Korean Certified Information Manager Yang Hee-eun: AI Projects Fail More from Unusable Data than Dirty Data
Based on her hands-on experience performing pseudonymization for public institutions and companies in South Korea, Certified Information Manager Yang Hee-eun argues that AI projects stall not because data is dirty but because it is unusable — legally restricted, unable to leave the institution, or invisible in unstructured formats. She advocates treating privacy governance as a traffic light rather than a brake, selecting pseudonymization, anonymization, or synthetic data pathways according to the intended use.
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"The data is a mess." When an AI project falls short of expectations, this is the most common explanation. Yang Hee-eun, a Korean Certified Information Manager, acknowledges in her ZDNet Korea column that this diagnosis is not wrong: Gartner predicts that 60% of AI projects lacking "AI-ready" data will be abandoned by 2026, and 63% of organizations either have not established a data management system suited to AI or are not even sure whether they have one. Many companies accordingly pour budgets into cleansing, standardization, and quality control.
But Yang Hee-eun points out that the failures she encountered while personally performing data pseudonymization for public institutions and companies looked entirely different. "Projects stopped not because the data was dirty, but because it could not be used." Cleaned data sat right there, yet could not enter training because it contained personal information; or problems only surfaced after deployment, forcing services to be taken offline.
She distinguishes two commonly conflated standards: data quality (accuracy, completeness, consistency) and data usability (whether this particular use is permitted). Consider anomaly-detection modeling: if you erase "outliers" as noise, quality metrics improve but the model loses the very signal it was supposed to capture. Quality is not an intrinsic property of data but is relative to purpose. Going further, even if every quality metric passes, if the law does not permit that use, the data might as well not exist for AI.