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Enabling AI with real-time data integration

CIO Business Intelligence

While not uncommon in modern enterprises, this reality requires IT leaders to ask themselves just how accessible all that data is. Particularly, are they achieving real-time data integration ? For AI to deliver accurate insights and enable data-driven decision-making, it must be fed high-quality, up-to-date information.

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Bridging the gap between mainframe data and hybrid cloud environments

CIO Business Intelligence

A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. These tools dont have the necessary connectors, metadata relationships, or lineage mapping that spans both mainframe and cloud environments. This presents a lack of visibility in the metadata lineage spanning across mainframe and cloud data.

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Deep automation in machine learning

O'Reilly on Data

In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. Humans are still needed to write software, but that software is of a different type. Developers of Software 1.0

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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO Business Intelligence

Managing the lifecycle of AI data, from ingestion to processing to storage, requires sophisticated data management solutions that can manage the complexity and volume of unstructured data. As customers entrust us with their data, we see even more opportunities ahead to help them operationalize AI and high-performance workloads.

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Demystify data sharing and collaboration patterns on AWS: Choosing the right tool for the job

AWS Big Data

For producers seeking collaboration with partners, AWS Clean Rooms facilitates secure collaboration and analysis of collective datasets without the need to share or duplicate underlying data. It provides data catalog, automated crawlers, and visual job creation to streamline data integration across various data sources and targets.

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5 Ways Data Modeling Is Critical to Data Governance

erwin

Today’s data modeling is not your father’s data modeling software. While it’s always been the best way to understand complex data sources and automate design standards and integrity rules, the role of data modeling continues to expand as the fulcrum of collaboration between data generators, stewards and consumers.

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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.