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Data Classification Can Make or Break Data Governance

Dataversity

The news these days is full of massive fines levied against companies that failed to protect their customer data. An effective data classification approach is one of the best ways to ensure that companies can identify and protect their most valuable data. Our experience continues to […].

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Enhancing Data Fabric with SQL Asset Type in IBM Knowledge Catalog

IBM Data Science in Practice

Metadata Enrichment: Empowering Data Governance Data Quality Tab from Metadata Enrichment Metadata enrichment is a crucial aspect of data governance, enabling organizations to enhance the quality and context of their data assets. More on metadata enrichment can be read here.

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The Role of IT in Data Governance

The Data Administration Newsletter

Through advanced technologies and tools, IT ensures that data is securely stored, backed up, and accessible to authorized personnel. IT also enforces data governance policies and procedures, such as data classification and access […]

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

Align your data strategy to a go-forward architecture, with considerations for existing technology investments, governance and autonomous management built in. Look to AI to help automate tasks such as data onboarding, data classification, organization and tagging.

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Why Your Master Data Management Needs Data Governance

Precisely

An MDM consolidates important domain data into unique or linked instances (e.g. a “golden” record) and then uses that unique record as a reference point for aggregating associated data, purging duplicates, standardizing data across various applications, and creating rules to continuously resolve, merge, or disassociate records.

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Data governance 101: Building a strong foundation for your organization

Dataconomy

It involves the creation of rules for collecting, storing, processing, and sharing data to ensure its accuracy, completeness, consistency, and security. Some key concepts related to data governance include: Data quality:  Ensuring that data is accurate, complete, and consistent.

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Data governance 101: Building a strong foundation for your organization

Dataconomy

It involves the creation of rules for collecting, storing, processing, and sharing data to ensure its accuracy, completeness, consistency, and security. Some key concepts related to data governance include: Data quality:  Ensuring that data is accurate, complete, and consistent.