This ebook introduces a modern, predictive approach to data quality and observability, emphasizing that poor data quality leads to revenue loss, operational risk, and compliance failures. It explains why traditional, reactive data quality methods do not scale in modern data environments. The ebook presents a five-step framework for predictive data quality, including connecting data, gaining awareness, automating controls, defining conditions, and taking action. It highlights the role of machine learning in detecting issues early, reducing manual effort, and improving trust. The document concludes that integrating data quality, governance, catalogs, and lineage creates a unified foundation for reliable, trusted data.

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