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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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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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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.

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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.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.