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What Every Business Leader Needs to Know About Data Modeling

Dataversity

But decisions made without proper data foundations, such as well-constructed and updated data models, can lead to potentially disastrous results. For example, the Imperial College London epidemiology data model was used by the U.K. Government in 2020 […].

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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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Testing and Monitoring Data Pipelines: Part Two

Dataversity

In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.

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Data is Risky Business: Data is a Communications Skill

The Data Administration Newsletter

Over the past few months, my team in Castlebridge and I have been working with clients delivering training to business and IT teams on data management skills like data governance, data quality management, data modelling, and metadata management.

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Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed?

ElegantJ BI

One of the crucial success factors for advanced analytics is to ensure that your data is clean and clear and that your users have a good understanding of the source of the data so that they can put results in perspective.

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Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed?

ElegantJ BI

One of the crucial success factors for advanced analytics is to ensure that your data is clean and clear and that your users have a good understanding of the source of the data so that they can put results in perspective.

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Advanced Analytics: If You Don’t Know What You Need, How Can You Succeed?

ElegantJ BI

One of the crucial success factors for advanced analytics is to ensure that your data is clean and clear and that your users have a good understanding of the source of the data so that they can put results in perspective. Data Governance and Self-Serve Analytics Go Hand in Hand.