Data silos continue to be a significant challenge for many teams, with 86% affected, prompting a blog series offering technical and cultural solutions, eventually compiled into an ebook. The series includes posts on creating a data dictionary, building a tech stack and data warehouse, and using reverse ETL. The final piece focuses on data integrity testing, which ensures data quality, accuracy, consistency, and completeness, addressing common pitfalls like insufficient test coverage and inadequate monitoring. The text outlines physical and logical data integrity types, emphasizes testing at various granularity levels, and highlights the importance of accuracy, conformity, consistency, and timeliness in maintaining clean data. Anomalies and data drift are identified as critical concerns, with statistical methods, machine learning algorithms, and visual inspections recommended for anomaly detection. The text provides a detailed framework for data integrity testing, including defining objectives, gathering requirements, designing test cases, choosing tools, integrating with data pipelines, and establishing review and correction processes, emphasizing the ongoing nature of maintaining data integrity to prevent data silos.