Bad data has a cost. It's not abstract. It shows up in real dollars — in campaigns that don't convert, reports that require manual correction, deals that close late because a contact's phone number was wrong, and compliance fines for data that should have been cleaned before it was used.
IBM estimated the annual cost of poor data quality in the United States alone at $3.1 trillion (IBM, 2016). Gartner puts the average organization's cost at $12.9 million per year. Industry estimates vary, but they all point in the same direction: bad data is expensive.
Here's where those costs actually live — for small and mid-size businesses specifically.
Sohovi gives you the data quality picture you need to make the case for fixing it — and to track improvement over time.
Direct Campaign Waste
Every email sent to an invalid address costs money. The send itself is cheap. The downstream costs aren't:
- Deliverability penalties that tank open rates for your entire list (not just the bad addresses)
- Paid re-targeting audiences built from CRM exports that include duplicates and invalid records
- ABM campaigns targeting the wrong company-size tier because firmographic data was never updated
For marketing ops teams specifically, data quality issues are the most common cause of underperforming campaigns — not creative, not timing.
Time Cost: The Hidden Multiplier
The most underestimated cost of bad data isn't the direct spend. It's the time your team spends working around it.
Sohovi shows you exactly what is wrong with your data — completeness gaps, type mismatches, duplicates — in one clear report.
- An analyst who spends 4 hours a week reconciling mismatched reports between tools is spending 200 hours a year on a problem that shouldn't exist
- A RevOps manager who manually cleans a pipeline export before every forecast call is spending 2 hours per week — 100 hours per year — on data janitorial work
- A marketing ops manager who has to re-segment every list before a send because the CRM fields are unreliable is adding a full day to every campaign cycle
At fully-loaded staff costs, these hours add up fast. The "free" CRM your team uses isn't free when bad data means someone spends 20% of their time cleaning instead of building.
Compliance Risk
If you handle customer data — and if you're in marketing ops, you almost certainly do — data quality failures carry compliance risk. GDPR and CCPA both include requirements around data accuracy. Sending email marketing to an address where the person has requested deletion, or using data that's materially inaccurate in a regulated context, creates liability.
Sohovi scores your dataset against your own accuracy standards and highlights the columns and rows where values fall outside expected ranges.
Most small businesses don't face GDPR enforcement. But the trend is toward stricter data accuracy requirements, not looser ones. Building a clean data practice now costs far less than a retroactive cleanup after a compliance issue.
The Fix Doesn't Require Enterprise Software
Most companies assume fixing data quality requires expensive tools, IT involvement, or a dedicated data team. It doesn't — not at the scale most small and mid-size businesses operate.
What data profiling actually shows you is a complete picture of which fields have problems, how severe they are, and where to start. Running that profile on your most important dataset takes minutes, not months.
Sohovi is built for exactly this use case: upload a CSV, get an instant quality score and breakdown — completeness, duplicates, format issues, potential PII — in your browser. No setup. No code. No IT team. Try it free at sohovi.com.
The first step isn't a year-long data governance project. It's uploading your most important spreadsheet and seeing what's actually in it.
The Compounding Effect of Ongoing Bad Data
The costs above are often treated as one-time problems. In reality, they compound. A business that doesn't address its data quality problems this quarter pays those costs again next quarter — plus the cost of the additional bad data that accumulated in the meantime.
Consider email deliverability: a company whose marketing database decays by 25% per year and doesn't run regular list hygiene loses its sender reputation slowly, then suddenly. The first year, bounce rates are slightly elevated. The second year, inbox placement drops noticeably. By year three, campaigns are reaching 40% of their intended audience — and the team doesn't know why because they've never run a quality check.
The fix, had they caught it in year one, was a 30-minute list validation. The fix in year three is a multi-month sender reputation rebuild, with real revenue impact throughout.
Data Quality Is a Competitive Moat
Businesses that invest in data quality compound an advantage over those that don't. Clean customer lists deliver better campaign ROI. Accurate pipeline data produces better forecasts and resource allocation. Reliable product data reduces returns and customer service contacts.
These aren't marginal improvements. Over 2–3 years, the difference between a business operating on trusted data and one operating on dirty data shows up in conversion rates, retention, and operational efficiency — and the gap widens every year.
If you're ready to stop guessing about your data quality, Sohovi is built for exactly this. Upload your first CSV free — no credit card, no IT team, no code needed.