Answers are grounded in this paper and cite their sources.
Analysis reveals hidden costs of poor data quality in enterprise AI initiatives, suggesting substantial financial implications.
Data quality stands as the silent executioner of enterprise AI initiatives, responsible for an estimated 60-73% of AI project failures. This article presents a comprehensive economic framework for understanding, measuring, and mitigating the costs of substandard data in AI systems. Drawing on fourteen years of enterprise software development and seven years of AI research, I examine the hidden cost multipliers that transform minor data quality issues into multi-million dollar failures.
No takes yet. Share an insight, caveat, or question.
Oleh Ivchenko (2026) studied this question.
Synapse has enriched 8 closely related papers on similar clinical questions. Consider them for comparative context: