Why your data catalog should be powered by a knowledge graph
Search like you’re on Google
- Metadata and data are logically organized and in machine-readable format, speeding search and discovery
- Relationships are mapped, linked, and understood by people and machines, uncovering hidden relationships between key concepts to improve search accuracy
Catalog with flexibility, speed, and scale
- Efficiently onboard, integrate, and catalog any new data source including semi-structured and unstructured in days, not months.
- Connect to any solution in your data ecosystem including data quality, data lineage, data prep, and other metadata tools. No more knowledge silos.
Activate your metadata through better automation
- Analyze and traverse lineage to understand changes to metadata, connect concepts, terms, or metric definitions to “physical” tables and columns, automate relationships between concepts and definitions.
- Build upon your foundation for next-generation AI/ML, enabling NLP-powered personal assistants on top of your metadata.
Enhance your data governance
- Map data assets to key enterprise concepts to make them discoverable and accessible for greater user self service
- Drive greater trust and consumption of key data resources throughout the organization
data.world is the only data catalog powered by an enterprise knowledge graph
What is a knowledge graph?
Knowledge graphs power some of the most ubiquitous applications in the world today including Amazon Alexa, Netflix, Facebook, and Google Search. They are extraordinarily flexible, agile, and resilient, with a different structure than traditional relational databases. Rather than representing data as a table with rows and columns, a knowledge graph captures and organizes relationships between real-world concepts in the form of a graph.
Knowledge graphs are modern data infrastructure where data and metadata connect to all users in an organization. They describe the most important and crucial things in your company that form the basis of next-generation search, recommendations, graph machine learning and AI applications such as chatbots, natural language question answering and personal assistants.
Knowledge graphs bridge the gap between how data consumers understand their business world and how companies store the data. They are unique in that business terminology is represented as concepts and relationships that are both understandable by people and machines in the exact same way.
Why now is the time for Knowledge Graph: