This presentation examines the key practices for effective data quality management, and will provide a practical roadmap to enhance, or implement, data quality practices. Addressing data quality management is essential for organizations to ensure the success and reliability of next generation technology (AI, LLMs, etc.) implementations and this talk will provide valuable insights applicable from the C-Suite to the data enthusiast.

I will first analyze the strategic definition of data quality standards, emphasizing various principles. The importance of proactive data profiling, coupled with regular assessments, is then highlighted for identifying and addressing anomalies. The session also reinforces the significance of a robust data governance framework, automated validation processes, and metadata management to ensure contextual insights.

Continuous monitoring, supported by alert systems, is discussed for tracking data quality metrics in real-time. Cultivating a data quality culture through user training and awareness is suggested as a vital component. The presentation also explores the utilization of data quality metrics and key performance indicators to benchmark and assess effectiveness. The commitment to continuous improvement concludes the discussion, emphasizing the importance of regularly reviewing and adapting practices to evolving business needs.

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