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

AWS Big Data

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

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8 data strategy mistakes to avoid

CIO Business Intelligence

Using unstructured data for actionable insights will be a crucial task for IT leaders looking to drive innovation and create additional business value.” One of the keys to benefiting from unstructured data is to define clear objectives, Miller says. What are the goals for leveraging unstructured data?”

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The What & Why of Data Governance

erwin

Led by Frank Pörschmann of iDIGMA GmbH, an IT industry veteran and data governance strategist, it examined “ The What & Why of Data Governance.”. The What: Data Governance Defined. Data governance has no standard definition. The Why: Data Governance Drivers. The Benefits of erwin Data Intelligence.

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The Journey to DataOps Success: Key Takeaways from Transformation Trailblazers

DataKitchen

At Workiva, they recognized that they are only as good as their data, so they centered their initial DataOps efforts around lowering errors. Hodges commented, “Our first focus was to up our game around data quality and lowering errors in production. GSK’s DataOps journey paralleled their data transformation journey.

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Database vs. Data Warehouse: What’s the Difference?

Jet Global

Whether the reporting is being done by an end user, a data science team, or an AI algorithm, the future of your business depends on your ability to use data to drive better quality for your customers at a lower cost. All About That (Data)Base. Enhancing a Data Warehouse with Cubes.

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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.