Factors Contributing to the Data Quality Crisis
Several key factors are driving this decline in data quality:
Increased Model Complexity: Modern AI models, particularly generative AI systems, require more sophisticated and nuanced data than their predecessors.
Frequent Model Updates: With 86% of companies updating their models at least quarterly, maintaining consistent data quality has become increasingly challenging.
Scale of Data Requirements: The sheer volume of data needed for modern AI systems makes quality control more difficult.
Specialized Annotation Requirements: Advanced AI models often require more complex and precise data annotations, increasing the potential for errors.