People navigate and reason through life with relationships. Knowledge Graphs put facts and relationships in context to enable understanding and analysis. Because Knowledge Graphs help teams understand who curates a dataset and who depends upon it being accurate and up-to-date, it helps bridge existing data silos and promote a coherent understanding across them, with better governance. Many organizations currently have hundreds of custom applications, each with their own siloed data. As enterprises shift to a data-centric approach, Knowledge Graphs have become the foundation of a flexible and extensible system that enables data to be leveraged as an organizational asset. Some of the world’s biggest companies, like Google, Apple, and Amazon use Knowledge Graphs at the core of their data-centric architectures.
Enterprise semantic layers built on top of these Knowledge Graphs drive an ability to democratize access across an organization and improve efficiency through intuitive domain representations. The concepts and relationships of an enterprise ontology that derive from the low-level (often messy) enterprise databases and data lakes are the core of these data-centric architectures to increase efficiency and ability for engineers and data scientists to not only find the information they need, but also to ensure it’s accurate and up to date. This can remove the pain points that organizations suffer through data duplication, misuse of assets, and conflicting results.
Join our session to discuss the architecture around building semantic layers that use both patterns and generators to refine the constructs of an ontology by weaving data from databases and data lakes. We will discuss use cases around organizations using knowledge graphs and semantics layers to drive business drivers such as: detecting/preventing fraud and understanding customer 360.
