By Shawn Johnson | Published July 2, 2026
TL;DR: Bad CRM data costs the average organization about $12.9M a year, and research ties 15 to 25% of annual revenue to poor data quality. Yet roughly 60% of companies never measure it, so it never reaches the budget conversation. For RevOps, the fix is to translate hygiene into finance language: clean pipeline data is forecast credibility. Fixing capture at the source, the moment a call ends, attacks the cost directly. That source-level fix is what we call Call-to-CRM.
Most cost lines on a P&L have an owner. Headcount, software, travel, rent. Someone signs off on each one.
Bad CRM data does not. It has no invoice, no vendor, no monthly bill. So it hides in plain sight while it quietly drains selling time, kills leads, and skews the forecast the board is trusting.
How much does bad CRM data cost?
More than most leaders think, because the number never lands on a single line.
Gartner puts the average cost of poor data quality at about $12.9M per organization, per year (source). That figure covers wasted time, rework, and decisions made on numbers that were wrong to begin with.
The revenue view is larger still. Research out of MIT Sloan ties 15 to 25% of annual revenue to poor data quality (source). For a company doing $100M, that is $15M to $25M in exposure tied to data nobody can fully trust.
The scary part is what happens to that number. Most organizations never see it at all.
What is the business impact of poor data quality?
It shows up in three places, all of them expensive.
First, wasted selling time. Reps chase dead leads, work stale contacts, and rebuild context the CRM should have held. That is hours a week spent fighting the record instead of closing.
Second, missed deals. When a rep learns something in a meeting and it never reaches the CRM, the next-step intel dies with it. The deal does not fail from bad selling. It fails because good selling went uncaptured.
Third, and most damaging at the top, decisions made on wrong numbers. When the pipeline data is soft, the forecast built on it is soft too. The board is trusting a projection assembled from records that decay and gaps that were never filled.
Here is the trap. About 60% of companies never measure the cost of poor data quality (source). A cost you never measure never gets a budget. And a problem with no budget never gets fixed.
None of this is the rep's fault. The rep gathered the intel. The system just gave them no clean way to record it before the recall window closed. Memory is terrible at 6pm in a parking lot, and typing into a CRM screen at that hour loses to the drive home every time.
How do I make the case for CRM data quality to finance?
Stop calling it hygiene. Start calling it forecast credibility.
Finance does not fund cleanup projects. Cleanup sounds like maintenance, and maintenance gets deferred. But finance does care, deeply, about a forecast the board can defend. That is the language that moves budget.
So reframe the ask. Clean pipeline data is not a tidiness goal. It is the input that makes the forecast trustworthy. When the inputs are clean, the projection holds. When the board trusts the projection, RevOps earns credibility that compounds.
Then point at the source. Most data quality spend goes to cleaning data after it is already broken, deduping, enriching, patching. That is bailing water. The cheaper move is to stop the leak, capturing the truth while the call is still fresh, before the record ever goes stale.
That is where Call-to-CRM fits. The mechanic is simple. The rep calls June right after a meeting, on the drive home, in the parking lot. June asks the qualifying questions a sharp manager would ask on a ride-along, and the clean record lands in the CRM while the memory is still accurate. Cleaner inputs, less leakage, a forecast finance can defend.
This is the opposite of the old Voice-to-CRM idea, where a rep dictates a one-way command into an app and the tool records whatever they happened to say. A guided debrief surfaces what the deal actually needs. Dictation only captures what the rep remembered to mention.
The invisible line item stays invisible for one reason: nobody measures it, so nobody funds the fix. RevOps is the function that can put a number on it and hand the CFO something they can finally see.
FAQ
What does bad data actually cost? About $12.9M per year on average, according to Gartner, plus an estimated 15 to 25% of annual revenue tied to poor data quality per MIT Sloan research.
Why don't companies fix it? Most never measure it. About 60% of companies never quantify the cost of poor data quality, and a cost with no number never gets a budget.
How do I sell hygiene to a CFO? Do not frame it as cleanup. Frame it as forecast credibility. Clean pipeline data is the input that lets finance defend the forecast to the board, and fixing capture at the source is what protects that input.