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Facing a Big Data Blank Canvas: How CxOs Can Avoid Getting Lost in Data Modeling Concepts

Dataversity

This requires a strategic approach, in which CxOs should define business objectives, prioritize data quality, leverage technology, build a data-driven culture, collaborate with […] The post Facing a Big Data Blank Canvas: How CxOs Can Avoid Getting Lost in Data Modeling Concepts appeared first on DATAVERSITY.

Big Data 147
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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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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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Data Modeling for Quality

The Data Administration Newsletter

In this article, I describe a method of modelling data so that it meets business requirements. Central to this method is that modelling not only the required data, but also the subset of the real world that concerns the enterprise.

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Power of ETL: Transforming Business Decision Making with Data Insights

Smart Data Collective

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses. Both approaches aim to improve data quality and enable accurate analysis.

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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.