You just closed on a new platform company. The thesis is clear: consolidate a fragmented market, drive operational efficiency, bolt on acquisitions, and create value through scale. The board wants standardized reporting across entities within the first quarter.
So you post a job for a Head of Data. Six weeks to find someone. Two weeks of onboarding. Another month to understand the business. Then they need to hire two more people, evaluate tools, negotiate contracts, set up infrastructure, build pipelines, and start modeling data.
You're looking at six months before a single dashboard exists. And that's the optimistic timeline.
The Math Doesn't Work
Mid-market companies doing $50M to $500M in revenue sit in an awkward spot. They're too complex for spreadsheets but not large enough to justify a full internal data engineering team. A competent data team — a data engineer, an analytics engineer, and someone to manage the BI layer — runs $400K to $600K annually in salary alone. That's before tools, infrastructure, and the management overhead of keeping them productive and retained.
For a PE-backed platform in year one, that's capital deployed against hiring instead of against the operating improvements the data was supposed to enable.
What the Board Actually Needs
The board doesn't need a data team. They need answers:
- How is each portfolio entity performing against the same set of KPIs?
- Where are the operational inefficiencies across locations?
- What does the consolidated P&L actually look like when you reconcile across five different accounting systems?
- Are the bolt-on acquisitions tracking to the underwriting model?
These are data problems, not staffing problems. The distinction matters because it changes the buy-vs-build calculus entirely.
The Managed Alternative
A managed data stack gets you from zero to standardized reporting in days, not months. No recruiting. No onboarding. No tool evaluation. You get:
- Every source system connected and syncing automatically
- A single cloud data warehouse with all entities normalized
- Business logic built by people who've done this across dozens of similar companies
- Dashboards that the board can actually use on day one
And critically, you get a team that doesn't quit. The number one risk with a small internal data team is concentration — when your one data person leaves, your entire reporting infrastructure becomes an unmaintained liability.
The Real Cost Comparison
When you factor in fully loaded compensation, tool licensing, time-to-value, and the opportunity cost of delayed insights, a managed data stack typically runs 40-60% less than an equivalent internal team. And it starts producing value in weeks instead of months.
For PE portfolio companies operating on a 3-5 year hold period, those months matter. Every quarter without standardized reporting is a quarter where operational improvements go unidentified and value creation stalls.
The Bottom Line
You don't need to hire a data team to be data-driven. You need your data to work. The fastest, most cost-effective way to get there is to let someone who's done it before run it for you — so your team can focus on what they were actually hired to do: operate the business.