Remove Data Modelling Remove Data Quality Remove Government
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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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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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Putting the Business Back Into Business Innovation

Timo Elliott

You lose the roots: the metadata, the hierarchies, the security, the business context of the data. It’s possible, but you have to recreate all that from scratch in the new environment, and that takes time and effort, and hugely increases the possibility of data quality and other governance problems. Business Content.

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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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Harrods’ Data Analytics Transformation: Turning Challenges into Insights

Timo Elliott

The challenges were daunting: Siloed Data: Data was fragmented across 18 different SQL servers and multiple other platforms, with no unified system. Lack of Granular Data: Critical business processes werent being captured at the level of detail needed for meaningful analysis.

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What are the Challenges of Applying Machine Learning in Economics and Business?

Analysts Corner

Additionally, machine learning models in these fields must balance interpretability with predictive power, as transparency is crucial for decision-making. This section explores four main challenges: data quality, interpretability, generalizability, and ethical considerations, and discusses strategies for addressing each issue.

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

Tableau

If we asked you, “What does your organization need to help more employees be data-driven?” where would “better data governance” land on your list? We’re all trying to use more data to make decisions, but constantly face roadblocks and trust issues related to data governance. . A data governance framework.