<b>Principles of Data Quality and Their Role in Building Effective Artificial Intelligence Systems</b>

In the intersecting fields of statistics and artificial intelligence (AI), ensuring data quality is crucial for deriving accurate and actionable insights.This short paper examines the core principles that underpin the quality of statistical data (relevance, accuracy and reliability, timeliness and punctuality, and comparability and consistency) and their critical role in developing effective AI systems. By integrating these principles, we can enhance the robustness and utility of AI models.The discussion highlights how relevant data must align with specific problems, how accuracy and reliability affect model performance, and the importance of timely updates and consistency across contexts.Strategies for incorporating these principles into AI system design and management are explored, emphasizing data selection, ongoing maintenance, and rigorous monitoring.Ultimately, the article underscores the necessity of adhering to these quality principles to achieve precise, timely, and relevant outcomes, thereby supporting informed and effective decision-making in both statistical and AI applications.