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Why Data Quality is the Secret Ingredient to AI Success

insideBIGDATA

In this contributed article, engineering leader Uma Uppin emphasizes that high-quality data is fundamental to effective AI systems, as poor data quality leads to unreliable and potentially costly model outcomes.

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The Art of Lean Governance: A Systems Thinking Approach to Data Governance

The Data Administration Newsletter

A systems thinking approach to process control and optimization demands continual data quality feedback loops. Moving the quality checks upstream to the source system provides the most extensive control coverage. Data Governance is about gaining trust and […]

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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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Master Data Governance in a Multi-Cloud Environment

Smart Data Collective

Mastering data governance in a multi-cloud environment is key! Delve into best practices for seamless integration, compliance, and data quality management.

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Difference between modern and traditional data quality - DataScienceCentral.com

Flipboard

Modern data quality practices leverage advanced technologies, automation, and machine learning to handle diverse data sources, ensure real-time processing, and foster collaboration across stakeholders.

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AI Success – Powered by Data Governance and Quality

Precisely

Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust data governance for AI ensures data privacy, compliance, and ethical AI use. Proactive data quality measures are critical, especially in AI applications.

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Data Integrity vs. Data Quality: How Are They Different?

Precisely

When companies work with data that is untrustworthy for any reason, it can result in incorrect insights, skewed analysis, and reckless recommendations to become data integrity vs data quality. Two terms can be used to describe the condition of data: data integrity and data quality.