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generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and DataGovernance application.
DataObservability and Data Quality are two key aspects of data management. The focus of this blog is going to be on DataObservability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.
This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Data quality monitoring Maintaining good data quality requires continuous data quality management.
And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data. Protected and compliant data.
To achieve true data integrity, organizations must attend to data integration, datagovernance, data quality, and context with data enrichment. Dataanalysts also use contextual data enrichment to help monitor the spread of infectious diseases. Competitive disadvantage.
In its essence, data mesh helps with dataobservability — another important element every organization should consider. With granular access controls, data lineage, and domain-specific audit logs, data catalogs allow engineers and developers to have a better view of their systems than before.
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