Executive Summary
Private equity is facing a data crisis that most firms only discover when it costs them money.
The numbers tell the story: 75% of PE firms cannot consolidate data across their portfolio companies. 54% still collect portfolio data via email attachments. And 70-90% of M&A deals fail to meet expectations, with flawed due diligence frequently cited as the cause.
In 2026, these problems are no longer tolerable. Exit values dropped 21.2% year over year while exit volume increased. LPs are scrutinizing returns more carefully. AI initiatives are stalling because the underlying data is not ready. And buyers are conducting more rigorous diligence than ever before.
This whitepaper examines the three data problems most likely to erode portfolio value, quantifies the cost of inaction, and provides a practical framework for PE operating partners and portfolio company leadership teams to address them.
The 2026 Context
The Exit Backlog Is Forcing Harder Conversations
Global private equity exits rose 5.4% in 2025 to 3,149 deals. But the total value of those deals declined 21.2% year over year to $412.1 billion. Firms are exiting more companies at lower valuations.
This dynamic creates pressure in both directions. Sellers need to demonstrate value more convincingly. Buyers are looking harder for reasons to negotiate price down.
LP Pressure Is Increasing
Fundraising conditions remain constrained. Limited partners continue to manage denominator pressure and slower distribution cycles. Commitments are being channeled more selectively toward managers with established track records.
Performance dispersion between top-quartile and bottom-quartile managers is widening. The managers who can demonstrate clear value creation with defensible metrics will attract capital. Those who cannot will struggle.
AI Readiness Requires Data Readiness
51% of PE firms say they are seeking more data scientists and AI experts. The industry recognizes that data and analytics are essential competitive tools.
But AI cannot run on broken data. Every firm exploring AI-driven portfolio optimization, predictive analytics, or automated reporting discovers the same prerequisite: the underlying data must be clean, consolidated, and accessible.
The firms that fix their data infrastructure now will be positioned to deploy AI capabilities. Those that do not will watch competitors gain an operational advantage.
Problem One: Portfolio Visibility Gaps
The 75% Problem
According to research on PE data management, 75% of firms have highlighted the inability to consolidate siloed data systems as a major business challenge.
Portfolio companies typically run different ERP systems, different CRMs, different accounting platforms. Each system defines metrics differently. Each produces reports in different formats. Each requires manual effort to extract and normalize data.
The result: operating partners cannot get a unified view of portfolio performance without significant manual work.
The Email Attachment Economy
54% of PE portfolio company respondents use email with an attachment to collect data and respond to requests from their sponsors.
The Typical Data Flow
- An operating partner requests revenue figures
- A portfolio company CFO exports data from their system into Excel
- They email the spreadsheet
- Someone at the PE firm manually consolidates it with data from other portfolio companies
By the time analysis is complete, the data is already stale. And the manual handling introduces errors.
The Time Tax
More than 50% of respondents believe they waste considerable time and resources dealing with data because of manual processes and outdated systems.
This is not an abstract inefficiency. It is a direct cost. Finance teams spend hours on data wrangling instead of analysis. Operating partners make decisions based on information that is weeks old. Opportunities to intervene early in underperforming companies are missed.
Why This Matters for Value Creation
Value creation plans depend on accurate, timely visibility into portfolio company performance. If an operating partner cannot see that a portfolio company's gross margin is declining until the quarterly board deck arrives, they have lost weeks of potential intervention time.
The firms with real-time or near-real-time portfolio visibility can identify problems earlier, course-correct faster, and demonstrate value creation with defensible evidence.
Problem Two: Due Diligence Failures
The 70-90% Statistic
Studies indicate that 70-90% of M&A deals fail to meet expectations. Flawed due diligence is frequently cited as a contributing factor.
This is not primarily a problem of missing documents. It is a problem of data quality.
What Buyers Actually Find
When sophisticated buyers conduct due diligence, they regularly encounter:
Poor customer master data
Incomplete records. Inaccurate contact information. Inconsistent naming conventions that make it impossible to determine how many unique customers a company actually has.
Data room chaos
Mislabeled documents. Multiple versions of the same file without clear dating. Missing key information that requires follow-up requests, extending timelines and raising concerns.
Metrics that do not reconcile
Revenue figures in the CRM that do not match revenue in the ERP. Customer counts that depend on how you define "customer." Churn rates calculated differently by different teams.
GTM data that requires interpretation
Go-to-market datasets that include fields requiring specialized knowledge to understand. Sales pipeline data with inconsistent stage definitions. Marketing attribution that cannot be validated.
The Valuation Impact
A firm that pays $500 million for an asset worth only $300 million suffers a tangible $200 million loss. This is not hypothetical. Overpayment due to data-quality-driven valuation errors happens regularly.
On the sell side, the impact is equally significant. Buyers who discover data quality problems during diligence use them as leverage to negotiate price reductions. A company that could have sold for 8x EBITDA may end up selling for 6x because the buyer prices in the risk of numbers they cannot trust.
