Data Fabrics and Knowledge Graphs for the AI Enterprise

Data Engineering Intermediate

Data Fabrics are emerging as the most effective means of integrating knowledge throughout the Enterprise and coupled with Knowledge Graphs they deliver a unique, symbiotic relationship because the combination streamlines the process to extract data and build knowledge from the myriad of sources that comprise Enterprise data.

Data Fabrics have emerged as the most effective means of integrating knowledge throughout the Enterprise. Experts agree these fabrics are the future of enterprise analytics and AI. Gartner recommends “Data and analytics leaders must upgrade to a Data Fabric design that enables dynamic and augmented data integration in support of their data management strategy.” Forrester states that “Enterprise architecture (EA) pros should use data fabric to democratize data across the enterprise for various use cases.”

Data Fabrics offer a cohesive means of integrating and sharing data regardless of differences in format, technology, or location. Data Fabrics are considered the most mature means of data integration because they organize the exchange of semi-structured, unstructured, and structured data—while offering a single access point to all data.

Gartner suggests that successful Data Fabrics must create and curate Knowledge Graphs. They are essential for governing content in terms of access management, data provenance, and data quality while unifying the terminology used to describe these assets across business lines, in an organization.

The semantic layer in Knowledge Graphs makes it more intuitive and easy to interpret, making the analysis easy for data and analytics leaders. It adds depth and meaning to the data usage and content graph, allowing AI/ML algorithms to use the information for analytics and other operational use cases.

During this presentation we will discuss building Knowledge Graphs as part of a comprehensive AI Data Fabric for your Enterprise.