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Automated Data Governance 101

KDnuggets

The way we control our data isn’t working. Data is as vulnerable as ever. Download this white paper, which outlines lessons about how data science and governance programs can, if implemented properly, reinforce each other’s objective.

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Following the Roadmap to Better Data Governance

Precisely

Data governance is no trivial undertaking. When executed correctly, data governance transitions businesses from guesswork to data-informed strategies. For those who follow the right roadmap on their data governance journey, the payoff can be enormous.

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Why Your Master Data Management Needs Data Governance

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Master Data Management systems (MDM) play an important role in harmonizing data assets across large and midsize enterprises. However, to get optimal value from your organization’s data, you need to apply the discipline of data governance to your MDM. How can they contribute their expertise?

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Responsible AI is a competitive advantage

IBM Journey to AI blog

A responsible AI strategy is at the core of this approach: The board’s white paper, “ Foundation models: Opportunities, risks and mitigations ,” illustrates that foundation models show substantial improvements in their ability to tackle challenging and intricate problems.

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Unraveling the Threads: Data Fabric vs Data Mesh for Modern Enterprises

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Both architectures tackle significant data management challenges such as integrating disparate data sources, improving data accessibility, automating management processes, and ensuring data governance and security. Problems it solves Data fabric addresses key data management and use challenges.

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How to Build a Meshy Data Fabric (With a Data Catalog!)

Alation

This white paper makes this information actionable with a methodology, so you can learn how to implement a meshy fabric with your data catalog. For the full story, download the white paper here ! Tip 3: Never, ever skip data governance. Data governance isn’t exactly a trip to Disneyland.

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Insurance Organizations Depend on the Quality of Their Data

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For others, inaccurate data led to poor quality decisions, conflicting reports, mispriced policies, delays in claims processing, and other significant problems. Read Building Data Quality at Scale Developing and maintaining data quality at scale requires a systematic approach, supported by the right enterprise-grade technology tools.