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CIOs weigh where to place AI bets — and how to de-risk them

CIO Business Intelligence

There are a lot of risks and a lot of land mines to navigate,” says the analyst. Coming to grips with risk The first step in making any bet — or investment — is to understand your ability to withstand risk. This ensures that none of our sensitive data and intellectual property are availed to an outside provider.”

Risk 133
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Introducing the technology behind watsonx.ai, IBM’s AI and data platform for enterprise

IBM Big Data Hub

We can use foundation models to quickly perform tasks with limited annotated data and minimal effort; in some cases, we need only to describe the task at hand to coax the model into solving it. But these powerful technologies also introduce new risks and challenges for enterprises.

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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

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Data Lakes: What Are They and Who Needs Them?

Jet Global

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. What was at first a data stream has morphed into a data river as enterprise businesses are harvesting reams of data from every conceivable input across every conceivable business function.

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Avoid generative AI malaise to innovate and build business value

CIO Business Intelligence

Deloitte 2 meanwhile found that 41% of business and technology leaders said a lack of talent, governance, and risks are barriers to broader GenAI adoption. Ensure that data is cleansed, consistent, and centrally stored, ideally in a data lake. Low-cost proof-of-concepts can help you reduce the risk of overprovisioning.

Data Lake 142
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Analyzing the business-case approach Perdue Farms takes to derive value from data

CIO Business Intelligence

On the agribusiness side we source, purchase, and process agricultural commodities and offer a diverse portfolio of products including grains, soybean meal, blended feed ingredients, and top-quality oils for the food industry to add value to the commodities our customers desire. The data can also help us enrich our commodity products.

Data Lake 135
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Overcome these six data consumption challenges for a more data-driven enterprise

IBM Big Data Hub

Implementing the right data strategy spurs innovation and outstanding business outcomes by recognizing data as a critical asset that provides insights for better and more informed decision-making. By taking advantage of data, enterprises can shape business decisions, minimize risk for stakeholders, and gain competitive advantage.