Connect records to reality
Codes and fields alone may not explain which product, asset, person, or activity a record represents.
Enterprise data ontology Beta
Understand your operations through connected data.
Products, equipment, transactions, teams, documents, and processes. Explore how an ontology connects information across systems to the objects and relationships in your operations.
Talk to salesWhen collected data still leaves the bigger picture unclear
Understand connected work
When one object's status changes, you search across records to identify related work, transactions, and responsible teams.
Share business definitions
When teams use the same term or status differently, analysis and AI answers start from different assumptions.
What is an ontology?
An ontology is a shared blueprint for describing real-world objects and relationships in data. It defines what an object means, which properties describe it, and how it relates to others. In a business, that can include products, equipment, customers, documents, teams, and processes.
Objects
What does the model represent?
Define the types and meanings of the objects that matter: products, equipment, people, teams, documents, or activities.
Properties
Which information describes them?
Specify information such as location, quantity, status, and time.
Relationships
How do the objects connect?
Describe which equipment supports a task, which products belong to an order, or which documents provide evidence for an activity.
Definitions
Which meanings and conditions are shared?
Agree what completion means, how objects are distinguished, and when relationships apply so teams interpret data consistently.
- Source data
- Documents and drawings
- Business systems
- Operational records and measurements
- Shared business model
- Object meanings and properties
- Relationships between objects
- Shared terms and definitions
- Use connected information
- Explore related data and evidence
- Analyze status and dependencies
- Give AI business context
Different industries. The same modeling approach.
The objects you connect depend on the question your organization needs to answer. These fictional examples illustrate the approach across different kinds of work.
Assets and operations
Which tasks and inspection records relate to this asset?
- FacilityFacility A
Facility records
Houses - EquipmentAsset B
Asset register and status
Check - TaskInspection
Work schedule
Record - HistoryCheck record
Results, time, and owner
Products and supply
Which inventory and orders need review when supply changes?
- SupplierSupplier A
Supplier records
Supply - ProductPart B
Product and part catalog
Stock - InventoryStock
Location, quantity, status
Assign - OrderOrder C
Order records and dates
Teams and knowledge
Which team owns this work, and which guidance should it use?
- ProjectProject A
Project register
Includes - ActivityReview
Work details and status
Owner - TeamOperations
Roles and responsibilities
Uses - DocumentGuidance
Criteria, process, version
These conceptual diagrams illustrate possible adoption scenarios. Data meanings, relationships, and usage are reviewed with the people who know the work.
How does ontology relate to a digital twin?
A digital twin represents real objects or operations through data and models that reflect changes in reality. An ontology can supply shared meanings and relationships within that representation.
- Meanings and relationships
What is represented and how it connects
The shared model an ontology defines - Data from reality
How status, history, and measurements update
Data connections and update design - Analysis and use
Which questions and scenarios to evaluate
The analysis, prediction, or simulation models needed
For a digital twin, also consider data updates, analytical models, and operational use. The feasibility and scope of real-time connections, simulation, and system control require a separate review of your goals and environment.
Start with the work you want to improve.
Start with what you need to understand and which decisions you want to support. Then identify the objects and data those questions require.
Work and expected results
Describe the questions and results you need, such as understanding status, tracing dependencies, or finding evidence.
Objects and data
Review the objects involved, their data sources, identifiers, and how records change.
Relationships and definitions
Work with domain owners to define connections and agree what terms and states mean.
Access and operating environment
Review access permissions, integration scope, internal-network needs, and how the model will be used.
Before you adopt an ontology
What is an enterprise data ontology?
It is a shared model for describing real-world objects and relationships in data. It defines meanings, properties, and connections for products, assets, people, teams, documents, or activities. It is not limited to one industry or data format.
Why do companies describe ontology differently?
The core concept is a model that defines object meanings and relationships. Some products use the term more broadly to include data integration, analytics, AI, and operational actions. Evaluate both the model and the capabilities a particular implementation provides.
Are ontology and digital twins the same thing?
An ontology defines object meanings and relationships. A digital twin uses data and models to reflect real objects or operations as they change. An ontology can provide its shared model; data updates, analysis or simulation, and operational connections also need design.
How can ontology work with RAG?
RAG retrieves material to support an answer, while an ontology defines the meanings of the objects and relationships that material describes. Together, they can provide document evidence and context across systems.
How is it different from a knowledge graph?
An ontology specifies how objects and relationships are described. A knowledge graph represents actual objects and connected facts as a graph. It can use ontology definitions to connect data consistently.
Which data can we evaluate?
Start with data relevant to your workflow, such as documents, drawings, business-system records, operational history, or measurements. Data meanings, identifiers, quality, and access determine connection methods and usage scope.
Can we discuss an internal-network deployment?
For internal-network requirements, we review data access, processing, and existing infrastructure. Ontology adoption is scoped around the systems involved, operating requirements, and security needs.
What should a beta adoption inquiry include?
Describe the work you want to improve, your expected results, and the data you use. We can review the objects and relationships involved, the people who can confirm their meanings, and security and operational requirements.
What should your organization connect?
Tell us about the work you want to improve and the data you use, whatever your industry or data format. We can explore the objects, relationships, and adoption scope together.
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