Every PE diligence process has a moment where the technology section of the management presentation comes up. Slides 18 through 22. There is a cloud infrastructure slide, a cybersecurity posture slide, and something labeled "data and analytics." Most operating partners skim past that last one. They are looking for red flags, not opportunities. If the portco has a Snowflake instance and something resembling a BI tool, the box gets checked and the conversation moves on.
That checkbox habit is starting to cost exits.
Data maturity is now an active diligence criterion for strategic acquirers and growth equity investors. Not a secondary question buried in technical due diligence. A primary signal. The PE firms I have been watching use it as a proxy for operational credibility: a portco that can produce clean, governed, current data is a portco whose management team knows what is actually happening in the business. A portco with opaque pipelines, undocumented metrics, and four different versions of the revenue number tells a different story, and sophisticated acquirers have started to notice.
The operating partners who get ahead of this during the hold period are not just making their portcos more efficient. They are engineering a cleaner exit. The ones who discover the problem in data room prep are doing remediation under time pressure, which costs more and delivers less.
What data debt actually looks like in a diligence process
Data debt is the term I use to describe what accumulates when data infrastructure is treated as an IT overhead problem instead of a business problem. The symptoms are specific and they show up the same way across portcos regardless of sector or size.
Undocumented pipelines are the most common symptom. There is data moving from the ERP to a warehouse to several dashboards, and nobody has written down what transformations happen in between. The people who built those pipelines have usually been there for years. When one of them leaves, a piece of institutional knowledge leaves with them. Acquirers ask questions during diligence that nobody can answer without pulling that person into a call, and that kind of answer erodes confidence.
No data lineage is the second symptom. If an acquirer asks where the gross margin figure on the board deck came from, and the honest answer requires reconstructing a chain of six spreadsheet joins and a custom SQL query that only one analyst can access, that is a lineage problem. The number may be accurate. The inability to demonstrate that it is accurate, in real time, at diligence speed, is a trust problem.
Ad-hoc BI is the third. Every department has built its own dashboards in its own tool, on its own slice of data, using its own definitions. The sales team's revenue metric does not match the finance team's revenue metric. This is not simply a data problem. This is a governance problem that the data exposes. But it lands in the lap of the acquirer's diligence team and raises the question: does management have a shared understanding of performance, or is everyone operating off a different version of reality?
I have seen each of these show up in diligence processes for healthy businesses with legitimate fundamentals. The financials are clean. The customer base is solid. The management team knows the business. And yet the data room takes three times as long as it should because basic questions require bespoke analysis to answer. Some acquirers interpret that as complexity. Others interpret it as risk. Either way, it affects the conversation around valuation, and not in the direction you want.
Diligence teams are scoring data maturity now
The old framing was binary: do you have a data warehouse or not? That question is mostly obsolete. Most portcos above a certain revenue threshold have some version of a modern data stack. The question now is whether it is governed, documented, and trusted.
From what I have been seeing, a number of the more operationally sophisticated PE shops have formalized this into what amounts to a data maturity scorecard in their technical due diligence frameworks. They are not just asking whether you have Snowflake or Databricks. They are asking whether your metric definitions are codified and version-controlled. Whether data pipelines have automated quality testing. Whether different stakeholders in the business produce the same numbers from the same sources. Whether the data team is four firefighters or a functioning engineering organization with a roadmap.
The firms doing this are not doing it because data infrastructure is interesting to them. They are doing it because data maturity correlates with other things they care about: the speed and accuracy of management reporting, the ability to model what operational improvements would look like, and the confidence that what the portco says happened actually happened. A portco that cannot answer these questions cleanly is one where the acquirer has to do more work to trust the numbers. That work has a price, and it usually comes off the multiple.
For operating partners sitting on portco boards, the practical implication is this: if you wait until you are 90 days out from a process to assess the data stack, you are doing remediation, not value creation. The window where data infrastructure investment pays off on valuation is during the hold period. Not the exit period.
The platform consolidation argument
There is a specific scenario I have seen pay off repeatedly at exit: portcos that made a deliberate platform consolidation decision during the hold period and can articulate why they made it.
