Remove Data Analytics Remove Data Governance Remove Data Transformation Remove Metadata
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As insurers look to be more agile, data mesh strategies take centerstage

CIO Business Intelligence

The human political element plays a significant role here as well, as local data owners push back on relinquishing control over domain-specific data assets to centralized data governance authorities. These domain data leaders often cite the diminishing returns and significant effort of central data team engagement.

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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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Tackling AI’s data challenges with IBM databases on AWS

IBM Big Data Hub

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape.

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Why The Public Sector Needs Data Governance

Alation

What Is Data Governance In The Public Sector? Effective data governance for the public sector enables entities to ensure data quality, enhance security, protect privacy, and meet compliance requirements. With so much focus on compliance, democratizing data for self-service analytics can present a challenge.

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“You Complete Me,” said Data Lineage to DataOps Observability.

DataKitchen

Data lineage can also be used for compliance, auditing, and data governance purposes. DataOps Observability Five on data lineage: Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. What is missing in data lineage?

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Data Mesh 101: How Data Mesh Helps Organizations Be Data-Driven and Achieve Velocity

Ontotext

For many organizations, a centralized data platform will fall short as it gives data teams much less autonomy over managing increasingly diverse and voluminous datasets. It embraces the reality of business and specialized data needs within teams and organizations.

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

To ingest the data, smava uses a set of popular third-party customer data platforms complemented by custom scripts. After the data lands in Amazon S3, smava uses the AWS Glue Data Catalog and crawlers to automatically catalog the available data, capture the metadata, and provide an interface that allows querying all data assets.