Your company's data quality isn't just an operational headache; it's actively destroying your valuation multiple right now.

I just reviewed research showing that poor data quality destroys up to 15% of portfolio company revenue, while companies with clean data command exit premiums of 20% or more. That's not a rounding error. On a $100M valuation, we're talking about a $20 million difference.

Yet most CEOs still treat data quality as if it were the IT department's problem. Here's the brutal truth: PE buyers are getting sophisticated about data diligence, and your messy spreadsheets are costing you millions. Every. Single. Day.

Today, I'm going to show you exactly how data quality moves the needle on valuations and what you can do about it before your next board meeting.

• Why working capital adjustments (averaging 0.5% of enterprise value) are just the tip of the iceberg

• The hidden EBITDA adjustments that can swing valuations by $10M+ at typical multiples

• How to avoid becoming the next WeWork (hint: it's about data verification)

Let's dive in.

The $11.5 Billion Data Disaster Nobody Talks About

WeWork's valuation collapsed from $47 billion to under $3 billion. An $11.5 billion loss.

The culprit? Unverified, overly optimistic data and inadequate validation processes.

This isn't an outlier. It's a pattern. And it's happening in deals every single day on a smaller scale that doesn't make headlines.

Here's what I'm hearing from PE partners: They're now dedicating entire teams to data diligence. They're not just checking your financials, they're reverse-engineering your entire data pipeline. One partner told me last month: "If the data story doesn't hold up in week one, we're out. We don't have time for science projects."

3 Ways Data Quality Directly Impacts Your Exit Multiple

Here's what the PE buyers aren't telling you during those friendly "exploratory conversations":

1. Your EBITDA Adjustments Are Under a Microscope

Quality of earnings analyses regularly identify EBITDA adjustments of $100,000 to $1 million or more. At a typical 10x multiple, that's $1-10 million in valuation impact.

Think about that. A single questionable add-back in your EBITDA calculation (maybe that "one-time" fee that happens every so often) just costs you millions.

Another one: Companies adding back their CEO's "above-market" compensation, but they can't produce a single comparable salary benchmark. Or they're adding back "integration costs" from an acquisition two years ago. Buyers see right through this, and every questionable add-back erodes trust in ALL your numbers.

What buyers are really checking:

• Can you prove every adjustment with clean, traceable data?

• Do your customer cohorts actually support your growth story?

• Are your "one-time" expenses really one-time?

2. Working Capital True-Ups Are Pure Value Leakage

Here's a stat that should keep you up at night: Working capital adjustments occur in more than 90% of private target deals, averaging about 0.5% of enterprise value.

On a $200M deal, that's $1 million walking out the door at closing.

Why? Because your inventory counts don't match. Your AR aging is inconsistent. Your accounting policies change quarter to quarter.

This isn't sophisticated financial engineering. It's basic blocking and tackling that companies mess up every day.

3. The R&W Insurance Tell

75% of PE transactions now use representations and warranties insurance. Claim frequency? About 20% of policies.

The top two claim categories:

• Financial statement breaches

• Customer contract issues

Both are fundamentally data quality problems. And when these claims hit, they average $5.5 million.

Insurance companies have gotten smart. They're pricing your data risk into their premiums. If underwriters think your data is sketchy, that risk gets priced into the deal—either through higher insurance costs or a lower valuation.

The 20% Premium: What Good Data Looks Like

[PLACEHOLDER: Insert your perspective on what truly "clean" data looks like in practice]

PwC research shows PE funds will pay premiums exceeding 20% for companies with high-quality data infrastructure compared to financially identical peers.

Let me repeat that: Same revenue. Same EBITDA. 20% higher price.

Here's what moves the needle:

Revenue Verification

• Customer-level profitability analysis that actually reconciles

• Cohort retention data spanning 24+ months

• Clean attribution from first touch to closed-won

Operational Excellence

• Real-time KPI dashboards (not month-old Excel reports)

• Inventory and supply chain data that matches physical counts

• Predictive analytics on customer churn and pipeline

Compliance & Risk

• GDPR/CCPA compliance with documented data governance

• Audit trails on all financial calculations

• Clean data lineage from source systems to board reports

Your Data Reality Check

Here's what's actually happening when PE firms dig into your data:

Week 1-2 of Diligence: They discover your "single source of truth" is actually 47 different Excel files maintained by 6 different people.

Week 3-4: They find customer concentration issues you didn't know existed because your CRM and accounting system don't talk.

Week 5-6: The deal either reprices down 15-20%, shifts to a heavy earnout structure, or dies entirely.

The brutal truth? If you're waiting until you're "ready to explore strategic options" to fix your data, you're already 18 months too late. The right time to get your data house in order is when you hit $5-10M in revenue and beyond. The complexity is manageable, but the habits you build will scale. Not when the bankers are already calling.

The Path Forward: From Data Chaos to Premium Valuation

You don't need a $10M data transformation project. You need focused improvements in the areas buyers actually care about.

Start This Week: Run this simple test: Ask your CFO and head of sales to independently calculate your top 10 customers' lifetime value. If their numbers don't match (and they won't), you've just identified your first data quality project. Fix this one thing, and you've eliminated a major red flag that kills deals.

Next 30 Days:

1. Create a Data Room Before You Need One

   • Map your customer journey from marketing to revenue

   • Document every KPI calculation and source

   • Reconcile your three biggest revenue streams monthly

2. Fix Your Customer Data

   • Deduplicate your CRM (yes, it's that basic)

   • Match customer records between systems

   • Calculate true customer lifetime value

3. Lock Down Your EBITDA Story

   • Document every adjustment with supporting data

   • Create audit trails for all calculations

   • Test your story with an outside advisor

Next Quarter:

• Implement monthly working capital calculations

• Build cohort analyses that actually tie to revenue

• Create a KPI book that someone outside your company can understand

• 
  

The Bottom Line

Every day you operate with messy data is a day you're destroying enterprise value.

This isn't about building perfect systems. It's about proving to a buyer that your numbers are real, your growth is sustainable, and your operations are under control.

The difference between companies that command premium valuations and those that get beaten down in diligence isn't their financial performance; it's their ability to prove that performance with clean, credible data.

When you're grinding to hit your quarterly numbers, "data quality" feels like a luxury you'll get to "someday." But here's what I've learned from watching hundreds of deals: The companies that exit successfully didn't scramble to clean up their data six months before going to market. They built data discipline into their DNA when they were still figuring out product-market fit. Because data quality is something you build, transaction by transaction, customer by customer, from day one.

The market has shifted. PE buyers have options. And they're walking away from messy deals, no matter how good your growth story is. The question isn't whether you need better data; it's whether you'll fix it on your timeline or theirs.

That's it.

Here's what you learned today:

• Data quality issues routinely destroy 15-20% of enterprise value

• Working capital adjustments and EBITDA scrutiny are just the beginning

• Companies with clean data command 20%+ valuation premiums

The choice is yours: spend the next quarter cleaning up your data, or leave millions on the table when it matters most.

PS...If you're enjoying Transformed With Data, please consider referring this edition to a friend. They'll thank you for helping them avoid expensive data mistakes.

Whenever you are ready, we can help you with a ​free data valuation assessment​ to identify your highest-impact opportunities. Get in touch! Listen to the Transformed With Data podcast every week on: ​YouTube​ ​Spotify​ ​Apple​