Context:
Government think tank NITI Aayog proposed institutionalising data ownership, incentivising data quality, and interoperability of data across platforms as measures to improve data quality in the country in view of Indians’ growing dependence on digital public infrastructure.
NITI Aayog Report
- Report Title: India’s Data Imperative: The Pivot Towards Quality (Released: 25 June 2025)
- Key Finding: Fiscal leakage and faulty/duplicate beneficiary records inflate welfare outlays by 4–7% annually.
- Core Concern: Poor data quality leads to mis-targeted schemes, delayed corrections, and erosion of public trust.
What is India’s Data Ecosystem?
- A digital infrastructure network powering governance, welfare schemes, and financial inclusion across sectors.
- Integrates platforms such as:
- Aadhaar – identity authentication
- UPI – real-time financial transactions
- Ayushman Bharat – health data interoperability
- DBT – subsidy and benefit transfers
- Aadhaar e-KYC – cost-effective user onboarding
Key Highlights
- Aadhaar Authentications: 27+ billion
- UPI Transactions: ₹23.9 trillion/month
- Ayushman Bharat IDs Issued: 369 million
- DBT Transfers: ₹5.47 lakh crore across 330+ schemes
- e-KYC Transactions: 1.8 billion
- Digital Penetration: 1.2 billion mobile users; 800 million internet users
Why a Robust Data Ecosystem is Essential
- Prevent Fiscal Leakage: Reduces duplicate beneficiaries and erroneous payouts (saves 4–7% of welfare budget).
- Enable Data-Driven Governance: Facilitates AI-backed decision-making and targeted delivery.
- Build Public Trust: Enhances confidence in digital governance.
- Strengthen AI Innovation: Clean data is foundational to AI progress in healthcare, agri-tech, and governance.
- Cross-Ministerial Coordination: Enables real-time, integrated service delivery across departments.
Key Challenges:
- Fragmentation: Siloed systems across ministries
- Lack of Ownership: No designated data custodians
- Legacy IT Systems: Hinders real-time data flow
- Incentive Mismatch: Quantity prioritized over accuracy
- Weak Data Culture: Tolerance for low-accuracy standards
Recommended Measures
- Institutional Ownership: Appoint national/state/district-level data custodians
- Quality Incentives: Link data accuracy with appraisals and budgets
- Interoperability: Use IndEA, NDGFP for format standardisation
- Tools for Improvement: Implement NITI Aayog’s Data Quality Scorecard
- Capacity Building: Train officials for better data governance
