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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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Saving Data Costs with Data Lineage

Octopai

How can you save your organizational data management and hosting cost using automated data lineage. Do you think you did everything already to save organizational data management costs? What kind of costs organization has that data lineage can help with? Well, you probably haven’t done this yet!

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Accomplish Agile Business Intelligence & Analytics For Your Business

datapine

Working software over comprehensive documentation. The agile BI implementation methodology starts with light documentation: you don’t have to heavily map this out. You need to determine if you are going with an on-premise or cloud-hosted strategy. Finalize documentation, where necessary. Document only when necessary.

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The Value of Data Governance and How to Quantify It

erwin

erwin recently hosted the second in its six-part webinar series on the practice of data governance and how to proactively deal with its complexities. Led by Frank Pörschmann of iDIGMA GmbH, an IT industry veteran and data governance strategist, the second webinar focused on “ The Value of Data Governance & How to Quantify It.”.

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A summary of Gartner’s recent DataOps-driven data engineering best practices article

DataKitchen

Overview of Gartner’s data engineering enhancements article To set the stage for Gartner’s recommendations, let’s give an example of a new Data Engineering Manager, Marcus, who faces a whole host of challenges to succeed in his new role: Marcus has a problem. Summary: 10x your data engineering game.

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What is Data Mapping?

Jet Global

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

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How Dafiti made Amazon QuickSight its primary data visualization tool

AWS Big Data

Robust documentation – The material provided by AWS proved to be helpful, allowing our team to put the project into production. Unifying our solution We were previously using Qlikview, Sisense, Tableau, SAP, and Excel to analyze our data across different teams. Conclusion Choosing a data visualization tool is not a simple task.