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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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Measure performance of AWS Glue Data Quality for ETL pipelines

AWS Big Data

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

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Get started with AWS Glue Data Quality dynamic rules for ETL pipelines

AWS Big Data

They establish data quality rules to ensure the extracted data is of high quality for accurate business decisions. These rules assess the data based on fixed criteria reflecting current business states. We are excited to talk about how to use dynamic rules , a new capability of AWS Glue Data Quality.

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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Alation Launches Open Data Quality Framework

Alation

In a sea of questionable data, how do you know what to trust? Data quality tells you the answer. It signals what data is trustworthy, reliable, and safe to use. It empowers engineers to oversee data pipelines that deliver trusted data to the wider organization. Today, as part of its 2022.2

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How IBM HR leverages IBM Watson® Knowledge Catalog to improve data quality and deliver superior talent insights

IBM Big Data Hub

People analytics is at the center of human resources (HR) strategy and planning. Companies rely heavily on data and analytics to find and retain talent, drive engagement, improve productivity and more across enterprise talent management. According to a Gartner report , poor data quality costs organizations an average of USD 12.9