Overview

• Poor data quality undermines public trust, hinders service delivery and jeopardizes India’s AI ecosystem.
• Even a 5% error rate can leave 5 million welfare beneficiaries without payments and distort policy decisions.
• Niti Aayog CEO BVR Subrahmanyam advocates cutting error rates to 0.0001% to match Japanese precision and avoid exclusions.
• The Ministry of Statistics and Programme Implementation has convened meetings with 30 ministries to standardize data formats and improve interoperability.
• Data stewards should be appointed and automated validation checks implemented to prevent errors in high-value government datasets.