New Delhi: India’s journey to becoming a global leader in digital public infrastructure has been nothing short of remarkable. Platforms like Unified Payments Interface (UPI), Aadhaar, and Ayushman Bharat have transformed financial inclusion, identity verification, and healthcare access for millions. However, a new report by NITI Aayog, titled India’s Data Imperative: The Pivot Towards Quality, released on June 24, 2025, emphasizes that the next phase of India’s digital evolution hinges on a critical shift—from scale to precision.
India’s Digital Success: A Foundation Built on Scale
India’s digital public infrastructure (DPI) is a global benchmark, powering services that touch nearly every citizen. The report highlights staggering statistics that underscore this success:
- UPI: In April 2025, UPI processed 17.89 billion transactions worth ₹23.9 trillion, rivaling the monthly GDP of several mid-sized economies.
- Aadhaar: Over 27.07 billion authentications were recorded in FY 2024-25, forming the backbone of identity-linked service delivery.
- Ayushman Bharat: With over 369 million digital health IDs in circulation, it is revolutionizing health data interoperability.
- DigiLocker: As of February 1, 2025, it boasts 46.52 crore users, enabling seamless document access.
- Direct Benefit Transfer (DBT): In FY 2024-25, ₹5.47 lakh crore was transferred to beneficiaries across 330+ schemes.
- Digital Penetration: India has 1.2 billion mobile subscribers and 800 million internet users, with 96.96 crore internet connections (June 2024) and 94.92 crore broadband connections (August 2024).
These figures reflect India’s unprecedented scale in digital governance. However, as the report warns, scale alone is no longer enough. The next decade demands a pivot toward data quality, defined by six core attributes: accuracy, completeness, timeliness, consistency, validity, and uniqueness. Without this shift, India risks undermining public trust, fiscal efficiency, and the potential of AI-driven governance.
Why Data Quality Matters for Digital Governance
High-quality data is the cornerstone of effective digital governance. The NITI Aayog report outlines several reasons why prioritizing data quality is non-negotiable:
1. Fiscal Efficiency and Resource Allocation
Poor data quality, such as erroneous or duplicate beneficiary records, leads to significant fiscal leakage. The report estimates that 4–7% of annual welfare expenditure—billions of rupees—is wasted due to inaccurate or duplicate records. For example, removing 17.1 million ineligible names from the PM-Kisan list saved approximately ₹90 billion. Inaccurate data also results in misallocated resources, delaying critical interventions like crop-loss compensation due to mismatched land titles.
2. Policy Effectiveness and Precision
Inconsistent data distorts policy decisions, leading to misaligned programs or delayed adjustments. A single incorrect digit, such as a wrong IFSC code in PM-Kisan records, can stall subsidy transfers, affecting farmers’ livelihoods. The report cites examples like pension and health benefit denials due to incorrect Aadhaar details, which erode the effectiveness of welfare schemes.
3. Public Trust and Governance
Mismatched records or rejected claims erode citizens’ confidence in digital platforms. The report notes that incorrect data has blocked pensions, healthcare benefits, and subsidies, leading to citizen frustration and undermining trust in government initiatives. For instance, in late 2022, 17,000 health insurance cards were blocked due to identity mismatches, with no clear mechanism to resolve the issue.
4. Efficiency in Service Delivery
Poor data quality slows down service delivery, creating bottlenecks in welfare programs. The report highlights cases like ration card readers failing to recognize elderly fingerprints in 2024, delaying subsidized grain distribution. Similarly, during a 2013 LPG subsidy rollout, only 60% of households were correctly linked, with 40% bulk-rejected to meet speed-driven targets.
5. AI and Data-Driven Governance
As India embraces AI-driven governance, the quality of data feeding these systems is critical. Inaccurate or incomplete data can lead to AI hallucinations—misleading outputs that undermine public welfare. The report emphasizes that clean, validated data is essential for AI models to deliver accurate predictions and drive innovation in healthcare, agriculture, and e-governance.
6. Data Quality Debt
Persistent poor data quality creates a data quality debt, an accumulation of unaddressed errors that compounds over time. Fixing these errors, such as the two years required to resolve bulk LPG subsidy rejections, demands costly manual reconciliation, further straining public resources.
