Today’s organization demands rapid insight from increasingly hybrid, varied, and changing data. Traditional enterprise data management systems can’t keep up with this growing complexity and insights are hidden from data consumers, buried amidst application silos, databases, data catalogs, and analytics applications.
Conventional graph and relational data architectures lack the access, context, and inferencing required to meet the grueling demands of innovating and monetizing advanced analytical solutions. Data catalogs provide an inventory of information assets, however if the catalog is disconnected from the rest of the enterprise data, you’re left with a metadata silo. This leaves data consumers constrained by architectural limitations for highly scalable, discovery-style analysis in relation to business problems.
Data fabrics have emerged as a modern solution to address these needs and free data, but how? A knowledge graph is the key enabling ingredient to a data fabric. As a unified graph data model enriched with logical definitions, it provides a flexible, semantic data layer that dynamically weaves together data across the organization. With a knowledge graph, you can connect to data regardless of where it’s stored, bring to life your data catalog, and empower your data and analytics teams with the data and insights they need, faster.