TL;DR: Group structure data is the structured record of how legal entities relate to each other inside a corporate group — parent companies, subsidiaries, branches, joint ventures, and ultimate beneficial owners. B2B teams use it for account-based marketing, sales territory planning, KYC and AML compliance, credit and risk decisions, master data deduplication, and M&A intelligence. It works alongside firmographic data, not instead of it.
A group structure describes how a single corporate group is wired together as a network of legal entities — which company owns which, which entities share a parent, where the ultimate beneficial owner sits at the top of the chain. Group structure data is the dataset version of that picture: a structured record of the relationships, ownership percentages, and entity types that make up a corporate group.
This article defines group structure data in the B2B context, walks through the relationship types it covers, and goes deep on the six use cases where teams reach for it. Use it as a reference when scoping a data project that needs more than a flat list of companies — when the question is not "which companies?" but "which companies belong together?".
What is group structure data?
Group structure data is the structured representation of how legal entities are linked inside a corporate group. Where company-level firmographic attributes describe a single company in isolation — its size, industry, headquarters, revenue — group structure data describes the wiring between companies. It answers questions like: who owns this entity? what does this entity own? which other entities share the same parent? what is the path from this subsidiary up to the ultimate beneficial owner?
A typical group structure record contains the standard set of fields needed to make those relationships unambiguous:
- Entity name — the legal name of the company on the record.
- Registration number — the official identifier from the national registry.
- Country of incorporation — the jurisdiction the entity is registered in.
- Parent entity — the immediate parent company, plus a pointer up the chain to the ultimate parent.
- Ownership percentage — the share the parent holds in the entity (50.1%, 100%, etc.).
- Type of relationship — subsidiary, branch, joint venture, sister company, holding company, ultimate beneficial owner.
- Effective dates — when the relationship started and, if applicable, when it ended.
A concrete example makes this tangible. Alphabet Inc. is the parent of Google LLC, which in turn is the parent of YouTube LLC, Fitbit LLC, and Google Cloud entities incorporated in different countries. Each of those is a separate legal entity with its own registration number, jurisdiction, and operations. A group structure record on YouTube would list Google LLC as its immediate parent and Alphabet Inc. as its ultimate parent — with the relationship type, ownership percentage, and effective dates attached. Without that record, a B2B team looking at YouTube as a single firmographic profile has no idea it sits inside the Alphabet group.
Types of corporate relationships in group structure data
Group structure data covers a small vocabulary of relationship types. Each one describes a different kind of link between two legal entities.
- Parent ↔ subsidiary. The parent owns more than 50% of the subsidiary's voting shares, giving it control. The subsidiary remains a distinct legal entity with its own registration number and books.
- Branch. Not a separate legal entity — a branch is the parent operating in another jurisdiction under the same legal identity. Branches show up in registries but do not have their own ownership chain.
- Joint venture. A separate legal entity owned by two or more parents, typically for a specific purpose. Ownership is shared and no single parent has full control.
- Holding company. A parent whose primary purpose is to hold equity in other entities rather than operate a business directly. Often inserted between the operating subsidiaries and the ultimate parent for tax or regulatory reasons.
- Sister company. Two entities that share the same parent but have no direct ownership link between them. They are siblings, not parent-child.
- Ultimate beneficial owner (UBO). The natural person or top-level entity that ultimately controls the corporate group, after walking up the chain through every holding company and intermediate parent. UBOs are central to compliance and AML reporting.
In practice these relationships nest. A typical multinational has the ultimate parent at the top → a regional holding company in the middle → operating subsidiaries at the bottom → branches of those subsidiaries in additional jurisdictions. Joint ventures and sister companies fan out sideways at any level. A clean group structure dataset stitches all of this into a single, queryable graph.
How to use group structure data — six use cases
This is where group structure data earns its keep. Six use cases dominate. Each one is a problem that flat firmographic data alone cannot solve.
Account-based marketing (ABM)
ABM teams sell to entire corporate groups, not isolated entities. When a regional subsidiary signs a deal, the next conversation is often with sister subsidiaries in adjacent markets and with the parent that consolidates procurement. Group structure data lets ABM teams assemble the full account map up front: every entity inside the group, every country it operates in, every subsidiary that should be on the same target list. Without it, ABM platforms treat each subsidiary as an independent account and miss the group-level signal entirely. For a deeper walkthrough of the prospecting angle, see our companion guide on account-based marketing and the dedicated piece on group structure as GTM intelligence for subsidiary targeting.
Sales prospecting and territory planning
The other side of the ABM problem is operational. When two reps each prospect into a different subsidiary of the same parent, the customer experiences two uncoordinated outreach campaigns and the sales team books duplicate meetings. Group structure data is what RevOps teams use to assign related entities to a single account owner — the rep covering Acme Corp also covers every Acme subsidiary worldwide. The territory planning logic flips from "companies in this geography" to "groups whose ultimate parent sits in this geography", which is the unit that actually matters for enterprise selling.
KYC, AML, and compliance
Compliance teams have to know who they are doing business with — and that "who" is rarely the entity in front of them. KYC, AML, and beneficial-ownership reporting all require walking the ownership chain up to the ultimate beneficial owner, screening every entity in between against sanctions and PEP lists, and re-running the check whenever the corporate structure changes. Group structure data is the operational input to those workflows. It also feeds KYB (Know Your Business) checks where the question is the legitimacy and ownership of a counterparty rather than an individual customer. For a tooling overview, see the KYB data providers comparison.
