Enterprise Knowledge Graph

Connect Your Data, Power Your Intelligence

Data Harmonization

By applying identification rules and resolving inconsistencies, the knowledge graph integrates diverse information sources including documents, databases, social profiles and unstructured language texts into a single, organized structure.

Semantic Extraction

Sophisticated natural language processing, machine learning and semantic modeling techniques deeply examine all available information. They identify valuable entities, categorize core topics, derive implicit relations and extract contextual meanings and intent that were previously hidden within structured and unstructured data.

Graph Storage

The semantically enriched knowledge models extracted from enterprise information are centrally stored in a standardized graph data model format, allowing interoperability with any analytic or visualization graph technology. This unified schema supports flexible, comprehensive querying and multi-dimensional exploration of enterprise intelligence.

Stakeholder Integration

Interfaces and collaboration capabilities enable decision-makers, subject matter experts and other personnel across business units, geographies and departments easy access to insights surfaced from the knowledge graph.

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Futureproof Design

Built on open standards, models and data remain reusable and can reference internal/industry models for improved governance.

Discovery Power

Semantic analysis and graph techniques reveal hidden correlations, topics and facts within structured and unstructured data.

Strategic Advantage

Quality insights from all corporate information support evidence-based strategizing and risk/opportunity evaluation.

Partnership Approach

Dedicated experts collaborate throughout implementation to configure the knowledge graph for long-term value, ensure user adoption and track tangible impacts.

Enterprise Knowledge Graph

Connect Your Data, Power Your Intelligence

Case study 1

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