Remove Compliance Remove Data Engineering Remove Training
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The future of data: A 5-pillar approach to modern data management

CIO

This approach is repeatable, minimizes dependence on manual controls, harnesses technology and AI for data management and integrates seamlessly into the digital product development process. Operational errors because of manual management of data platforms can be extremely costly in the long run.

Data 167
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From legacy to lakehouse: Centralizing insurance data with Delta Lake

CIO

Features like time-travel allow you to review historical data for audits or compliance. Delta Lake: Fueling insurance AI Centralizing data and creating a Delta Lakehouse architecture significantly enhances AI model training and performance, yielding more accurate insights and predictive capabilities.

Insurance 164
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Why thinking like a tech company is essential for your business’s survival

CIO

We developed clear governance policies that outlined: How we define AI and generative AI in our business Principles for responsible AI use A structured governance process Compliance standards across different regions (because AI regulations vary significantly between Europe and U.S. Does their contract language reflect responsible AI use?

Company 186
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United Airlines’ AI strategy: The airline that makes decisions fastest wins

CIO

Uniteds methodical building of data infrastructure, compliance frameworks, and specialized talent demonstrates how traditional companies can develop true AI readiness that delivers measurable results for both customers and employees. We also built an organization skilled in the data engineering and data science required for AI.

Airlines 124
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When is data too clean to be useful for enterprise AI?

CIO

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

Data 211
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IT leaders rethink talent strategies to cope with AI skills crunch

CIO

Now, they’re racing to train workers fast enough to keep up with business demand. And they need people who can manage the emerging risks and compliance requirements associated with AI. He wants data scientists who can build, train, and validate models for use cases, and who can perform exploratory analysis and hypothesis testing.

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Beyond the hype: 4 use cases that show what’s actually working with gen AI

CIO

Part of it has to do with things like making sure were able to collect compliance requirements around AI, says Baker. Once you get Copilot for Office 365, you go through training, and thats driven up our utilization to around 93%. And then there are guardrail considerations. Were taking that part very slowly.