How Knowledge Graphs Work:
Data Fabric
Semantic metadata to power your models
Data Lineage
Intelligently track how your data has changed
Data Quality
Automate error identification through semantics
Data Pipeline
Manage repeatable, dynamic ETLs
Data Catalog
Ensure discovery of data assets
Data Lakes
A semantic layer to activate your data
The Knowledge Graph advantage
The classic data structure, a relational database, has its limitations. While you can store a lot of facts and figures, at scale discovery becomes impossible. Moreover your team’s intuition is limited. How can researchers find all things to do with “cancer” when they search “tumor” or “oncology” to build the next drug?
Helping you avoid the software graveyard
Knowledge Graphs are able to accommodate diverse data and metadata that adjusts to solve real-world problems. No more science projects. Knowledge Graphs power business applications to enable cybersecurity, data privacy, drug discovery, and many others by the same principle – a shared understanding of your data.
Knowledge Graphs are self-descriptive
The meaning of the data is stored alongside the data in the graph, in the form of the ontologies or semantic models. This makes Knowledge Graphs self-descriptive.
Enterprise Knowledge Graphs enable insights that are:
Unified
Central, collaborated upon understanding of data relationships and connections to power discovery and machine learning.
Flexible
And extensible. Based on open source standards to dynamically pull in information from external sources or across the organization to a rich, informed data model.
Intuitive
Semantics helps experts share, users identify, and machines use information the way the human brain thinks.