Practical guides on data diligence, exit readiness, and the data problems that erode valuation.
Operating partners managing multiple portfolio companies need a repeatable data playbook, not bespoke strategies for each acquisition. This guide covers what to standardize, what to leave alone, and how to deploy in 90 days.
Pillar GuideData governance protects EBITDA and preserves exit multiples. The four-layer framework, five controls buyers look for in diligence, and a 90-day implementation plan for PE portfolio companies.
Pillar Guide80% of PE firms deployed AI by 2025. Only 20% got operational value. The five-prerequisite data framework for portfolio companies that actually produces measurable AI returns.
When surfacing a data problem gets you scrutinized instead of supported, people stop surfacing problems. The downstream cost appears in diligence, years after it was still fixable.
The unrealized PE portfolio has hit $3.8 trillion. Average holds stretch to seven years. And every additional year of holding erodes the return. Data readiness in year one compounds through exit. Waiting until year five is too late.
PE firms that advise from a distance and claim credit selectively are running out of runway. The math demands shared accountability between sponsors and management teams.
Required EBITDA growth doubled in a decade while leverage dropped and rates rose. The math now demands operational value creation, and data infrastructure is how you deliver it.
Data integrity and leadership integrity are twin blind spots that erode PE portfolio company value long before diligence. Why they travel together, and how to diagnose and fix both in 90 days.
Data separation is the hardest technical problem in any carve-out transaction. This playbook covers the five phases from inventory to standalone infrastructure, with the common traps that derail timelines.
DPI has become the most scrutinized LP metric, creating exit urgency that flows directly to portfolio companies. Clean data operations are now the difference between transacting and waiting.
Most data-and-valuation conversations focus on pre-close diligence. But data errors continue to destroy value after the close through revenue leakage, procurement waste, and delayed value creation.
Operating partners managing multiple portfolio companies need a repeatable data playbook, not bespoke strategies for each acquisition. This guide covers what to standardize, what to leave alone, and how to deploy in 90 days.
Rollover equity has risen from 14% to nearly 17% of TEV. When sellers keep skin in the game post-close, data quality matters beyond the close date.
Data governance protects EBITDA and preserves exit multiples. The four-layer framework, five controls buyers look for in diligence, and a 90-day implementation plan for PE portfolio companies.
Five things every CFO at a PE-backed company should have locked down six months before exit. Revenue reconciliation, flash reports, retention metrics, and more.
A practical framework for operating partners to justify data quality investments to investment committees. Three value drivers, one-page template, and the cost-of-inaction math.
Most PE portfolio company AI initiatives stall not because of the technology, but because of the data underneath. Five root causes and what to do about each one.
A 90-day framework for integrating data after an add-on acquisition. Covers customer master deduplication, chart of accounts harmonization, KPI alignment, and reporting consolidation.
PE deal volume fell to a 2017 low last year. A-assets still close fast at premium multiples. Five characteristics that separate the companies that sell from the ones that stall in diligence.
Five areas to self-audit before a buyer gets into your data room. A practical framework for running reverse due diligence on your own data, with specific questions and 2-week fixes.
65% of PE firms struggle to reflect value creation in exit EBITDA. Three governance mistakes explain most of the gap, and all three are fixable.
80% of PE firms deployed AI by 2025. Only 20% got operational value. The five-prerequisite data framework for portfolio companies that actually produces measurable AI returns.
Most value creation plans have a vague line for data. Here is a concrete one-page framework that turns the data component into a real operating plan.
EBITDA multiples hit 11.8x. Financial engineering alone cannot deliver returns at these prices. Data is the operational lever that separates winners from the rest.
What to do with data in the first 100 days after acquiring a company. A phased playbook for operating partners and portfolio company leaders.
When to handle data diligence preparation yourself and when to bring in outside help. An honest comparison of cost, timeline, and outcomes.
QoE reports expose data infrastructure failures most teams never see coming. Here's why numbers break, what triggers adjustments, and how to fix it early.
Realistic timelines for fixing data before diligence. The 12-month ideal, 6-month sprint, and 3-month emergency paths with specific deliverables at each phase.
An anonymized case study showing how data issues led to QoE adjustments, buyer repricing, and a $20M valuation haircut on a mid-market PE exit.
The data readiness checklist PE-backed teams miss before an exit. Covers financial, operational, and customer data with timelines and real benchmarks.
Ten questions your team should answer in 48 hours or less. A practical readiness test for PE-backed companies approaching an exit.
Seven specific data problems that make buyers walk away or reprice. What each red flag signals, why it matters, and how to fix it before diligence starts.
What buyers actually look for in data diligence. 15 questions, what good answers look like, common red flags, and how to prepare before the clock starts.