[GTM glossary](/glossary.txt) Business ontology Contextual Intelligence & AI # Business ontology In short A business ontology is a formal description of the things that matter in a business and how they relate. It defines concepts such as company, subsidiary, contact, product, and industry, and spells out links like "works at", "is a subsidiary of", and "competes with". Because the meaning is written down in a consistent form, systems and AI models can reason over data instead of treating it as loose text. It is the shared vocabulary behind a knowledge graph. ## What a business ontology contains * **Entities:** The types of things you track: accounts, contacts, products, locations, events. * **Attributes:** Properties of each entity, such as employee count or industry. * **Relationships:** How entities connect, such as parent company, customer of, or reports to. * **Rules and definitions:** What terms mean, for example what counts as an "active customer". --- ## Why GTM teams need one ### Consistent definitions Sales, marketing, and finance often use the same word differently. An ontology fixes one meaning for "account", "customer", or "SMB". ### Better AI answers A model that knows a company is a subsidiary of a larger group, or that two product names refer to one item, makes fewer wrong inferences. ### Cleaner data joins Matching records across tools is simpler when entity types and relationships are defined once. --- ## How to start small ### 1. List core entities Begin with the five or six objects your team talks about most. ### 2. Define each in one sentence Write what qualifies and what does not, and who owns the definition. ### 3. Map the relationships Draw how entities connect, including hierarchy such as parent and child accounts. ### 4. Apply it in your systems Align CRM fields, picklists, and data pipelines to the model, and update it when the business changes. --- ## Business ontology vs. taxonomy A taxonomy is a classification list, such as an industry hierarchy. An ontology goes further by defining many entity types and the relationships between them. --- ## Common mistakes * **Too ambitious:** Modeling everything delays value. Start with one use case. * **No owner:** Definitions drift without someone responsible. --- ## Business ontology FAQs **What is a business ontology?** A business ontology is a structured model of the concepts in a business, such as accounts, contacts, and products, along with their definitions and relationships. It gives people and software a shared, consistent way to interpret company data. **How is a business ontology different from a taxonomy?** A taxonomy is a classification scheme, like a tree of industries. A business ontology covers multiple entity types, their properties, and the relationships between them, so it can describe how things connect rather than only how they are grouped. **Why does a business ontology matter for AI in sales?** It gives a model clear definitions and relationships, such as which companies belong to the same group or what counts as a customer. That reduces misinterpretation and helps the model reason over data rather than guess from text. **How do you build a business ontology?** Start with one use case, list the main entities, define each in one sentence, map how they relate, and align your CRM and data pipelines to that model. Assign an owner and review it whenever your business or products change. Share term --- ## More related phrases * [Context layer](/glossary/context-layer.txt) * [Context graph](/glossary/context-graph.txt) * [Company embeddings](/glossary/company-embeddings.txt) ## Similar phrases * [Ontology](/glossary/ontology.txt) An ontology is a structured map of the concepts in a domain and how they relate, giving data and AI systems a shared, precise vocabulary for things like companies, roles, and industries. * [Knowledge graph](/glossary/knowledge-graph.txt) A knowledge graph stores entities such as companies and people as nodes and their relationships as links, giving search and AI a structured, queryable map of facts. * [Entity resolution](/glossary/entity-resolution.txt) Entity resolution is the process of deciding which records across systems refer to the same real-world company or person, so each entity has one reliable identity. * [Contextual intelligence](/glossary/contextual-intelligence.txt) Contextual intelligence is the ability to interpret data in light of its surrounding situation, so GTM teams act on what a signal means for a specific account, not just that it exists. * [Account hierarchy](/glossary/account-hierarchy.txt) An account hierarchy maps how parent companies, subsidiaries, and locations relate, so sales teams see the full buying group and avoid duplicate outreach.