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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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What is data governance? Best practices for managing data assets

CIO Business Intelligence

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

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The Missing Link in Enterprise Data Governance: Metadata

Octopai

In order to figure out why the numbers in the two reports didn’t match, Steve needed to understand everything about the data that made up those reports – when the report was created, who created it, any changes made to it, which system it was created in, etc. Enterprise data governance. Metadata in data governance.

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The Rising Need for Data Governance in Healthcare

Alation

This data is also a lucrative target for cyber criminals. Healthcare leaders face a quandary: how to use data to support innovation in a way that’s secure and compliant? Data governance in healthcare has emerged as a solution to these challenges. Uncover intelligence from data. Protect data at the source.

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8 data strategy mistakes to avoid

CIO Business Intelligence

“IT leaders should establish a process for continuous monitoring and improvement to ensure that insights remain actionable and relevant, by implementing regular review cycles to assess the effectiveness of the insights derived from unstructured data.” This type of environment can also be deeply rewarding for data and analytics professionals.”

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The Enduring Significance of Data Modeling in the Modern Data-Driven Enterprise

erwin

Let’s explore the continued relevance of data modeling and its journey through history, challenges faced, adaptations made, and its pivotal role in the new age of data platforms, AI, and democratized data access. Over time, it evolved from rudimentary diagrams to sophisticated methodologies.

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Navigating the Data Mesh Paradigm: Opportunities, Challenges, and the Path Forward

Data Virtualization

Reading Time: 5 minutes The data landscape has become more complex, as organizations recognize the need to leverage data and analytics for a competitive edge. Companies are collecting traditional structured data as well as text, machine-generated data, semistructured data, geospatial data, and more.