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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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5 Ways Data Modeling Is Critical to Data Governance

erwin

Then there’s unstructured data with no contextual framework to govern data flows across the enterprise not to mention time-consuming manual data preparation and limited views of data lineage. So here’s why data modeling is so critical to data governance.

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Doing Cloud Migration and Data Governance Right the First Time

erwin

That means your cloud data assets must be available for use by the right people for the right purposes to maximize their security, quality and value. Why You Need Cloud Data Governance. Regulatory compliance is also a major driver of data governance (e.g., GDPR, CCPA, HIPAA, SOX, PIC DSS).

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How to Build a Successful Metadata Management Framework

Alation

This is where metadata, or the data about data, comes into play. Having a data catalog is the cornerstone of your data governance strategy, but what supports your data catalog? Your metadata management framework provides the underlying structure that makes your data accessible and manageable.

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Don’t Fear Artificial Intelligence; Embrace it Through Data Governance

CIO Business Intelligence

Data-centric AI is evolving, and should include relevant data management disciplines, techniques, and skills, such as data quality, data integration, and data governance, which are foundational capabilities for scaling AI. Further, data management activities don’t end once the AI model has been developed.

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5 Ways Data Engineers Can Support Data Governance

Alation

These data requirements could be satisfied with a strong data governance strategy. Governance can — and should — be the responsibility of every data user, though how that’s achieved will depend on the role within the organization. Low quality In many scenarios, there is no one responsible for data administration.

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Do I Need a Data Catalog?

erwin

It’s no surprise that most organizations’ data is often fragmented and siloed across numerous sources (e.g., legacy systems, data warehouses, flat files stored on individual desktops and laptops, and modern, cloud-based repositories.). This also diminishes the value of data as an asset. Technical Metadata.

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