DEEP DIVE
The 0.4x Multiple Tax You’re Paying for Bad Data
Most companies think they have clean data.
They have a modern ERP. Regular financial audits. Dashboard upon dashboards. The board loved the quarterly updates.
Then they hired a banker to prep for exit. Two weeks into diligence prep, the banker asked a simple question: “Can you show me cohort-level retention by customer segment?”
The answer came back five days later. With caveats. And footnotes explaining why certain segments weren’t comparable.
That hesitation cost them 0.4x on their multiple. On a $50M business, that’s $2M left on the table.
The math is simple. The execution is not.
GF Data analyzed 360 transactions and found something most operators miss: sellers who conducted sell-side Quality of Earnings reports alongside data quality assessments commanded 7.4x EBITDA multiples versus 7.0x for those who didn’t.
That 0.4x difference isn’t about having better numbers. It’s about buyer confidence in the numbers you show them.
Because here’s what happens during diligence when your data doesn’t hold up:
Scenario 1: The Revenue Recognition Question
Buyer asks: “Walk me through how you recognize revenue for multi-year contracts.”
Good answer: “Here’s the policy. Here’s the source system. Here’s how it flows to financials. Here are three customer examples showing it in practice.”
Red flag answer: “Let me check with our accounting team and get back to you.”
The good answer takes 20 minutes. The red flag answer triggers three more questions and a special diligence workstream.
Scenario 2: The Customer Cohort Request
Buyer asks: “Show me year-over-year retention by customer segment and acquisition channel.”
Good answer: Segmentation exists. Data is clean. Answer arrives in 48 hours with confidence intervals noted.
Red flag answer: “We don’t track it that way, but we can pull something together.”
Translation: Your data model wasn’t built for this question. The buyer now wonders what other questions you can’t answer.
Scenario 3: The Source System Audit
Buyer asks: “How many systems feed your revenue reporting, and can you show me the reconciliation logic?”
Good answer: “Three core systems. Here’s the data lineage documentation. Here’s the monthly reconciliation process. Last discrepancy was $4,200 in Q2, documented and resolved.”
Red flag answer: “Our finance team handles the reconciliation manually each month. It usually balances.”
That word “usually” just cost you negotiating leverage.
Why this keeps happening
Most mid-market companies build their data infrastructure to run the business, not to sell the business.
That’s rational. Until it’s not.
You optimize for operational reporting - dashboards that tell you if you’re hitting plan, if payroll cleared, if inventory is moving. Those systems work fine for Monday morning leadership meetings.
But exit diligence asks different questions. They want cohort analysis. Customer lifetime value by segment. Revenue bridge detail at SKU level. Margin decomposition that reconciles to GAAP financials.
If you can’t produce those answers cleanly and quickly, buyers do one of three things:
- Discount the purchase price to account for integration risk
- Structure earnouts to shift risk back to you
- Walk away entirely if the data raises too many flags
All three options cost you money.
What good looks like
I’m not talking about perfect data. That doesn’t exist.
I’m talking about defensible data. Data you can stand behind during questioning. Data that comes with lineage, documentation, and confidence.
Here’s the practical test: If your banker asks your finance team a detailed analytical question, how long does it take to get an answer you’d bet your valuation on?
If the answer is 48 hours or less, you’re probably ready.
If the answer is “let me check with IT” or “we’ll need to pull that manually,” you’re not.
The work that matters
Three months before you talk to a banker, run your own diligence:
1. Audit your source systems Map every system that feeds financial reporting. Document the connections. Know where the gaps are before a buyer finds them.
2. Test the cohort questions Ask your team to produce the analysis buyers will request: customer retention, revenue bridges, margin decomposition, cohort-level economics. If they struggle, fix it now.
3. Document your data lineage Show how data flows from operational systems to financial reporting. Include reconciliation logic. Note any manual steps. This becomes your answer to “how do you know these numbers are right?”
This work isn’t glamorous. It doesn’t show up in EBITDA. Your leadership team won’t applaud you for it.
But it protects your multiple. And that’s worth more than most operational improvements you could make in the same timeframe.
The question that matters
Six months from now, if a buyer asks your CFO to explain a variance in your customer retention data, will they have the answer in 48 hours?
If not, you’re paying the 0.4x tax. You just don’t know it yet.