The Hidden Cost: Deal Velocity
Data problems slow deals down. Every data quality issue that requires explanation extends the diligence timeline. Extended timelines create opportunities for market conditions to change, for competing bidders to emerge, or for buyer enthusiasm to cool.
Problem Three: Exit Readiness Gaps
The Last-Minute Scramble
Most companies do not think about exit readiness until a transaction is imminent. A banker comes in, asks for data room materials, and suddenly everyone discovers that the company cannot produce defensible metrics.
By then it is too late to fix the underlying problems. The best the company can do is paper over gaps with explanations and hope buyers do not dig too deep.
What "Diligence Ready" Actually Means
A diligence-ready company can produce the following without heroic effort:
- Clean customer and revenue data. A single source of truth for who the customers are, what they pay, and how that has changed over time. Customer counts, revenue by segment, cohort retention, all reconciled and defensible.
- Operational metrics with clear definitions. If the company reports gross margin, there is documentation of exactly what is included and excluded. If the company reports customer acquisition cost, the methodology is consistent and auditable.
- Financial data that ties to source systems. Revenue in the board deck matches revenue in the ERP matches revenue in the CRM. Any differences can be explained and documented.
- Historical data with lineage. Not just current state, but the ability to show how metrics have evolved over time, with confidence that historical figures were calculated consistently.
The AI Readiness Connection
Exit readiness and AI readiness require the same foundation: clean, consolidated, well-governed data.
A company that can produce defensible metrics for a buyer can also feed reliable data into AI models. A company with fragmented, inconsistent data cannot do either.
This means the investment in data readiness pays off twice: once when AI initiatives can actually be deployed, and again when the company goes to market.
The Cost of Inaction
Direct Costs
- Valuation haircuts. Buyers who discover data problems price in risk. A 10-20% reduction on a $100M transaction is $10-20M left on the table.
- Extended deal timelines. Every week a deal is delayed costs money in advisory fees, management distraction, and market risk.
- Failed transactions. Deals that fall apart due to diligence findings waste months of effort and create reputational risk.
Indirect Costs
- Delayed AI initiatives. Firms that cannot get their data in order cannot deploy AI capabilities that competitors are using to gain operational advantage.
- Slower decision making. Operating partners working with stale, incomplete data make slower and worse decisions than those with real-time visibility.
- Team burnout. Finance teams spending 50%+ of their time on manual data wrangling are not doing higher-value analysis work.
The Compounding Effect
These costs compound. A portfolio company with poor data infrastructure will have worse operating decisions, slower AI adoption, and lower exit valuation than a comparable company with clean data.
The Data Readiness Hierarchy
Fixing data problems does not require multi-year transformation programs. It requires focus on the right priorities in the right sequence.
Goal: Operating partners can see portfolio company performance without manual data collection.
Key interventions: Standardized metric definitions, automated data collection, portfolio dashboard with consistent, timely data.
Signal of success: Operating partners no longer need to request spreadsheets to understand portfolio performance.
Goal: Data is accurate, complete, and reconciled across systems.
Key interventions: Customer master data audit, revenue reconciliation across CRM/ERP/financial reporting, documented metric definitions and methodologies.
Signal of success: Any metric can be traced from the board deck back to source systems with full documentation.
Goal: Company can populate a data room and answer buyer questions without scrambling.
Key interventions: Standardized data room templates, documentation for all key metrics, ongoing data governance processes.
Signal of success: A mock diligence request can be fulfilled within 48 hours.
Goal: Data infrastructure can support AI and advanced analytics initiatives.
Key interventions: Consolidated data repositories, data quality monitoring, documentation and governance for AI model training data.
Signal of success: AI initiatives can be deployed without data being the bottleneck.
Where to Start
Most firms do not need to solve every data problem at once. The right starting point depends on where the pain is most acute:
- If operating partners are flying blind: Start with Level 1. Get visibility first.
- If a transaction is approaching: Start with Level 3. Get diligence-ready quickly, then backfill the foundation.
- If AI initiatives are stalling: Assess where the data gaps are and work backward to the prerequisite level.
- If you are not sure: A focused diagnostic can identify the single biggest constraint and the highest-leverage intervention.
Conclusion
Private equity's data problems are not new. But the 2026 environment has made them more costly.
Exit valuations are under pressure. LP scrutiny is increasing. AI capabilities are creating competitive separation between firms that can deploy them and firms that cannot.
The firms that address their data infrastructure now will exit companies at higher valuations, make better operating decisions, and deploy AI capabilities ahead of competitors.
The firms that wait will discover their data problems at the worst possible time: when a buyer finds them during diligence.
Assess Your Data Readiness
A focused diagnostic can identify the single biggest data constraint and the highest-leverage intervention for your portfolio company.
Contact Graeme Crawford