The data factory metaphor highlights how every step in data handling—transportation, copying, and manipulation—presents opportunities for error, leading to data corruption despite intentions of enhancement.
Issues arise from various factors including poor quality inputs, inadequate descriptions, software bugs, and improper sequencing, all of which can turn good data bad.
Human factors also contribute significantly, as changes in data handling by people, whether through new features, bug fixes, refactors, or optimizations, can inadvertently introduce errors that degrade data quality.
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