Most PE firms do not set out to build software. They set out to solve a specific problem. A CFO wants 13-week cash flow visibility across the portfolio without chasing individual company finance teams. A deal team needs to track covenant compliance without waiting for spreadsheets that arrive late and formatted differently each time. An operating partner is six months into a roll-up and needs to normalize charts of accounts across eight acquisitions before the next board pack goes out.
The instinct that follows is reasonable: we know exactly what we need, we have capable people, and we understand our portfolio better than any vendor does. Why pay for something that does not quite fit when we could build something that does?
Three years later, the same firm is maintaining a collection of Python scripts, a custom Airtable build, and a reporting workflow that lives in one analyst's head. The analyst has moved on. The quarterly LP report takes three weeks instead of three days. A fundraise is underway.
This article is not an argument against building things. There are situations where an internal build is the right call. But for portfolio data infrastructure in private equity specifically, the buy-versus-build decision has a clear answer that most firms eventually reach, often after absorbing costs that were avoidable. The question is whether you get there before or after the damage is done.
Why Building Feels So Attractive
The appeal of building is not irrational. PE firms operate with real specificity. Your data model reflects your investment thesis. Your reporting cadence reflects LP commitments. Your operating model reflects how your portfolio companies are actually run, which is rarely how generic software assumes they are run.
There is also a legitimate control argument. When you own the system, you own the roadmap. You can add a field, restructure a view, or change a formula without submitting a support ticket and waiting. For firms with technical capacity in-house, this is genuinely attractive.
Modern tooling has also made the initial build feel cheaper than it used to. A capable analyst with access to good infrastructure can put together something that looks functional in a few weeks. The prototype works. The demo is convincing. The project gets greenlit.
None of this is wrong. The problems come later.
The firms that get into the most serious trouble are usually those where the initial build worked well enough to create dependency before the structural problems became visible.
Where Internal Builds Start to Break
A prototype that consolidates data from five portfolio companies is not the same thing as a production system that handles thirty-five, across three fund vintages, with a mix of buyout and growth equity assets.
The schema mismatch problem alone grows in a way that is hard to anticipate. One portfolio company tracks revenue by product line. Another tracks it by geography. A third runs a non-calendar fiscal year. The roll-up you acquired in Q3 brought six entities with their own chart-of-accounts conventions, two of which conflict with each other.
Every exception needs a fix. Every fix needs testing. Every test needs someone's time, and that time compounds.
The system is embedded. People rely on it. Replacing it carries its own switching cost. So they patch. They extend. They hire someone to maintain a thing that was never designed to be maintained by anyone other than the person who built it.
One mid-market growth equity firm spent roughly two years building a bespoke reporting layer to handle LP reporting, board pack production, and KPI tracking across a portfolio of around thirty companies. The system worked adequately while the portfolio was smaller and the core team that built it was intact.
When two of the key people left within six months of each other, the firm discovered that the critical calculation logic was spread across undocumented scripts with no single person who could explain all of it confidently. Producing the quarterly LP report went from a three-day process to a three-week one. This happened during an active fundraise. The timing created pressure that was entirely avoidable.
Structural Pattern
This is not an unusual story. Versions of it repeat across the market with enough consistency that it is worth treating as a structural pattern rather than bad luck.
Why Portfolio Data Infrastructure Is Harder Than It Looks
Generic build-versus-buy arguments do not fully capture the complexity specific to portfolio data in a PE context.
Chart-of-accounts normalisation
Chart-of-accounts normalisation across a portfolio. Running a buy-and-build across a sector means your portfolio companies will not arrive with comparable financials. Getting EBITDA to mean the same thing across twelve companies in a board pack requires either a robust rules engine or a lot of manual reconciliation. Building that normalisation layer properly is a substantial data engineering problem. It is not a spreadsheet task.
13-week cash flow visibility
13-week cash flow collection and visibility. Lenders want it. Operating partners track it. Doing it well requires a consistent format, timely submission from portfolio company CFOs, and a validation layer that catches errors before they reach the investment committee.
Building a collection workflow that portfolio company finance teams will actually use, rather than ignore or work around, is as much a product design problem as a technical one.
Board packs and LP reporting
Board pack and LP reporting workflows. These are not just data outputs. They involve version control, prior-period comparisons, sign-off processes, and narrative text sitting alongside numbers. A bespoke system that generates board packs also needs to handle corrections at 11pm before a meeting, formatting that survives the journey from database to PDF, and enough auditability that you can explain any figure to an LP who asks.
Building this robustly is a different order of complexity from building a dashboard.
Covenants, DDQs, and operating partner workflows
Covenant tracking with an audit trail. Credit agreements have specific, often negotiated definitions. Covenant calculations frequently depend on adjusted EBITDA figures that themselves depend on management judgement calls. Tracking breaches, near-misses, and cures requires both data integrity and a paper trail. A bespoke solution that gets this wrong is not a nuisance. It is a liability.
