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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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Dark Data: How to Find It and What to Do with It

Timo Elliott

If storage costs are escalating in a particular area, you may have found a good source of dark data. If you’ve been properly managing your metadata as part of a broader data governance policy, you can use metadata management explorers to reveal silos of dark data in your landscape. Storing data isn’t enough.

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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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Top 20 Data Warehouse Best Practices in 2024

Astera

These systems can be part of the company’s internal workings or external players, each with its own unique data models and formats. ETL (Extract, Transform, Load) process : The ETL process extracts data from source systems to transform it into a standardized and consistent format, and then delivers it to the data warehouse.

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Data Governance Framework: What is it? Importance, Pillars and Best Practices

Astera

What is a Data Governance Framework? A data governance framework is a structured way of managing and controlling the use of data in an organization. It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards.

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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.