DEEP DIVE
What the government's impending billion-dollar data integration failure can teach PE firms about portfolio intelligence
The federal government just announced what they're calling the "AI Manhattan Project" - a massive initiative to break down data silos across agencies and build a unified platform for AI-driven insights.
It sounds ambitious. It will probably fail.
Not because the vision is wrong, but because you can't retrofit data governance onto decades of technical debt, inconsistent standards, and systems that were never designed to talk to each other. The government is trying to unify datasets that use different schemas, operate under conflicting legal frameworks, and live in legacy systems that predate the iPhone.
But here's what's interesting for PE firms: you're facing the exact same challenge across your portfolio companies, except you actually have the mandate to fix it.
The Problem Is Identical (Just Smaller)
Think about your portfolio right now:
- Company A runs NetSuite, Company B is on SAP, Company C built everything in Excel
- Revenue recognition policies differ across portcos despite being in the same sector
- Customer data lives in six different CRMs with no common taxonomy
- Your deal team needs 72 hours and three analysts to answer: "What's our actual SaaS ARR across the portfolio?"
This is the same fragmentation problem the government faces. Multiple autonomous entities, different systems, sensitive data, and a sponsor trying to create shared intelligence.
The difference? You can impose standards. They can't.
Why This Actually Matters for Exits
When you can't quickly aggregate and analyze data across portfolio companies, you lose three things that directly impact valuation:
1. Cross-portfolio pattern recognition
Individual portcos can't see pricing power trends, churn drivers, or working capital levers that only become visible across multiple businesses. You're sitting on insights that could inform better pricing, better retention strategies, better capital deployment - but only if the data can actually be compared.
2. Speed and confidence in buyer conversations
Strategic buyers will ask cross-cutting questions during diligence: How does customer acquisition cost trend across your portfolio? What's the real margin profile when you normalize accounting? How sticky is revenue across different customer segments?
If you need three days and manual spreadsheets to answer, you've just signaled risk. Buyers discount what they don't trust.
3. The "enterprise-grade data maturity" premium
Increasingly, buyers care about whether a business can be integrated post-acquisition. Fragmented, ungoverned data isn't just an IT problem - it's an integration risk that shows up as earnouts, price cuts, and lower multiples.
The PE Advantage (That Most Firms Aren't Using)
Here's where you have a massive advantage over the government's doomed Manhattan Project:
You can standardize from day one post-acquisition.
The government has to negotiate with entrenched agencies, navigate conflicting regulations, and respect decades of operational autonomy. You don't.
Within 100 days of close, you can:
- Define a core KPI model (revenue, margin, pipeline, churn) that every portco reports into
- Build centralized pipelines that ingest data in a governed, standardized way
- Create a "group analytics layer" while preserving domain-specific detail at the portco level
- Establish role-based access and clear purpose limitations so data isn't just thrown into one big bucket
This isn't about forcing Company A to rip out SAP and adopt Company B's systems. It's about creating a conforming layer that lets you see across the portfolio without flattening important nuance.
What Actually Works (From Firms That've Done This)
The pattern that works isn't "build one massive integrated platform." It's more modular:
Treat each portco as a domain responsible for its own high-quality data products - clean, documented datasets they contribute to a shared environment with common contracts and governance.
Separate your monitoring layer from your experimentation layer - regulatory reporting and board decks pull from one system of record; AI experiments and what-if scenarios happen in a sandbox with different controls.
Build a library of reusable accelerators - reference pipelines, standard KPI definitions, feature templates that can be cloned and adapted per portco rather than rebuilt from scratch every time.
This is basically a data mesh approach, and it's how you get the benefits of integration (cross-portfolio insights, shared AI capabilities, faster diligence) without the concentration risk of putting everything in one giant, ungoverned bucket.
The Thing Nobody Wants to Hear
The AI Manhattan Project will struggle because the hard part isn't the technology. The hard part is getting autonomous entities with different incentives to adopt common standards and contribute clean data.
Sound familiar?
Your Portco CEOs are running their own businesses. They have their own priorities. "Feeding data to the parent company's analytics platform" is rarely at the top of the list unless you make it part of the mandate from day one.
This is an organizational problem that requires executive sponsorship, clear governance, and accountability - not just a technology fix.
The good news? You control the incentives.