materials, postage, and lost opportunity.
Duplicate work: Duplicate customer records mean duplicate outreach. Calculate: (duplicate rate × outreach volume × cost per outreach). A 15% duplicate rate on a 100,000-record email list means 15,000 duplicate sends per campaign at whatever your per-send cost is.
Technology costs: Many organizations buy larger database licenses, more storage, or additional data processing capacity for data they don't actually need — including duplicates, stale records, and invalid entries. Cleaning data reduces infrastructure costs.
Sohovi automatically finds every duplicate in your dataset — including near-matches — and shows you exactly which rows are affected.
The Indirect Cost Categories (Larger)
Bad decisions: Harder to quantify but potentially most significant. A pricing decision based on inaccurate competitive intelligence. A market expansion decision based on duplicated customer counts that overstate demand. A staff hire based on workload data that was measured incorrectly.
Lost revenue: Customers who should have received a follow-up but didn't (wrong email). Proposals sent to the wrong person (wrong contact). Deals lost because the CRM showed a deal as closed that was actually still open.
Compliance risk: Incorrect personal data, GDPR violations from wrong records, financial restatements from accounting errors. Regulatory and legal costs have the highest magnitude of any category.
Sohovi automatically detects PII in your datasets — emails, phone numbers, SSNs — all processed client-side so your data never leaves the browser.
The Calculation Framework
For each identified data quality problem:
- What business process is affected?
- What is the frequency of impact (per day, per transaction, per campaign)?
- What is the cost per incident (labor, direct cost, opportunity cost)?
- Annual cost = frequency × cost per incident × 52 weeks (or appropriate period)
Sum across problems. You almost always find the number is large enough to justify the investment in data quality improvement.
