The article highlights the issues with traditional backfilling methods for Slowly Changing Dimensions (SCD) in data systems. The author recounts a personal experience where a customer dimension needed to be backfilled after a source system migration. The previous team's approach involved running daily incremental processes for each day over three years, resulting in 47,000 records with overlapping date ranges and thousands of gaps. This compounded error made the data unfixable by conventional means. Instead of attempting to patch the existing data, the author developed the concept of Healing Tables, which can rebuild dimensions from source data at any time, effectively "healing" inconsistencies. The Healing Tables framework functions by separating change detection from period construction. Traditional methods tightly couple these processes, making them fragile and prone to errors. In contrast, Healing Tables follow a six-step pipeline that operates entirely on source data. This includes creating an Effectivity Table to document change points, generating time slices for valid periods, and applying hash computations for efficient change detection. By structuring the rebuild process to handle source data directly, the framework avoids the pitfalls of incremental updates and allows for a more reliable data quality management approach. The article emphasizes that by designing systems with rebuilds in mind, data engineers can create more resilient and accurate data architectures.