The numbers are in. Median EBITDA multiples for PE deals closed at 11.8x through late 2025, the highest on record. GF Data reported the same pattern across their mid-market sample. Upper-quartile deals traded above 14x.
At 11.8x, you are paying eleven dollars and eighty cents for every dollar of earnings. You need that dollar to grow, and you need the growth to be real, measurable, and defensible when you sell.
Financial engineering got you here. It will not get you out. The hold-period returns from leverage, multiple arbitrage, and financial restructuring have compressed. The firms that will generate top-quartile returns over the next five years are the ones that create operational value. And every operational value lever depends on data.
The math has changed
A decade ago, a firm could acquire a company at 7x, apply moderate leverage, make a few operational improvements, and exit at 8x to 9x. The spread between entry and exit multiples, combined with debt paydown, generated strong returns without fundamentally changing the business.
That math is broken.
At 11.8x entry, a fund needs roughly 14x to 16x exit multiples to hit a 2.5x return over a five-year hold, depending on leverage and growth. You cannot rely on multiples expanding further. They might. But building a thesis around “we will sell for more than we paid” is not a strategy. It is a hope.
The alternative is creating real enterprise value during the hold period. Revenue growth. Margin expansion. Operational efficiency. Pricing optimization. Customer retention improvement. Every one of these requires data.
Not data as a technology project. Data as the operational infrastructure that tells you where value is being created, where it is leaking, and what to do about it.
Where $200B in AI investment meets portfolio reality
Global AI infrastructure spending crossed $200B in 2025. Every PE firm I speak with has an AI thesis. Many have hired data science teams, built AI roadmaps, and told their LPs about the AI-driven value creation they plan to deliver.
The problem is that 53% of PE firms now cite hiring digital transformation specialists as a top priority, according to recent industry surveys. They are hiring the people. They are investing in the tools. But they are doing it on top of data foundations that cannot support the weight.
I have watched this pattern play out repeatedly. A portfolio company gets an AI mandate from the sponsor. The company hires a data scientist or brings in a vendor. The AI initiative starts. Within 60 days, the team realizes they cannot build the model because the training data is inconsistent, incomplete, or locked in systems that do not talk to each other.
The AI project does not fail because the AI was wrong. It fails because the data underneath it was never ready.
At current multiples, firms cannot afford a twelve-month learning-the-hard-way cycle on data quality. Every quarter of the hold period matters.
The quality bifurcation
GF Data has been tracking a pattern they call quality bifurcation. Companies with strong operational metrics (clean financials, documented processes, consistent data, clear reporting) are trading at premiums. Companies without these attributes are trading at discounts.
The spread is meaningful. In GF Data’s mid-market sample, the gap between top-quartile and bottom-quartile multiples has widened. Buyers are paying more for operational discipline and punishing its absence more aggressively.
This makes sense. When entry multiples are high, the buyer’s risk tolerance is low. They need confidence that the business can deliver the growth the model assumes. Clean data gives them that confidence. Messy data does the opposite.
I have seen this on both sides. During diligence on a $150M revenue business services company, the buyer’s team asked for revenue by customer by product by month for 36 months. The management team produced it in two days, reconciled to the penny against the GL, with documented methodology. The buyer’s investment committee moved to final approval in a week.
Contrast that with a similar-sized company where the same request took three weeks and produced numbers that did not match across systems. The buyer repriced the deal by 0.7x. At $30M EBITDA, that was $21M in lost enterprise value.
Same industry. Similar financials. The difference was data readiness.
Five operational levers that require clean data
Data is not a standalone value creation initiative. It is the enabler for every other initiative in the value creation plan. Here are five specific levers that break without it.
1. Pricing optimization
Repricing is one of the fastest paths to margin expansion. But you cannot optimize pricing without knowing your unit economics at a granular level. Which customers are profitable? Which product lines carry negative margin? Where is price leakage happening through discounts, credits, and one-off deals?
A portfolio company I worked with discovered that 18% of their customer base was margin-negative after properly allocating costs. They had been subsidizing small accounts for years without knowing it. The fix was not complex. Minimum order sizes, tiered pricing, selective price increases. Within 12 months, gross margin improved by 340 basis points.
