Every portfolio company eventually faces the same question: build an analytics stack in-house, or buy an off-the-shelf platform and adapt the business to fit it. Both paths get pitched as the fast one. In a middle market context, both are usually slower and more expensive than they first appear — for very different reasons.
The Case for Buy — and Where It Breaks Down
Off-the-shelf BI and analytics platforms are attractive because the software already exists. Procurement is fast, pricing is predictable, and the vendor has a polished demo ready to go. For a business with standard, well-documented processes, that can be the right call.
The trouble is that most middle market companies are not standard. They run on a patchwork of legacy ERP systems, industry-specific software, and spreadsheets that encode years of institutional knowledge no vendor template accounts for. Off-the-shelf platforms are built for the median customer, and a company with genuinely unique operating dynamics spends months customizing a tool that was supposed to save months.
What to watch: a "quick" platform rollout with an implementation timeline stretching past 90 days is a sign the tool is being bent to fit the business, not the other way around.
The Case for Build — and Where It Breaks Down
Building in-house gives a portfolio company exactly what it needs, precisely matched to how the business actually runs, with full ownership of the resulting data architecture. For companies with genuinely unique operating models, this is often the right instinct.
The trouble is resourcing. A capable in-house build requires data engineering, analytics, and increasingly AI expertise — a skill set that is difficult to hire for even at scale, and harder still at a middle market company competing for the same specialized talent as much larger organizations. Building also means the company owns ongoing maintenance indefinitely, long after the initial project excitement fades.
Build gets you precision at the cost of speed and specialized headcount. Buy gets you speed at the cost of precision. Most portfolio companies need both — and neither path alone delivers it.
The Third Option: Embedded Build
The model that consistently outperforms pure build or pure buy is an embedded build: specialized data and AI talent deployed directly into the portfolio company to build precisely-fitted infrastructure, using proven architecture patterns instead of starting from a blank page.
This captures the precision advantage of building in-house — the system fits the business, not a vendor's median customer — while solving the resourcing problem that makes pure build so slow. The specialist arrives with the relevant expertise already developed, rather than the company spending months trying to hire it.
What This Looks Like in Practice
- Architecture reused, not reinvented: proven patterns for connecting source systems and standardizing KPI definitions, adapted to the specific business rather than built from scratch.
- Speed of buy, precision of build: a working KPI intelligence layer live in weeks, fitted to the actual operating model rather than the closest available template.
- No permanent headcount required: the specialized skill set shows up for the build and the ongoing evolution of the platform, without a full-time hire the business has to sustain indefinitely.
Making the Call
The right answer depends less on company size and more on how standard the underlying operating model actually is. A business running common processes on common systems may do fine with an off-the-shelf platform. A business with real operational complexity — multiple business units, non-standard systems, industry-specific workflows — usually needs something built to fit.
Either way, the decision should get made deliberately, with a clear view of the true cost of each path — not by default, because a vendor had the most convincing demo or because building felt like the more rigorous choice. The businesses that get this right treat their analytics infrastructure the same way they treat any other capital allocation decision: with a real analysis of cost, speed, and fit before committing.