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Fueling Enterprise Generative AI with Data: The Cornerstone of Differentiation

Cloudera

By leveraging an organization’s proprietary data, GenAI models can produce highly relevant and customized outputs that align with the business’s specific needs and objectives. Structured data is highly organized and formatted in a way that makes it easily searchable in databases and data warehouses.

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Want AI? Here’s how to get your data and infrastructure AI-ready

CIO Business Intelligence

CIOs are responsible for much more than IT infrastructure; they must drive the adoption of innovative technology and partner closely with their data scientists and engineers to make AI a reality–all while keeping costs down and being cyber-resilient. That’s because data is often siloed across on-premises, multiple clouds, and at the edge.

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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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5 modern challenges in data integration and how CIOs can overcome them

CIO Business Intelligence

million terabytes of data will be generated by humans over the web and across devices. That’s just one of the many ways to define the uncontrollable volume of data and the challenge it poses for enterprises if they don’t adhere to advanced integration tech. As well as why data in silos is a threat that demands a separate discussion.

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Making OT-IT integration a reality with new data architectures and generative AI

CIO Business Intelligence

Manufacturers have long held a data-driven vision for the future of their industry. It’s one where near real-time data flows seamlessly between IT and operational technology (OT) systems. Legacy data management is holding back manufacturing transformation Until now, however, this vision has remained out of reach.

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Real-time artificial intelligence and event processing  

IBM Big Data Hub

AI and event processing: a two-way street An event-driven architecture is essential for accelerating the speed of business. Non-symbolic AI can be useful for transforming unstructured data into organized, meaningful information. This helps to simplify data analysis and enable informed decision-making.

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How ZS built a clinical knowledge repository for semantic search using Amazon OpenSearch Service and Amazon Neptune

AWS Big Data

We use leading-edge analytics, data, and science to help clients make intelligent decisions. AWS services such as Amazon Neptune and Amazon OpenSearch Service form part of their data and analytics pipelines, and AWS Batch is used for long-running data and machine learning (ML) processing tasks.