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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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Data duet: Why business analyst and data scientist make a great match

Analysts Corner

If the same data is available in several applications, the business analyst will know which is themaster. Data quality Poor data quality can have consequences for the result of the analysis. In our case we prioritised using data from the services that members use themost.

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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.

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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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Power of ETL: Transforming Business Decision Making with Data Insights

Smart Data Collective

By harmonising and standardising data through ETL, businesses can eliminate inconsistencies and achieve a single version of truth for analysis. Improved Data Quality Data quality is paramount when it comes to making accurate business decisions.

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Harrods’ Data Analytics Transformation: Turning Challenges into Insights

Timo Elliott

Performance and Data Quality Issues: Transitioning to live connections in the new environment revealed gaps in the data models and performance challenges. Parallel Systems: Adoption issues forced the team to run legacy platforms alongside the new system, adding complexity.

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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.