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Improve Data Governance with These Data Modeling Tips

The Data Administration Newsletter

Data Governance describes the practices and processes organizations use to manage the access, use, quality and security of an organizations data assets. The data-driven business era has seen a rapid rise in the value of organization’s data resources.

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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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Who Needs A Data Model Anyway?

The Data Administration Newsletter

These days, there is much conversation about the necessity of the data model. The data model has been around for several decades now and can be classified as an artifact of an earlier day and age. But is the data model really out of date? And exactly why do we need a data model, anyway? […]

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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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Empowering Your Data Science: Introducing Domo’s Amazon Bedrock ML Connector

Domo

Bridging the Gap: Data Science and Business Decisions AI’s real value comes from its day-to-day applications in your business. The Amazon Bedrock ML Connector does exactly that—bridging the gap between intricate data models and daily business decision-making. Ensuring data governance and security.

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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.