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AI & the enterprise: protect your data, protect your enterprise value

CIO Business Intelligence

The premise was that enterprises needed to secure their critical data more stringently in the wake of data hacks and emerging AI processes. I wrote, “ It may be even more important for the security team to protect and maintain the integrity of proprietary data to generate true, long-term enterprise value. Years later, here we are.

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Enterprises reevaluate virtualization strategies amid Broadcom uncertainty

Network World

Its enterprise-grade. For enterprises navigating this uncertainly, the challenge isnt just finding a replacement for VMware. It would take a midsize enterprise at least two years to untangle much of its dependency upon VMware, and it could take a large enterprise up to four years. IDC analyst Stephen Elliot concurs.

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Network convergence will drive enterprise 6G wireless strategies

Network World

Enabling such seamless integration would require new standards governed by different bodies, hardware advances, and changes to network infrastructure all of which happen over long time scales. However, this isnt something that enterprises can accomplish on their own, he adds. Or perhaps, the other way around.

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Gartner: 13 AI insights for enterprise IT

CIO Business Intelligence

Artificial intelligence is an early stage technology and the hype around it is palpable, but IT leaders need to take many challenges into consideration before making major commitments for their enterprises. With AI and data proliferating everywhere in the enterprise, AI and data are no longer centralized assets that IT directly controls.

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LLMOps for Your Data: Best Practices to Ensure Safety, Quality, and Cost

Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase

Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.

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Abu Dhabi set to become the world’s first fully AI-Powered government by 2027

CIO Business Intelligence

In a move to establish itself as a global leader in AI-driven government, the government of Abu Dhabi has unveiled its ambitious Abu Dhabi Government Digital Strategy 2025-2027.

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Are enterprises ready to adopt AI at scale?

CIO Business Intelligence

To overcome those challenges and successfully scale AI enterprise-wide, organizations must create a modern data architecture leveraging a mix of technologies, capabilities, and approaches including data lakehouses, data fabric, and data mesh. To learn more about how enterprises can prepare their environments for AI , click here.

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Top Considerations for Building an Open Cloud Data Lake

Data fuels the modern enterprise — today more than ever, businesses compete on their ability to turn big data into essential business insights. Increasingly, enterprises are leveraging cloud data lakes as the platform used to store data for analytics, combined with various compute engines for processing that data.

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4 Key Steps to Data Transformation Success with Data Mesh

Today, the average enterprise has petabytes of data. Disparate datasets and technologies make it more difficult than ever to give your customers and users the information and insight they need, when they need it (and how they want it) while addressing the complexities of compliance, governance, and security.

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Partner Webinar: A Framework for Building Data Mesh Architecture

Speaker: Jeremiah Morrow, Nicolò Bidotti, and Achille Barbieri

Data teams in large enterprise organizations are facing greater demand for data to satisfy a wide range of analytic use cases. In this session, you will learn: How the silos development led to challenges with data growth, data quality, data sharing, and data governance (an example of datamesh paradigm adoption).

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The Business Value of MLOps

Machine learning operations (MLOps) is the technical response to that issue, helping companies to manage, monitor, deploy, and govern their models from a central hub. Download the report to find out: How enterprises in various industries are using MLOps capabilities.