Remove Data Processing Remove Data Quality Remove Metadata
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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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Introducing erwin Data Intelligence 14: Dive into data quality, ensure data reliability and leverage new deployment flexibility

erwin

Added data quality capability ready for an AI era Data quality has never been more important than as we head into this next AI-focused era. erwin Data Quality is the data quality heart of erwin Data Intelligence. erwin Data Quality is the data quality heart of erwin Data Intelligence.

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How Volkswagen Autoeuropa built a data mesh to accelerate digital transformation using Amazon DataZone

AWS Big Data

In this post, we discuss how Volkswagen Autoeuropa used Amazon DataZone to build a data marketplace based on data mesh architecture to accelerate their digital transformation. Data quality issues – Because the data was processed redundantly and shared multiple times, there was no guarantee of or control over the quality of the data.

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Governing data in relational databases using Amazon DataZone

AWS Big Data

As you experience the benefits of consolidating your data governance strategy on top of Amazon DataZone, you may want to extend its coverage to new, diverse data repositories (either self-managed or as managed services) including relational databases, third-party data warehouses, analytic platforms and more.

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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

For the past 5 years, BMS has used a custom framework called Enterprise Data Lake Services (EDLS) to create ETL jobs for business users. BMS’s EDLS platform hosts over 5,000 jobs and is growing at 15% YoY (year over year). It retrieves the specified files and available metadata to show on the UI.

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The importance of data ingestion and integration for enterprise AI

IBM Big Data Hub

Data ingestion must be done properly from the start, as mishandling it can lead to a host of new issues. The groundwork of training data in an AI model is comparable to piloting an airplane. The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions.

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Gartner Data & Analytics Summit 2022 in London: 3 Key Takeaways

Alation

Establish what data you have. Active metadata gives you crucial context around what data you have and how to use it wisely. Active metadata provides the who, what, where, and when of a given asset, showing you where it flows through your pipeline, how that data is used, and who uses it most often.