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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

Like the proverbial man looking for his keys under the streetlight , when it comes to enterprise data, if you only look at where the light is already shining, you can end up missing a lot. If storage costs are escalating in a particular area, you may have found a good source of dark data. Storing data isn’t enough. Use people.

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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.

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Data Vault 2.0: What You Need to Know

Astera

With rising data volumes, dynamic modeling requirements, and the need for improved operational efficiency, enterprises must equip themselves with smart solutions for efficient data management and analysis. This is where Data Vault 2.0 It supersedes Data Vault 1.0, What is Data Vault 2.0? Data Vault 2.0