AI Economics: Data Quality Economics — The True Cost of Bad Data in Enterprise AI
Abstract
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.
Fields of Study & Subjects
Data Quality and Management
Impact of AI and Big Data on Business and Society
Artificial Intelligence Applications
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 12 February 2026
- Language
- EN
- Document type
- Preprint