What Is Company Hierarchy Data?

What is company hierarchy data?

Company hierarchy data maps the ownership relationships between legal entities. It answers questions like: Who owns this company? What subsidiaries does this company have? What's the top-level parent of this corporate group?

It is not a picture of a company's internal structure. It is a map of which registered companies own which other registered companies, which is why it comes from company registers rather than from anyone's HR system.

The corporate linkage taxonomy

Corporate linkage is the industry vocabulary for the relationships between entities in a group. Eight terms cover almost every case, and providers use them inconsistently, which is a large part of why hierarchy data is hard to compare:

The distinction that catches people out most often is subsidiary versus branch. A subsidiary is its own company with its own registration and its own liability. A branch is the same company operating somewhere else. Screening, billing and credit decisions all turn on that difference.

A worked example: one group, every term

The taxonomy is easier to hold onto against a real group. Take Alphabet, and follow a single thread down from the top:

• Alphabet Inc. is the global ultimate. Nothing owns it. Every entity below is part of its group, worldwide.
• Google LLC is a subsidiary of Alphabet, and simultaneously a parent to the entities below it. Both terms are true at once, because they describe relationships rather than the entity itself. This is the single most common source of confusion in hierarchy data.
• Google Germany GmbH is a subsidiary of Google LLC. It is separately incorporated in Hamburg, has its own registration, and carries its own liability. It is a German company that Google owns, not "Google in Germany".
• If Google Germany GmbH opened an office in Munich that traded on the Hamburg registration, that office would be a branch. No new company, no new registration, no separate liability.
• Alphabet's "Other Bets" grouping is a division: a way of talking about part of the business that no registry has ever heard of.
• Waymo LLC is also a subsidiary of Alphabet, but on a different thread. It shares Google's global ultimate and has nothing to do with Google's operations.

That last point is what makes hierarchy data operationally hard. Ask most providers about Google and they hand you the whole Alphabet tree, Waymo and Verily included: hundreds of entities, of which one is the answer you wanted.

The domestic ultimate is the term people meet last and need most. If you are a German company selling to Google in Germany, the entity that signs your contract and pays your invoice is a German one. Alphabet Inc. is the global ultimate, but it is not who you are dealing with. The domestic ultimate is the top of the group within one country, and it is usually the entity that matters for contracts, credit and tax.

Legal forms are part of the hierarchy, not decoration on a name

Most hierarchy data is built US-first, where the legal form is a suffix you can mostly ignore: Inc., LLC, Corp. Outside the US it carries information, and dropping it loses the thread.

• GmbH (Germany, Austria): a private limited company. GmbH & Co. KG is a different thing again, a partnership whose general partner is a GmbH, and it is extremely common among German family groups.
• B.V. (Netherlands): private limited. N.V. is its public counterpart. Dutch holding structures sit in the middle of a very large share of European groups, so B.V. entities show up constantly as intermediate parents.
• S.à r.l. (Luxembourg), S.A. (France, Spain, Luxembourg), S.p.A. (Italy), AB (Sweden), Oy (Finland), A/S (Denmark).
• SE (Societas Europaea): a European public company that can move its registered seat between member states. Rare, and disproportionately common at the top of large groups.

Two practical consequences. First, the legal form tells you whether an entity can be a parent at all: a division cannot, a GmbH can. Second, the same trading name recurs across a dozen jurisdictions with a dozen forms, and the form is often the only thing distinguishing them. "Siemens" is not an entity. Siemens AG is.

Corporate hierarchy, org charts and data hierarchies are three different things

Search for "company hierarchy" and you get three unrelated subjects mixed together. They are worth separating, because only one of them is what B2B data teams mean:

• Corporate hierarchy (corporate linkage): which legal entities own which. Alphabet owns Google, Google owns Google Germany GmbH. Sourced from company registers. This is the subject of this guide.
• Organizational chart: which people report to whom inside one company. The CEO, then VPs, then teams. Sourced from HR systems. Nothing to do with ownership, and no registry publishes it.
• Data hierarchy: a computer-science concept about tree-structured data, with parent nodes and child nodes. It shares the words "parent" and "child" with corporate linkage and shares nothing else.

Academic economics adds a fourth usage: papers on "corporate hierarchy" usually mean the number of management layers within a firm. Also unrelated to ownership.

Why it matters for different teams

Sales: The warm cold call

A subsidiary of an existing customer converts at 3-4x the rate of a true cold prospect. If you have a deal with Salesforce in the US, their subsidiaries in the UK, Germany, and Japan are warm leads. You can reference the existing relationship, ask for internal introductions, and skip the trust-building phase entirely.

Without hierarchy data, your reps cold-call these subsidiaries without knowing the relationship exists. They might even pitch against a colleague who's already selling into a different entity of the same group.

Finance: Guarantor risk and consolidated billing

When a small subsidiary requests $500K in payment terms, the risk profile depends entirely on who backs them. A subsidiary of a Fortune 500 company carries less risk than a standalone company with the same revenue. Hierarchy data lets your credit team assess the true financial backing behind any entity.

For billing, hierarchy data enables consolidated invoicing across a corporate group. Instead of sending 12 separate invoices to 12 Siemens entities, you can consolidate them under one master account.

Compliance: Sanctions screening and conflict checks

The OFAC 50% rule means a sanctioned parent contaminates its majority-owned subsidiaries. To comply, you need to screen not just the entity you're dealing with, but its entire ownership chain up to the global ultimate. Hierarchy data makes this possible without manually researching each company's ownership.

For consulting, legal, and accounting firms, hierarchy data is essential for conflict-of-interest checks. Is this new prospect a subsidiary of a company you already represent? Without hierarchy data, you'd never catch it.

Procurement: Vendor spend consolidation

Large enterprises buy from hundreds of vendors, many of which are subsidiaries of the same parent company. Without hierarchy data, procurement can't see that they're spending $2M across 8 different Deloitte entities. Consolidating that spend under one vendor relationship gives you negotiation leverage and volume discounts.

Where hierarchy data comes from, and why it is hard

Hierarchy data is built from official company registers, filings and legal-entity identifier records. That sounds simple and is not, for a structural reason: every country publishes its register differently. Germany's Handelsregister, the UK's Companies House, France's RCS and the US state-by-state Secretary of State systems each use their own identifiers, their own legal forms and their own update cadence. None of them link to each other.

So a hierarchy provider's real job is reconciliation: deciding that an entity in one register is the same company as an entity in another, and that one owns the other. That is where providers diverge, and it is why two vendors can hand you different parents for the same company.

Three failure modes follow from it, and they are worth recognising in any provider's data:

It goes stale. Corporate structures change constantly through acquisitions, divestitures and restructuring. A hierarchy snapshot from last year is already wrong in places.

It is often the whole tree, not the part you asked about. Look up Google and a tree dump gives you every Alphabet entity, Waymo and DeepMind included. If you wanted Google's German subsidiary, you now have 200 entities to filter.

Legal forms get flattened. Providers built around US data tend to treat GmbH, B.V., S.à r.l. and SE as decoration on a name rather than as the legally meaningful entity types they are.

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