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8 strategies for accelerating IT modernization

CIO Business Intelligence

Those principles are data centric, platform first, cloud based, automation led, and zero trust (so that everything is secure from the start). Having this framework that outlines the principles allows us not to get bogged down in the process but to remain focused on making principle-driven decisions.”

Strategy 140
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The art and science of data product portfolio management

AWS Big Data

This post is the first in a series dedicated to the art and science of practical data mesh implementation (for an overview of data mesh, read the original whitepaper The data mesh shift ). Taken together, the posts in this series lay out some possible operating models for data mesh within an organization.

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What’s the Difference: Quantitative vs Qualitative Data

Alation

Companies collect and analyze vast amounts of data to make informed business decisions. From product development to customer satisfaction, nearly every aspect of a business uses data and analytics to measure success and define strategies. When choosing between qualitative and quantitative data, think about what you want to learn.

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Beyond the hype: Key components of an effective AI policy

CIO Business Intelligence

While this leads to efficiency, it also raises questions about transparency and data usage. Data governance Strong data governance is the foundation of any successful AI strategy. This includes regular audits to guarantee data quality and security throughout the AI lifecycle.

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Security is dead: Long live risk management

CIO Business Intelligence

Information risk management is no longer a checkpoint at the end of development but must be woven throughout the entire software delivery lifecycle. They demand a reimagining of how we integrate security and compliance into every stage of software delivery.