Writing
Research notes on data methodology, sector mapping, and deal sourcing practice. Written by the Thema team for PE analysts and corporate development professionals.
When a business accelerates hiring in sales, finance, and HR functions simultaneously, it is often preparing to transact. We look at the mechanics of that signal and why it surfaces months before any formal process.
Most deal sourcing tools answer 'show me companies matching these filters.' Sector mapping answers a different question: 'what does the competitive structure of this space actually look like?' We explain why that distinction drives very different outputs.
Annual accounts, confirmation statements, and director appointment filings together tell a surprisingly complete structural story about a sector. We walk through what is available for UK industrials and what remains invisible.
Revenue for private companies is never disclosed directly. But filing data, headcount, payroll indices, and job category distributions combine into a triangulated estimate that is materially closer to reality than any single source alone. Here is how each source contributes.
The framing that sourcing is mostly relationship-driven turns out to be half the story. The other half, building the initial universe of names, is the part that consumes disproportionate analyst time and is most amenable to structure.
When three independent deal teams are working the same sector, they typically produce long-lists with substantial overlap. The problem is not effort: it is that everyone is drawing from the same visible layer of the market. Below that layer sits the more interesting set.
The UK filing regime is unusually rich compared to most jurisdictions. Annual accounts, PSC registers, and mortgage charges together create a structural picture of any UK-registered company. Most data vendors skim the surface. We look at what is deeper.
A keyword search index can be rebuilt. A well-structured sector taxonomy, one that correctly partitions a market into its meaningful competitive sub-segments, takes years to develop and is nearly impossible to replicate from the outside.
Raw headcount is a noisy revenue proxy. Asset-heavy industrials employ many more people per unit revenue than software businesses. Labour-type composition, specifically the ratio of technical to operational to commercial roles, is a better normalisation variable.
An entity graph for a sector needs three things: reliable entity identification, relationship types that reflect how companies actually interact in that market, and a consistent resolution layer to handle the same entity appearing under multiple names. We describe our approach.
New deal teams entering a sector face an asymmetric information problem. The incumbents have years of relationship history. The new entrant needs to build a credible universe from scratch. Structured extraction from the public record is the only scalable answer.
Healthcare services is one of the more complex sectors to map from public data. CQC registration data, NHS contract awards, and employment filing patterns each illuminate a different sub-segment. This note describes how we approach the integration.
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