They could not have found this without clean cost allocation data at the customer level. The data existed in their systems. Nobody had connected the dots because nobody had built the reporting.
2. Procurement and vendor consolidation
Post-acquisition, one of the first operating partner moves is consolidating vendors across portfolio companies. This requires spend visibility. What are you buying? From whom? At what price? How does that compare to what your other portfolio companies pay for the same thing?
If spend data lives in AP aging reports and miscategorized GL accounts, the procurement initiative stalls. I have seen vendor consolidation programs projected to save $5M annually that delivered less than $1M because the underlying spend data was too messy to act on.
3. Customer analytics and retention
At 11.8x entry, losing customers is not a nuisance. It is an existential threat to returns. Every point of churn compounds over a five-year hold.
Understanding churn requires customer-level data. Who is leaving? When? Why? What did their engagement pattern look like before they left? Which segments have the highest retention? Which acquisition channels produce the stickiest customers?
This analysis is only possible when customer data is clean, consistent, and connected across your CRM, billing, and product systems.
4. Revenue segmentation for the equity story
When it is time to sell, your buyer will value recurring revenue differently from project revenue, new logo revenue differently from expansion, and high-margin revenue differently from low-margin. If you cannot segment revenue along these dimensions, your investment bank will build the segmentation from imperfect data, and the buyer’s diligence team will challenge it.
Building the segmentation now, during the hold period, means you control the narrative at exit. Waiting until the last six months means you are reverse-engineering a story from inconsistent data under time pressure.
5. Add-on integration
Buy-and-build strategies account for more than half of PE deal activity. Each add-on acquisition brings its own systems, data formats, customer definitions, and revenue recognition practices.
If the platform company does not have a clean data model and a documented integration playbook, each acquisition adds complexity instead of scale. I have seen platform companies with four acquisitions running four separate reporting processes, manually consolidated in Excel each month. The monthly close took 25 days. The management team spent more time producing reports than acting on them.
What this means for operating partners
The operating partner’s job has changed. It used to be primarily financial oversight with selective operational intervention. Now, at record multiples, the mandate is operational value creation from day one.
That means the operating partner needs to know, within the first 100 days of the hold, whether the portfolio company’s data can support the value creation plan. Not whether it will support it someday after a technology investment. Whether it can support it now.
Three questions to answer in the first 100 days.
Can the company produce accurate, reconciled financial and operational metrics within five business days of month end? If the answer is no, the reporting infrastructure is not ready for the operational cadence PE firms require.
Can you segment revenue by customer, product, channel, and customer type (new vs. existing) using system data, not manual analysis? If the answer is no, you cannot validate the growth assumptions in the deal model.
Do the definitions of key metrics (retention, ARR, churn, CAC, LTV) match across departments and match what a buyer will expect at exit? If the answer is no, you are building the value creation plan on definitions that will not survive diligence.
If any of these answers is no, that is not a technology problem. It is a data readiness problem that should be addressed in the first 100 days, not the last 100 days before exit.
The firms that will win
The private equity firms that outperform in a high-multiple environment will share a common trait. They will treat data as operational infrastructure, not as an IT line item.
They will assess data readiness during diligence, not after close. They will include specific data milestones in the value creation plan. They will hold portfolio company leadership accountable for data quality the same way they hold them accountable for revenue growth and margin targets.
This is not futuristic. The firms already doing this are seeing the results. Faster integration of add-ons. More accurate forecasting. Better pricing decisions. Cleaner exits.
At 11.8x, there is no room for data to be a liability. It needs to be an asset. The firms that figure this out first will compound the advantage over every deal in the portfolio.
Where to start
If you are an operating partner or portfolio company CEO reading this and wondering where your data stands, start with the diagnostic work.
For a structured approach to assessing readiness, see PE Exit Readiness: The Data Checklist Most Teams Miss. For the first 100 days after acquiring a company, the Post-Acquisition Data Playbook lays out the phased approach.
For a weekly brief on the intersection of data, deal value, and operational excellence at PE-backed companies, subscribe to Inside the Data Room. One constraint, one framework, one practical tool. Every week.