This ebook provides practical guidance for detecting, validating, and interpreting anomalies in data. It outlines ten actionable tips that help analysts avoid common pitfalls such as mistaking noise for meaningful signals or overlooking contextual factors. Topics include data preparation, visual exploration, statistical validation, and combining multiple detection methods for higher confidence. The ebook highlights how domain knowledge is essential for distinguishing true anomalies from normal variation. By integrating visual analytics with advanced detection techniques, organizations can uncover risks, inefficiencies, and opportunities earlier while improving trust in analytical outcomes.

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