Follow the exposure.
Follow ownership, transactions and shared dependencies to understand how a warning in one part of the business connects to exposure elsewhere.
Platform / Enterprise Knowledge Graph
Organize data around how your business connects.
The answers you need often lie in the relationships between your data. Model those relationships and their meaning once. Let the questions change.
The challenge
Systems are in conflict. Your questions cross their boundaries.
Each system has its own version of the business: its own structures, definitions and assumptions.
Ask a question that crosses them and the reconciliation begins. Another join. Another mapping. Another pipeline.
Soon, the structure of the data is deciding which questions are practical to ask.
The platform
Give the business a model of its own.
Data + ontology = knowledge graph. The ontology describes the business independently of the systems that hold its data. DataWalk binds each source to that shared meaning.
01
Start with meaning.
Define what things are and how they relate. DataWalk maps source structures to those definitions, binding fields and records to business concepts and relationships.
This is semantic lift. The source provides the facts. The ontology gives those facts meaning.
02
Make relationships part of the data.
Relationships and the rules that maintain them belong to the shared model. As source data changes, DataWalk refreshes dependent connections and calculated values.
The connections remain available for the next question, without rebuilding them for each analysis.
03
One model. More than one way to ask.
Search when you know what you need. Calculate across the whole. Follow relationships when you don’t know where they lead.
Graph, relational and search analysis work on the same knowledge graph. No separate analytical copies to reconcile.
“DataWalk allowed us to bring all of our data together in one place, standardize it for the first time, and finally run true cross-business-line analysis. Before that, we were completely siloed.”
Freedom to explore
The next question wasn’t in the requirements.
Connecting enterprise data opens questions that no individual system was built to answer. About hidden exposure, untapped opportunity and the consequences of change.
Understand the dependencies.
Trace connections between suppliers, infrastructure, products and customers. Explore where disruption could spread and which operations depend on the same points of failure.
Discover opportunity.
Connect customer relationships, product use and organizational networks. Look for unmet needs and opportunities beyond the view of any single business line.
Give AI context to investigate.
Connect business definitions with facts and relationships, giving AI a foundation for gathering evidence and following an inquiry across systems.
Let the answer change the question.
Built for enterprise use
A knowledge graph the business can depend on.
An enterprise knowledge graph must support demanding analysis while keeping shared data secure, traceable and current. DataWalk brings those capabilities together so the graph can serve more teams, more use cases and more data.
01
Scale and performance.
Explore relationships across billions of entities. DataWalk’s set-based execution processes connected populations, supporting questions that combine large starting sets with multiple levels of relationships.
02
Security and governance.
Permissions govern access to records, attributes and connections during analysis, including path finding. Analysts, applications and AI agents work within their authorized access, with activity recorded for audit.
03
Shared meaning and traceable data.
Sources map to common business definitions that teams and applications can reuse. Records retain their source identifiers, preserving the connection between the shared model and the evidence behind it.
04
Continuity as data and requirements change.
Incremental loads bring new data into the existing graph. Dependency refresh updates configured connections and calculated values, while new sources and use cases build on the model already in place.
Resources
Explore what runs on the model.
Platform
Explore Connected Analytics
Connected Analytics
Search, calculate and traverse the same connected data.
Platform
Explore AI & Context
AI & Context
Put AI to work with business context and analytical tools.
Platform
Explore Architecture & Deployment
Architecture & Deployment
See how the knowledge graph is stored, computed and deployed.
FAQ
Questions about your model.
Do we need to model the whole enterprise first?
No. Start with the domain and questions you need to address. Define the relevant concepts and relationships, map the sources and extend the model as requirements grow.
Does DataWalk replace our source systems?
No. Data used for analysis is ingested and bound to the ontology. Warehouses, lakehouses and operational systems can remain in place as systems of record.
Is DataWalk LPG or RDF?
DataWalk uses its Enterprise Object Graph model. It represents entities and relationships, including business facts as objects in their own right, with multiple participants, attributes and time. This gives complex relationships a place in the model as business requirements evolve.
What keeps derived connections current?
DataWalk tracks the dependencies behind configured connections and calculated values. When their inputs change, dependency refresh updates the affected results.
What happens when a source or definition changes?
A source change is handled through its mapping to the ontology. A change in business meaning is handled in the model, with the corresponding updates to dependent calculations, analyses and applications.
Bring the question your schemas make difficult.
Start there. See where the answer takes you.
