This is a story about a company that should have sold for $400M and sold for $375M instead. The gap was not market conditions, management quality, or competitive dynamics. It was data.
The details are anonymized but the pattern is real. I have seen variations of this play out across multiple deals. The specifics change. The math does not.
The setup
A PE-backed business services company. $50M in revenue. Growing 12% year over year. Strong retention. Good management team. The kind of company that should trade at 8x EBITDA in a healthy market.
Adjusted EBITDA of $50M on $50M revenue is generous for this illustration, so let us simplify. Assume $50M revenue, $10M adjusted EBITDA, and a target multiple of 8x. That is an $80M enterprise value. The numbers here are scaled to make the math clear, but the proportional impact is consistent with what happens at larger deal sizes.
The management team had been preparing for 18 months. Financials were audited. Legal was clean. The CIM was polished. The banker had a strong buyer list.
The data conversation never happened.
What diligence uncovered
The buyer brought in a team to validate the financials and operational metrics. Within the first two weeks, three issues surfaced.
Issue 1. Revenue by customer did not reconcile to the GL
The management team presented revenue by customer from their CRM. The GL showed a different total. The gap was 3.2%, roughly $1.6M on $50M.
The root cause was mundane. The CRM tracked bookings. The GL tracked recognized revenue. Timing differences, credit memos, and a handful of manual adjustments created a persistent gap that nobody reconciled.
The company could have explained this in an hour if they had prepared for it. Instead, it took two weeks to trace the differences, during which the buyer’s confidence in the revenue data eroded.
Impact on the deal: The buyer’s QoE team flagged $800K in revenue adjustments. More importantly, the reconciliation delay pushed the diligence timeline out by three weeks.
Issue 2. Customer retention was calculated inconsistently
The investor deck showed 92% logo retention. The diligence team recalculated it using their methodology and got 87%.
The difference was not fraud or dishonesty. The company’s calculation excluded customers below a $5K annual threshold and used a trailing 12-month window. The buyer’s calculation included all customers and used a calendar year window.
Both numbers were defensible. But the inconsistency, combined with the revenue reconciliation issue, created a narrative problem. The buyer started questioning whether the growth story was as strong as presented.
Impact on the deal: The buyer revised their growth assumptions downward. In their model, the difference between 92% and 87% retention compounded significantly over the projected hold period.
Issue 3. EBITDA adjustments were supported by spreadsheets that nobody else could follow
The company had $2.5M in EBITDA adjustments. All reasonable (owner compensation, one-time legal costs, a facility move). But the supporting documentation was a set of interconnected spreadsheets that the CFO had maintained personally for three years.
The QoE team spent four days untangling the workbook logic. They confirmed $2.1M of the adjustments and rejected $400K due to insufficient documentation.
Impact on the deal: $400K direct EBITDA reduction, plus the cost of the additional diligence time and the erosion of buyer confidence.
The financial impact
Let us do the math.
Starting position:
- Revenue: $50M
- Management-reported adjusted EBITDA: $10M
- Target multiple: 8x
- Expected enterprise value: $80M
After diligence findings:
- Revenue adjustments: -$800K (flows through to EBITDA impact)
- Rejected EBITDA adjustments: -$400K
- Revised adjusted EBITDA: ~$9.4M
But the impact does not stop at the direct adjustments. The buyer also revised the multiple.
The multiple compression:
The buyer’s investment committee reviewed the diligence findings. Three data integrity issues. Three weeks of delays. A retention rate lower than presented. Their risk assessment changed.
The original bid was based on 8x. The revised bid came in at 7.5x.
On $9.4M of adjusted EBITDA, that is $70.5M versus the expected $80M. A $9.5M gap.
At larger deal sizes, scale the math proportionally. A $500M revenue company losing half a turn on a $100M EBITDA base leaves $50M on the table.
Why it happened
None of these issues were hard to fix. Each one could have been addressed in less than a month of focused work.
The revenue reconciliation needed a monthly process to tie CRM bookings to GL revenue with documented explanations for timing differences and adjustments. Total effort: one person, two days per month, plus a one-time buildout of the reconciliation template.
The retention calculation needed a documented methodology that matched what the buyer would expect. One meeting between finance and sales to align on the definition, one updated report. Total effort: maybe 20 hours.
The EBITDA adjustment documentation needed one-page summaries for each material adjustment with clear links to source transactions. Total effort: two to three days of the CFO’s time.
Combined, perhaps 80 hours of work spread across two months. The cost of not doing that work was $9.5M in enterprise value.
The math on prevention versus cure
Here is the comparison that makes CEOs pay attention.
Cost of prevention:
- Data readiness assessment: 40 to 80 hours of internal time
- Remediation of top issues: 80 to 160 hours over 3 to 6 months
- External help if needed: $50K to $150K
- Total investment: $75K to $200K in direct and opportunity costs
Cost of not preparing:
- Direct EBITDA adjustments: $400K to $2M (depends on severity)
- Multiple compression from buyer risk perception: 0.25x to 1.0x
- Extended diligence timeline: 3 to 8 weeks of additional legal, advisory, and management costs
- Deal fatigue and management distraction
- At a $100M EBITDA company with half a turn of compression: $50M
The ROI on data readiness is not 10x. It is not 100x. It is so lopsided that the only explanation for not doing it is that nobody told the CEO it was a risk.
What they could have done differently
Six months before going to market:
Run the 48-Hour Test. Discover that the team cannot answer basic diligence questions cleanly. Prioritize the top three issues.
Four months before:
Build the revenue reconciliation process. Align the retention definition with what buyers use. Start documenting EBITDA adjustments.
Two months before:
Run a mock diligence. Have someone outside the core team request data. Identify remaining gaps. Fix what can be fixed. Prepare honest responses for what cannot.
Result:
The same three issues would have been identified. Revenue reconciliation would have been explained in the first meeting with a documented variance analysis. Retention would have matched the buyer’s methodology. EBITDA adjustments would have had clean one-page summaries.
The diligence team would have moved through the data requests in days instead of weeks. The investment committee would have seen a company with clean, well-documented numbers. The multiple would have held.
The lesson
Data does not need to be perfect. It needs to be defensible. When a buyer asks a question, the answer needs to come quickly, match across sources, and be supported by documentation that someone other than the person who built it can follow.
The companies that lose value on data are not the ones with bad data. They are the ones who did not prepare for the questions.
For a structured approach to preparation, start with 7 Data Red Flags That Kill Deals and work through The Complete Data Diligence Guide.
For a weekly brief on the intersection of data and deal value, subscribe to Inside the Data Room.