DDQ and LP due diligence data. Investors increasingly expect firms to respond quickly to detailed operational and ESG data requests across the portfolio. This requires structured, queryable data, not a folder of Excel files that someone has to manually consolidate under deadline pressure. Building a DDQ-ready data layer is a materially different problem from building a reporting dashboard.
Operating partner workflows across the portfolio. An operating partner sitting across six companies needs visibility into specific KPIs without chasing individual teams every week. They also need to push workstreams, track progress against initiatives, and flag issues before they reach the board. This is a collaboration and workflow problem layered on top of the reporting problem, and it adds another dimension to anything built internally.
The Costs Most Firms Underestimate
The initial build cost is almost always the smallest part of the total. Most firms budget for development time and some external help. They do not fully account for ongoing maintenance as the portfolio changes, for the time spent debugging data that the system cannot explain, for the analyst hours spent on data plumbing instead of analysis, or for the cost of explaining a broken report to an LP at 7am.
The staff dependency risk is also systematically underestimated. Internal systems frequently become legible only to the people who built them. When those people leave, their replacement faces a choice between learning an undocumented system and rebuilding it. Both options cost money. Neither option is free.
Over a four-to-five year horizon, the total cost of ownership for a substantial internal build typically exceeds what a commercial platform would have cost, once you account for people, maintenance cycles, and the one-off crises that internal systems reliably produce at moments of maximum pressure.
Internal build risk
- Maintenance burden rises as the portfolio changes
- Critical logic often sits with a small number of people
- Pressure peaks exactly when reporting matters most
Commercial platform advantage
- Finite procurement and implementation work
- Better durability through team changes and portfolio growth
- Lower risk of reporting systems breaking at the worst moment
The Risks of Buying Are Real Too
Buying a software platform carries its own risks, and they deserve honest treatment rather than a dismissive footnote. Vendor lock-in is real. If a vendor stores data in a proprietary format, prices exit fees punitively, or controls integrations in ways that make migration painful, the convenience of buying creates a long-term dependency.
Integration complexity is often underestimated on the buy side too. A platform that does not connect cleanly to your fund administration system, your CRM, or the accounting tools your portfolio companies actually use is not a solution. It is a better-designed silo.
Roadmap compromises are a genuine frustration. When a vendor's development priorities diverge from yours, you wait. For firms with specific requirements, this can mean the product never quite fits the way you need it to.
And some platforms are simply not suited to particular firm types. A platform designed around large-cap buyout reporting may be a poor fit for a growth equity firm tracking SaaS cohort metrics. Buying the wrong tool and then trying to adapt it creates its own category of wasted time and internal frustration.
These risks are real. They are also manageable, in a way that internal-build risk often is not.
What Durable Platforms Tend to Have in Common
Vendor risk can be addressed through procurement diligence: checking data portability terms before signing, asking for references from firms with a similar portfolio mix, stress-testing integration claims with your fund administration provider, and being honest about where a platform's defaults diverge from your actual requirements.
Good platforms also tend to share a few characteristics. They were designed for firms that need private equity portfolio monitoring, not generic reporting. They can handle fund structures, portfolio company hierarchies, management accounts, and KPI tracking without heavy customisation before becoming useful. They make data portability possible. They integrate with the systems portfolio companies already use. And they are maintained by people who understand how PE firms actually work when things get messy.
One is a procurement problem. The other is an infrastructure problem. Procurement problems are frustrating. Infrastructure problems become existential when your data stack sits underneath board reporting, LP communications, lender discussions, and value creation tracking all at once.
A bespoke system that outlives the people who built it becomes painful in an embedded way that gets harder to fix the longer it runs.
When Building Actually Makes Sense
There are cases where building makes sense: a small and stable portfolio, genuine requirements no available platform meets, strong internal technical capacity, and a team that will stay long enough to maintain what they build. That situation exists. It is not the common one.
For most PE firms, the portfolio monitoring and reporting problem is more standard than it feels from the inside. The requirements that seem unique are often shared by other firms. The ongoing maintenance burden is underestimated in ways that are consistent enough to be predictable. And the consequences of a broken or degraded system fall hardest at exactly the moments of highest pressure: LP reporting cycles, credit reviews, exits, and fundraises.
Building is a satisfying answer to the question of what to do right now. Buying a governed, PE-specific platform is the more honest answer to the question of what works across a fund cycle, through team changes, portfolio growth, and the kinds of complexity that prototypes were never designed to handle.
See What a PE-Specific Platform Looks Like in Practice
Watch how Planr helps firms move from fragile reporting workflows to governed portfolio intelligence.
