Remove Business Analytics Remove Data Architecture Remove Metadata
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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

We also examine how centralized, hybrid and decentralized data architectures 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.

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Big Data Hub

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.

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Intel® Technologies Move Analytics Forward. Learn more at [link]. .

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Amazon Redshift announcements at AWS re:Invent 2023 to enable analytics on all your data

AWS Big Data

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.

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Convergent Evolution

Peter James Thomas

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.