## [Data Semantics](/en/glossary/data-semantics) ## What Is Data Semantics Data semantics is the meaning of data and the context needed to interpret it correctly. It includes what a field or business term represents, how a value is measured, which rules apply, and how the information relates to other business concepts. For example, a report may show 10,000 active customers. Understanding that number requires knowing what “customer” means, what makes a customer “active,” and which period the figure covers. One team might count customers with a current contract; another might count anyone who purchased in the past 12 months. Both calculations may be technically correct, but they answer different business questions. Data semantics makes those meanings explicit so people, applications, and AI systems can interpret information appropriately. ## What Problems Does Data Semantics Address? Data can have the right format and still be misunderstood. A numeric value does not explain its currency or unit. A date does not reveal whether it records an event or the time that event was entered into a system. Even a familiar term such as “revenue” may refer to booked, billed, or recognized revenue. When these distinctions are undocumented, consumers fill in the gaps with their own assumptions. Reports become difficult to reconcile, applications may apply the wrong business rules, and AI systems can produce plausible answers based on an incorrect interpretation. Clear semantics helps consumers recognize which information is appropriate to their question. It also helps teams identify when different definitions are legitimate, rather than forcing every domain to use the same interpretation. ## What Gives Data Meaning - Business definitions – Whether “customer” represents a person, a household, or a legal entity. - Units and measurement – Whether an amount is expressed in dollars or thousands of dollars, and which currency applies. - Time and scope – Whether a date represents the event date or record creation date, and which reporting period a figure covers. - Calculation rules – How net revenue is calculated and which refunds or discounts are included. - Relationships – How a customer relates to accounts and contracts, and whether a transaction belongs to an account or an individual. These details help consumers understand what a value means and when it can be used. Clear definitions support consistent interpretation, while source accuracy, freshness, and governance determine whether the information is suitable for a particular decision. ## Data Semantics and Related Concepts ### How Data Semantics Differs from a Semantic Layer Data semantics describes the meaning to be understood. A [semantic layer](https://www.denodo.com/en/glossary/semantic-layer-definition-benefits-modern-applications) is a mechanism for making agreed-upon meaning available and reusable across data consumers. For example, semantics defines an active customer as someone with a current contract; a semantic layer can make that definition and its calculation available to dashboards, applications, and AI agents. An organization can document semantics in a business glossary, metadata, or a domain model. A semantic layer helps put those definitions into use as consumers access and work with data. This distinction separates the question of what data means from the question of how that meaning is consistently applied. ### How Ontologies and Knowledge Graphs Relate An [ontology](https://www.denodo.com/en/glossary/ontology) formally describes domain concepts and how they can relate. A [knowledge graph](https://www.denodo.com/en/glossary/knowledge-graph) represents particular entities and their connections. Both can help express semantics, but they serve different purposes: - Data semantics explains what “customer” and “holds a contract” mean. - An ontology defines Customer and Contract as concepts and specifies the relationship between them. - A knowledge graph shows that Acme Corporation holds Contract 456. - A semantic layer makes agreed customer definitions and contract metrics reusable by data consumers. These concepts can complement one another without all being required. Shared business meaning can be established without a formal ontology or a knowledge graph. ### Where Denodo Fits Understanding data semantics is the starting point. Organizations also need a practical way to apply shared definitions and relationships across distributed systems, rather than recreating them for every new consumer. Denodo’s [universal semantic layer](https://www.denodo.com/en/solutions/by-capability/universal-semantic-layer) makes business definitions, metrics, and relationships reusable across data sources and consumers. This supports [semantic trust](https://www.denodo.com/en/glossary/semantic-trust-definition-importance-and-key-components) by helping people, applications, and AI agents work from explicit business meaning. Denodo’s [AI data layer](https://www.denodo.com/en/data-management/ai-data-layer) combines semantic trust with connectivity, live delivery, and governed access to create [active context](https://www.denodo.com/en/data-management/active-context). This brings shared meaning together with current enterprise information and policies appropriate to the consumer and task, supporting governed, reusable enterprise data products. [Explore Denodo’s Semantic Layer solution](https://www.denodo.com/en/solutions/by-capability/universal-semantic-layer) [Back to Glossary page](/en/glossary)