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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions

AWS Big Data

Today, we are pleased to announce that Amazon DataZone is now able to present data quality information for data assets. Other organizations monitor the quality of their data through third-party solutions. Additionally, Amazon DataZone now offers APIs for importing data quality scores from external systems.

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Data governance in the age of generative AI

AWS Big Data

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. Implement data privacy policies. Implement data quality by data type and source.

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Erwin Data Intelligence: A Data Partner’s Perspective

erwin

While the essence of success in data governance is people and not technology, having the right tools at your fingertips is crucial. Technology is an enabler, and for data governance this is essentially having an excellent metadata management tool. Next to data governance, data architecture is really embedded in our DNA.

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Data Lineage Through the Decades: Where It’s Going (And Where It’s Been)

Alation

The product collected an impressive amount of metadata, from the user interface to the database structure. It then translated all that metadata into an image resembling a spider’s web. Back then, visualizing impact analysis seemed futuristic with great promise. That was my earliest taste of data lineage.

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Top 5 Data Catalog Benefits: Understanding Your Organization’s Data Lineage

erwin

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

Sources Data can be loaded from multiple sources, such as systems of record, data generated from applications, operational data stores, enterprise-wide reference data and metadata, data from vendors and partners, machine-generated data, social sources, and web sources.