Risk assessment and credit decisions
Credit and risk teams care about exposure across an entire corporate group, not exposure to one entity. A €5M credit line to a small subsidiary looks different when the parent is a Fortune 500 multinational than when the parent is a one-person shell company in a high-risk jurisdiction. Group structure data gives risk teams visibility into the consolidated picture: cross-entity guarantees, intra-group lending, the country of the ultimate parent, and the chain of holdings that could expose the lender to contagion if one part of the group fails.
Master data management
Inside the buyer's own systems, group structure data is what stitches duplicate records together. A multinational customer typically appears in the CRM as five different entities, in the ERP as four different vendor records, and in the support system as another set. Without the ownership chain, every system thinks each entry is a separate company. Master data teams use group structure data as the join key: every record that traces back to the same ultimate parent is one customer for revenue reporting, one vendor for procurement consolidation, and one account for executive reviews.
M&A intelligence
When a target company is acquired, the buyer inherits its full group — every subsidiary, branch, and joint venture. M&A teams use group structure data during due diligence to scope the integration: how many entities will need to be consolidated, in which jurisdictions, and what the regulatory filings look like in each. Post-deal, the same data drives integration planning — which subsidiaries roll up to the new parent immediately, which transition over a longer period, and which spin off. For competitive intelligence outside of an active deal, the same dataset surfaces acquisition trails — which holding companies have been quietly absorbing competitors over the past five years.
Where does group structure data come from?
The raw signal is public, but it is fragmented across hundreds of jurisdictions. National business registries hold the primary records: Companies House for the UK, SEC EDGAR for US-listed entities, and equivalents in every country with a registered companies body. Regulatory filings (annual reports, 10-Ks, parent disclosures) provide additional ownership detail, especially for listed companies. Beneficial-ownership registers — increasingly mandated by EU and US legislation — add the UBO layer that registries traditionally did not capture.
The freshness problem is the central data-quality challenge. Corporate structures change constantly: subsidiaries are spun up, acquired, merged, dissolved, redomiciled. A group structure dataset that is six months out of date is dangerously stale for compliance and credit use cases. Third-party aggregators add value by reconciling registry signals across jurisdictions and refreshing on a continuous cadence — daily or weekly for active records, real-time for premium tiers. When evaluating providers, ask about update cadence, the depth of coverage in non-Anglophone jurisdictions, and how UBO disclosures are reconciled when registries and filings disagree.
Group structure data vs firmographic data
Firmographic data describes what a company is in isolation — its size, industry, headquarters, revenue. Group structure data describes how that company relates to others. The two are complements, not substitutes: most B2B problems need both. Firmographic data answers "is this the right kind of company to target?". Group structure answers "is this the right entity to engage, or should I be talking to its parent or a sister subsidiary?".
A concrete pairing: an ABM team wants to target the North American subsidiaries of a European parent. Firmographic data tells them which subsidiaries have 50–500 employees and operate in retail. Group structure data tells them which subsidiaries report up to the same parent and therefore belong on the same account team's list. Used together, they produce a precise list — the exact entities, in the exact countries, that match the buyer's ICP and roll up to a single decision-making centre.
For a fuller primer on the firmographic side, see the firmographic data definition guide, and for a vendor comparison on that side of the data layer see our top firmographic data providers listicle.
Frequently asked questions
What is the difference between group structure and firmographic data?
Firmographic data describes a single company in isolation: its size, industry, headquarters, revenue, employee count. Group structure data describes the relationships between legal entities: which company owns which, which entities share a parent, who the ultimate beneficial owner is. Firmographic data tells you what a company is; group structure data tells you what group it belongs to and which other entities sit in that group with it.
How is group structure data collected and updated?
Group structure data is built primarily from national company registries (Companies House, SEC EDGAR, and their equivalents in every jurisdiction), regulatory filings such as annual reports and parent disclosures, and beneficial-ownership registers. Third-party data providers reconcile these sources across jurisdictions and refresh continuously — daily or weekly for active records — because corporate structures change constantly. Update freshness is the most important quality signal for compliance and risk use cases.
Can I get group structure data for private companies?
Yes, with caveats. Listed companies file detailed parent and subsidiary disclosures, which makes their group structure relatively easy to reconstruct. Private companies vary by jurisdiction: many EU countries publish parent and subsidiary records through national registries, the UK's Companies House publishes detailed group structure for incorporated entities, and US private-company structure is harder to verify because federal disclosure obligations are lighter. For private companies in opaque jurisdictions, beneficial-ownership registries and third-party reconciliation are the practical paths to coverage.
What is an ultimate beneficial owner (UBO)?
The ultimate beneficial owner is the natural person — or, in some definitions, the top-level legal entity — that ultimately owns or controls a corporate group. UBOs are determined by walking up the ownership chain through every holding company and intermediate parent until you reach an individual or an entity with no further parent. UBO disclosures sit at the heart of AML and beneficial-ownership compliance because they identify who actually benefits from a transaction, not just which entity signs the contract.
Why is group structure data important for ABM?
Account-based marketing treats the buying centre as a single account, not as a list of independent companies. Multinationals make many decisions at the parent level: vendor consolidation, master agreements, technology standards. Without group structure data, ABM platforms see each subsidiary as a separate account and miss the group-level signal — they cannot identify all of the parent's subsidiaries, route them to the same account team, or measure the relationship at the right unit. With group structure data, the account map is whole and the strategy can target the group as one customer.
