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As connectivity became the rule rather than the exception for computers, the importance and usefulness of sharing data necessitated the ability to share how that data was defined. Separating the definitions of the metadata from the data had the benefit of simplifying validation and introducing flexibility. DataGovernance.
Graphs boost knowledgediscovery and efficient data-driven analytics to understand a company’s relationship with customers and personalize marketing, products, and services. Graph-based solutions further leverage the relationships among the entities involved to create a semantically enhanced machine learning model.
The combination of AI and search enables new levels of enterprise intelligence, with technologies such as natural language processing (NLP), machine learning (ML)-based relevancy, vector/semantic search, and large language models (LLMs) helping organizations finally unlock the value of unanalyzed data. How did we get here?
There is a confluence of activity—including generative AI models, digital twins, and shared ledger capabilities—that are having a profound impact on helping enterprises meet their goal of becoming data driven. Equally important, it simplifies and automates the governance operating model.
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