Most mid-market portcos accumulate data infrastructure the same way they accumulate everything else: organically, without a plan. One team chose Tableau. Another chose Power BI. Someone in engineering started using Redshift because it was already in the AWS account. Finance built models in BigQuery because that is what the consulting firm they worked with was using. By year three of the hold, you have multi-tool fragmentation that costs real money to maintain and produces organizational confusion.
Portcos that consolidate onto a single modern warehouse, Databricks or Snowflake being the most common choices for serious data workloads, and do it deliberately, are not just reducing ongoing costs. They are producing an architectural decision that survives diligence scrutiny. An acquirer's technical team can read the architecture diagram and understand what they are buying. There is a platform strategy, a governance model, and documentation of what lives where and why.
The cost profile matters here too. A portco that has done this consolidation work can typically show a declining cost curve on its data infrastructure spend relative to data volumes, because managed services handle scale more efficiently than a fragmented prior environment did. That is a legitimate operational efficiency story that shows up in EBITDA and in the diligence conversation.
A three to five year hold period is long enough to run this work properly and show the output. Portcos that start in year one or two can show a clean trajectory. Portcos that try to do it in year four are usually compressing a genuine transformation into a timeline that does not support it. The work gets done halfway, and halfway-done data infrastructure is arguably harder to diligence than infrastructure that was never touched.
The operating partners who frame platform consolidation as a value creation initiative rather than an IT project get budget for it. They get board credit when it shows up in a diligence conversation. And they go into exit prep with a data room that does not require a data engineer on standby to answer questions that should be answerable by the dashboard.
What AI-readiness adds to this argument
I want to add one dimension here because it has become impossible to separate from the rest. Every strategic acquirer and growth equity investor has an AI-driven value creation thesis for the portcos they are buying. That thesis requires that the portco can actually run AI workloads on real, trustworthy data.
A portco with data debt, ungoverned pipelines, and no consistent metric definitions is not ready for AI. It is ready for an expensive pilot that stalls before it produces anything. The 87 percent of AI proofs of concept that never made it to production, a figure from the 2025 Databricks Data + AI Summit, failed primarily on data quality grounds, not model capability grounds. The acquirer's AI hypothesis depends on a foundation that either exists or does not.
If a buyer's value creation story assumes AI-driven revenue improvement in year two and the portco cannot support AI deployment until year one is spent cleaning data, that gap is priced into the deal. The best acquirers find it in diligence. Some adjust the price. Some adjust their conviction.
The portcos that come into a process with a clean data foundation and a story about one or two AI use cases they have already proven internally are selling something meaningfully different. They are demonstrating that the acquirer's hypothesis is executable, not aspirational. That difference shows up in competitive tension and in how much the buyer is willing to pay for the story they are telling themselves about the next three years.
What operating partners should actually do
The practical ask here is not complicated, but it does require treating data infrastructure as a board-level topic rather than a CTO agenda item.
If you are on the board of a portco and cannot answer three questions, that is the starting point. First: where does the business's authoritative data live, and is it consolidated or fragmented across systems and tools? Second: who owns the definition of the key metrics in the business, and are those definitions documented somewhere other than in people's heads? Third: when did a data pipeline last break, and how long did it take to find out?
Those questions do not require a data audit. They require an honest conversation with whoever runs data at the portco. The answers tell you what the risk profile looks like and where the investment needs to go.
The portcos that have worked through those questions and have clean answers are not just more operationally efficient. They are materially easier to acquire, which affects the pool of buyers and the competitive dynamics of the process. In a market where AI-driven value creation is the dominant acquirer narrative, a portco with a governed, clean, documented data foundation is starting from a different position than one that is not.
Data maturity is not a nice-to-have. It is a valuation input. The operating partners who have internalized that are building it into their hold period plans from year one. The ones who have not tend to find out the hard way, at exactly the point in the process when finding out is most expensive.
We run a data readiness assessment with portfolio companies at the beginning of engagements. If you want to know where your portco stands before a process forces the question, that is where we start.