Ontology: The Digital Twin of Your Enterprise

An enterprise's data is its lifeblood, but in most organizations, it flows through a maze of disconnected systems. Data teams manage databases and ETLs while operations teams speak in terms of Customers, Shipments, and Facilities. This gap between the physical reality of data and the conceptual reality of the business creates a permanent communication drag on the organization. It makes analytics slow, AI unreliable, and a shared operational picture impossible to achieve.

The Arkham Ontology is engineered to bridge this chasm. It is not merely a data model; it is the digital twin of your enterprise—a shared, living representation of your objects, processes, and their complex relationships. By mapping your data landscape to a clear, human-readable vocabulary, the Ontology establishes a common language for business and technical teams alike. This creates a single source of truth that radically accelerates development, ensures analytical consistency, and provides the rich semantic context required to power operational AI.

But the Ontology is more than a map. As described in the pillars below, it is an active, kinetic system. It supports not only reading data but writing it back, capturing operational decisions and user actions as a new, structured layer of insight. This transforms the Ontology from a passive model into a dynamic feedback loop, enabling your organization to learn, adapt, and automate at a scale that was previously unimaginable.

The Pillars of a Digital Twin: Semantics and Kinetics

The Arkham Ontology is more than a passive data model; it's an active, operational layer. It has two key dimensions that work together to create a true digital twin of your business.

• Semantics: This is the foundational map of your business. By defining Objects, Properties, and the Links between them, you decouple business logic from physical data storage. This creates a stable, human-readable layer that accelerates development and provides the essential context our AI Copilot TARS.
• Kinetics: This is the active layer of the Ontology. Once the semantic model is in place, it becomes a central hub for action. From updating data registries and to triggering workflows.

This dual-sided approach ensures the Ontology is not just a reference, but a dynamic tool for understanding and operating the business.

Core Components

The Arkham Ontology is comprised of two core components that allow you to model your business and then define logic on top of that model.

• Ontology Manager: Model your real-world business entities into reusable Objects.
• Metric Store: Define centralized, reusable business logic on top of your Objects.

Core Concepts

The User's Workflow: Semantics to Action

The Ontology bridges the gap between your physical data and business logic through a clear, three-step process designed for builders.

• Model Your Business (Ontology Manager): The journey begins in the Ontology Manager. Here, a data architect maps columns from trusted Production datasets to create foundational Objects like Customer or Product and defines the Relationships between them (e.g., "Customer places Order").
• Define Your Logic (Metric Store): Once the business is modeled, an analyst uses the Metric Store to build reusable Metrics on top of those objects. This is where a complex SQL aggregation becomes a trusted, governed metric like "Monthly Recurring Revenue."
• Consume with Confidence (Platform-Wide): With the semantic layer in place, everyone builds from the same source of truth. A business user exploring a Workbook, a data scientist building a model, and the TARS AI Copilot answering a natural language question all use the same Objects and Metrics, guaranteeing consistent and reliable results.

Related Capabilities

The Ontology serves as the semantic backbone for the entire Arkham platform, directly integrating with and enhancing several other key capabilities.

• Data Platform: The Ontology consumes trusted, production-grade datasets from the Data Platform as the source for its object mappings.
• AI Platform: The Ontology provides high-quality, semantically-rich features for models in the ML Hub and delivers governed metrics for analysis in Workbooks.
• TARS: The Ontology gives TARS the contextual understanding of business concepts it needs to answer complex, natural-language questions accurately.