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February 14, 20260 citationsOpen Access

AI Economics: Data Quality Economics — The True Cost of Bad Data in Enterprise AI

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OIOleh Ivchenko

Key Points

  • The research aims to quantify the economic impact of poor data quality on enterprise AI initiatives.
  • Developed an economic framework for measuring data quality costs.
  • Analyzed fourteen years of software development experience.
  • Integrated seven years of AI research to contextualize findings.
  • Identified hidden cost multipliers related to data issues.
  • Bad data accounts for 60-73% of AI project failures.
  • Minor data quality issues can lead to multi-million dollar failures.
  • Economic framework offers insights into measuring the cost of substandard data.

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.

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Cite This Study

Oleh Ivchenko (2026) studied this question.

synapsesocial.com/papers/699011712ccff479cfe581adhttps://doi.org/10.5281/zenodo.18624306
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