The Silent Slop Eating Your Portfolio’s Returns


The Messy Cost of Data Slop

Hi there Reader!

I'm in the midst of a flying visit to the UK this week - it's great to reconnect with data and PE folks to discuss some exciting work on this side of the pond.

I've been delving into Elon's thesis for Grokipedia, an AI-powered alternative to Wikipedia. This will be an interesting test of AI curation skills across a HUGE dataset.

I also had the pleasure of hosting Jordan Mix of the fabulous LateCheckout Agency on the 'Transformed With Data' podcast. The word 'legend' is overused these days, but Jordan fully merits it with the amazing audience/community/product funnels he builds and supercharges enterprise value with.

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Enjoy the rest of this week's Data Signals!

DEEP DIVE

The Silent Slop Eating Your Portfolio’s Returns

The hidden cost of messy data across mid-market deals


Private equity loves efficiency. But a different kind of inefficiency is quietly spreading across portfolios.

Not operational waste. Data waste.

Forecasts that can’t be reconciled. Dashboards that tell three versions of the truth. Revenue models built on Excel gymnastics and tribal knowledge.

It’s not happening because teams are careless. It’s happening because, in the race to scale, data infrastructure has not kept pace with the ambitions for valuation.


The $8 Million Problem No One Talks About

Imagine a portfolio company preparing for a $120 million exit. The financial model appears solid, the board deck is well-organized, and everyone feels prepared for buyer meetings.

Then someone asks a simple question: “What’s the source for this CAC figure?”

Three different systems, three slightly different answers. A 3 to 5 percent variance across platforms.

That difference doesn’t sound like much, but when applied to the deal size, it translates into millions of potential valuation erosion.

The cost to fix it would be a fraction of that.

That’s the real ROI of data transformation. Clean data isn’t cosmetic. It’s a compounding asset that protects value when it matters most.


The Quantified ROI of Getting It Right

Across dozens of transactions, the numbers tell a consistent story.

  • Portfolio companies with mature data foundations move through diligence 30 to 50 percent faster.
  • When KPI logic is consistent across finance and operations, buyers discount less.
  • Even small improvements in data quality have measurable effects on margin and perceived stability.

This is the ROI most operators overlook. Clean data accelerates trust. Trust protects valuation.


How the Slop Creeps In

Data slop doesn’t arrive overnight. It builds one small workaround at a time.

It starts with “we’ll fix that later.” Then it becomes “marketing’s numbers don’t quite match finance’s.”

By the time the company reaches scale, no one knows which number to defend.

And that’s when a small reporting inconsistency turns into a full-blown diligence problem.

Buyers don’t pay premiums for confusion. They pay for clarity.


The Equation Is Shifting

The last cycle rewarded scale at any cost. The next will reward clarity at any cost.

LPs are tightening oversight. Bankers are modeling more aggressively. And the firms that can demonstrate real, defensible data maturity are closing faster and more cleanly than their peers.

The firms that ignore it are still running their portfolio reviews in Excel and wondering why IRR is slipping.


The Takeaway

To prevent data slop from impacting your returns, start measuring the ROI of clean data in the same way you measure any other investment.

  • Every verified record accelerates trust.
  • Every consistent metric protects a multiple.
  • Every week of diligence saved improves the internal return rate.

The math adds up quickly.

FINAL SEND OFF

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Thank you for reading!

Cheers,

Graeme

Graeme Crawford

CEO at Crawford McMillan

Helping PE firms protect and grow company valuations with clean, reliable data.

CRAWFORD McMILLAN
Professional Data Consultancy of 25+ years

The content provided in this newsletter, including discussions regarding data architecture, operational efficiency, valuation readiness, and business strategy, is intended strictly for informational and educational purposes only. Decisions regarding capital allocation, investments, acquisitions, or business strategy should always be made in consultation with qualified professional financial, legal, and investment advisors.

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