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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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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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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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Your Generative AI LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers

DataKitchen

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure. Embedding: The retrieved data is encoded into embeddings that the LLM can interpret.

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Rocket Mortgage lays foundation for generative AI success

CIO Business Intelligence

Generative AI is becoming the virtual knowledge worker with the ability to connect different data points, summarize and synthesize insights in seconds, allowing us to focus on more high-value-add tasks,” says Ritu Jyoti, group vice president of worldwide AI and automation market research and advisory services at IDC. “It

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