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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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Get out of the data swamp with a governed data lake

IBM Big Data Hub

Making your data lake a “governed data lake” is the game changer. Without governance, organizations risk securing the data and as well as protecting it. A governed data lake contains data that’s accessible, clean, trusted and protected.

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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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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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Data Lakes: What Are They and Who Needs Them?

Jet Global

To address the flood of data and the needs of enterprise businesses to store, sort, and analyze that data, a new storage solution has evolved: the data lake. What’s in a Data Lake? Data warehouses do a great job of standardizing data from disparate sources for analysis. Taking a Dip.

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Data governance in the age of generative AI

AWS Big Data

To provide a response that includes the enterprise context, each user prompt needs to be augmented with a combination of insights from structured data from the data warehouse and unstructured data from the enterprise data lake. Implement data privacy policies. Implement data quality by data type and source.

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Data Mesh 101: How Data Mesh Can Be Used in an Organization

Ontotext

Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage. Establish and enforce data governance by ensuring all data used is accurate, complete, and compliant with regulations. For instance, JPMorgan Chase & Co.