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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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Data Governance in a Data Mesh or Data Fabric Architecture

Data Virtualization

Reading Time: 2 minutes Data mesh is a modern, distributed data architecture in which different domain based data products are owned by different groups within an organization. And data fabric is a self-service data layer that is supported in an orchestrated fashion to serve.

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How to Simplify Your Approach to Data Governance

Data Virtualization

Reading Time: 6 minutes Data Governance as a concept and practice has been around for as long as data management has been around. It, however is gaining prominence and interest in recent years due to the increasing volume of data that needs to be.

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Denodo recognized as a Leader in the 2023 Gartner® Magic Quadrant™ for Data Integration Report 

Data Virtualization

Reading Time: 3 minutes Denodo was recognized as a Leader in the 2023 Gartner® Magic Quadrant™ for Data Integration report, marking the fourth year in a row that Denodo has been recognized as such. I want to highlight the first of three strategic planning.

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Data Governance and Security: The Champions of IT Transformation in the Public Sector

Data Virtualization

Reading Time: 4 minutes Join our discussion on All Things Data with Fred Baradari, Federal Partner and Channel Sales Director at Denodo, with a focus on how Data Governance and Security are the real champions in bringing IT transformation. Listen to “The Role of.

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How companies are building sustainable AI and ML initiatives

O'Reilly on Data

In other words, could we see a roadmap for transitioning from legacy cases (perhaps some business intelligence) toward data science practices, and from there into the tooling required for more substantial AI adoption? Data scientists and data engineers are in demand.

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Artificial intelligence and machine learning adoption in European enterprise

O'Reilly on Data

Our survey showed that companies are beginning to build some of the foundational pieces needed to sustain ML and AI within their organizations: Solutions, including those for data governance, data lineage management, data integration and ETL, need to integrate with existing big data technologies used within companies.