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We also examine how centralized, hybrid and decentralized dataarchitectures support scalable, trustworthy ecosystems. As data-centric AI, automated metadata management and privacy-aware data sharing mature, the opportunity to embed data quality into the enterprises core has never been more significant.
Today, the way businesses use data is much more fluid; data literate employees use data across hundreds of apps, analyze data for better decision-making, and access data from numerous locations. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.
They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big dataanalytics. On the other hand, they don’t support transactions or enforce data quality. Intel® Technologies Move Analytics Forward. Learn more at [link]. .
Generative SQL uses AI to analyze user intent, query patterns, and schema metadata to identify common SQL query patterns directly allowing you to get insights faster in a conversational format without extensive knowledge of your organization’s complex database metadata.
Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. Of course some architectures featured both paradigms as well. This required additional investments in metadata.
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