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The research aims to quantify the economic impact of poor data quality on enterprise AI initiatives.

February 14, 2026Open Access

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

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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Oleh Ivchenko (Thu,) studied this question.

www.synapsesocial.com/papers/699011712ccff479cfe581ad — DOI: https://doi.org/10.5281/zenodo.18624306

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

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