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

Data Pine

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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Connecting the Three Spheres of Data Management to Unlock Value

Dataversity

Many organizations have mapped out the systems and applications of their data landscape. Many have documented their most critical business processes. Many have modeled their data domains and key attributes. But only very few have succeeded in connecting the knowledge of these three efforts.

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7 Data Quality Metrics to Assess Your Data Health

Astera

To do so, they need data quality metrics relevant to their specific needs. Organizations use data quality metrics, also called data quality measurement metrics, to assess the different aspects, or dimensions, of data quality within a data system and measure the data quality against predefined standards and requirements.

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The Essential Guide to ETL Developer Skills, Roles, and Responsibilities

Analysts Corner

Python, Java, C#) Familiarity with data modeling and data warehousing concepts Understanding of data quality and data governance principles Experience with big data platforms and technologies (e.g., Oracle, SQL Server, MySQL) Experience with ETL tools and technologies (e.g.,

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How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

Low data discoverability: For example, Sales doesn’t know what data Marketing even has available, or vice versa—or the team simply can’t find the data when they need it. . Unclear change management process: There’s little or no formality around what happens when a data source changes. Now, data quality matters.

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None Shall Pass! Are Your Database Standards Too Rigid?

The Data Administration Newsletter

Database standards are common practices and procedures that are documented and […]. Rigidly adhering to a standard, any standard, without being reasonable and using your ability to think through changing situations and circumstances is itself a bad standard.

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How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

Low data discoverability: For example, Sales doesn’t know what data Marketing even has available, or vice versa—or the team simply can’t find the data when they need it. . Unclear change management process: There’s little or no formality around what happens when a data source changes. Now, data quality matters.