NITI Aayog’s Report on Improving India’s Data Quality

Ensuring Data Accuracy: Key Highlights from NITI Aayog’s Report

Exam Relevance: Governance GS Paper II (UPSC)

Why In News: NITI Aayog’s Report

NITI Aayog has released its report titled “India’s Data Imperative: The Pivot Towards Quality”, highlighting the urgent need to improve the quality of public data in India to ensure better governance and service delivery.

What is India’s Data Ecosystem?

India’s data ecosystem includes digital infrastructure, platforms, and databases that support governance, welfare schemes, and financial inclusion in both public and private sectors.

Key Examples of India’s Data Infrastructure:

Aadhaar: Over 27 billion authentications done in FY 2024–25; essential for identity-linked services.
UPI: ₹23.9 trillion worth of transactions per month; world’s largest real-time digital payment system.
Ayushman Bharat Digital Health IDs: 369 million IDs issued, improving health data integration.
Direct Benefit Transfer (DBT): ₹5.47 lakh crore transferred to beneficiaries in FY 2024–25 across 330+ welfare schemes.
Aadhaar e-KYC: 1.8 billion e-KYC verifications done, cutting onboarding costs.
Digital Penetration: 1.2 billion mobile subscribers and 800 million internet users — among the largest digital user bases globally.

Why India Needs Better Data Management

Stop Leakages in Welfare Schemes: Poor data quality results in wrong or duplicate beneficiaries, causing 4–7% extra spending.
Support Evidence-Based Policy: High-quality data is essential for AI-driven insights and targeted welfare schemes.
Enhance Public Trust: Citizens’ confidence in digital services depends on accurate and reliable data.
Strengthen AI Innovation: AI in healthcare, agriculture, and governance requires clean, validated data.
Improve Coordination Among Ministries: Reliable, shared data improves efficiency and alignment across departments.

Major Challenges in India’s Data Ecosystem

Fragmentation: Data is stored in silos; different ministries use incompatible systems.
Lack of Accountability: No dedicated authority to ensure data quality across departments.
Outdated Technology: Legacy IT systems delay real-time updates and hinder smooth data sharing.
Misaligned Incentives: Focus is on fast data entry, not accuracy, compromising data reliability.
Low Quality Culture: The belief that “80% accuracy is enough” affects overall data integrity.

Steps Suggested by NITI Aayog to Improve Data Quality

Appoint Data Custodians: Dedicated officers at national, state, and district levels to oversee data quality.
Link Data Quality to Incentives: Track error rates and integrate data quality into performance reviews and budgets.
Ensure Data Interoperability: Use national frameworks like IndEA (India Enterprise Architecture) and NDGFP (National Data Governance Framework Policy) to standardise data.
Use Assessment Tools: Implement NITI Aayog’s Data Quality Scorecard and Maturity Framework for regular checks.
Invest in Training: Train field staff and managers to prioritise accurate data collection and management.

Conclusion

High-quality data is crucial for effective governance, efficient welfare delivery, and building public trust. NITI Aayog’s recommendations aim to make India’s data ecosystem more reliable, interoperable, and accountable — ensuring better outcomes from the country’s vast digital infrastructure.