A private equity firm managing a significant portfolio of companies had outgrown its internal data capacity. Like any PE firm, it depends on timely, accurate data to drive investment decisions, portfolio reporting, and operational visibility across its holdings. When data fails here, the consequences aren’t abstract — they surface in the boardroom.
The pain, cemented
The firm tried to bridge the gap with more people — first a low-cost outside team, later a larger, more expensive vendor. Neither fixed anything. Costs climbed, from roughly $50K a month to nearly $2M a year at the high point, but the core problems didn’t move: work billed as “automated” wasn’t, outputs were frequently wrong, and the firm’s own data leader spent 20 to 30 hours a week just managing and re-verifying what should have run itself. More hands and a bigger price tag treated the symptom, not the cause.
The breaking point
The clearest expression of the cost showed up in the firm’s single most demanding recurring deliverable: a 150-page, twice-yearly review of every portfolio company, covering financials, executive evaluations, and operational metrics, presented over a multi-day session in front of the CEO. It was rebuilt from raw exports and spreadsheets every cycle, and getting it out the door routinely took a dozen reprints and a run of all-nighters. Then, days before one presentation, the “automation” an outside team had built turned out not to exist. Errors ran throughout the report. One employee spent 48 straight hours manually fixing it before it could go to the CEO.
What actually fixed it
Rather than adding more people, the rebuild focused on the data itself: replacing convoluted, over-engineered pipelines with a single governed hub, built on the firm’s existing modern cloud data stack — one place where every entity’s identity and its relationships to every other entity were defined once, validated at the point of entry, and never re-derived downstream. When something was wrong, it got caught at the source — not discovered in the boardroom.
The proof
- Annual cost: roughly $500K lower than the prior vendor engagement, with a structurally more capable team.
- Flagship report: no more reprint cycles or all-nighters. The report now builds from governed data instead of being reassembled by hand.
- The most recent cycle of that report went to the CEO with zero change requests — the first time in the firm’s history.
- A separate suite of ~20 recurring goal-setting and performance workflows, previously manual and consuming the better part of a week per person per cycle, is now fully automated with no manual touch at all.
- Internal modeling estimated the freed-up capacity from these workflows could translate to a 30% increase in deal capacity per quarter, professionals redirected from data assembly to the work they were actually hired to do.
Why it matters
The dollar and hour figures are evidence the rebuilt foundation holds under real pressure — they aren’t the point of it. The real deliverable was a data function the firm could finally trust without re-checking, and that trust is what makes everything else possible: self-serve exploration, fast answers, and AI tools that inherit the same governed foundation instead of introducing new risk on top of it.
Details in this case study are anonymized at the client’s request.