Challenges in India’s Data Ecosystem
Despite its digital achievements, India’s data ecosystem faces pervasive challenges that hinder effective governance. The report identifies the following issues:
1. Systemic Design Flaws
Many data platforms prioritize quantity over accuracy, incentivizing speed over precision. For example, during a 2017 PM-Kisan linkage drive, 4.4 lakh ghost students were found claiming midday meal funds due to rushed data entry.
2. Data Fragmentation
Government data systems remain siloed, with incompatible formats across ministries. This fragmentation makes data sharing and integration manual and time-consuming, hindering seamless governance.
3. Legacy Systems
Many core systems rely on outdated technology lacking validation or audit trails. Minor updates can disrupt these systems, leading to errors and delays. For instance, in 2024, ration card readers failed to recognize elderly fingerprints, delaying grain distribution.
4. Lack of Accountability
No clear data ownership exists across systems, allowing errors to persist without resolution. The report cites the 2022 health insurance card issue, where 17,000 cards were blocked with no accountable body to address the problem.
5. Speed Over Accuracy
Rushed data entry, driven by enrollment targets, sacrifices accuracy. During the 2013 LPG subsidy rollout, 40% of households were bulk-re rejectioned due to errors introduced by speed-driven processes.
6. Low Expectations
A culture of accepting “80% accuracy as good enough” perpetuates systemic errors. For example, in 2019, a state declared itself open defecation-free, but a 2020 audit revealed nearly half of rural homes still lacked toilets.
Government Initiatives for Data Governance
India’s data governance journey began with the first Census in 1881 and has evolved through institutions like the National Sample Survey Organization (NSSO) and Central Statistical Organization (CSO). The rise of digital technologies has introduced Management Information Systems (MIS), such as the Health Management Information System (HMIS) for tracking healthcare indicators and the Pratibimba dashboard in Karnataka for real-time governance insights.
Key government measures to improve data quality include:
- National Data Governance Framework Policy (NDGP 2022): Aims to standardize data governance and establish the India Data Management Office (IDMO) to oversee data standards and sharing.
- Open Data Initiative (data.gov.in): Promotes transparency by providing machine-readable government data for public and private sector use.
- National Data Analytics Platform (NDAP 2022): Centralizes standardized government data, ensuring real-time updates and supporting data-driven research.
- Open Data Telangana (2016): A successful model for data transparency and public participation since 2017.
- Chief Data Officers (CDOs): Appointed in ministries to oversee data quality, validation, and compliance.
- Data Governance Quality Index (DGQI): Launched in 2020 by NITI Aayog’s Development Monitoring & Evaluation Office (DMEO), it assesses data preparedness across ministries. DGQI 2.0 (2021) expanded to include data strategy and outcomes.
- Centre for Data Management and Analytics (CDMA): Established in 2016 under the Comptroller and Auditor General of India (CAG), it guides data analytics for audit offices.
Global Examples of Data Governance
The report draws inspiration from global best practices:
- Singapore: The Government Data Office ensures standardized, transparent data sharing, boosting public trust.
- New Zealand: The Integrated Data Infrastructure (IDI) merges data across sectors for comprehensive policy insights.
- Australia: Chief Data Officers and a national data strategy enhance cross-departmental collaboration.
- Estonia: Digital governance and e-Residency streamline service delivery with secure data.
- United States: The Open Data Initiative and Data Quality Assessment Framework promote transparency.
- United Kingdom: The National Data Strategy ensures standardized data for efficient coordination.
NITI Aayog’s Tools for Data Quality
To address these challenges, the report introduces two innovative tools:
- Data Quality Scorecard: Tracks attributes like accuracy, completeness, and timeliness, helping departments identify gaps and take corrective actions.
- Data Quality Maturity Framework: Assesses data practices across seven dimensions and five maturity levels (Foundational to Institutionalized), guiding departments toward improvement.
Additional tools include:
- Starter Kit for Quick Wins: Offers practical actions like real-time validation and assigning data stewards.
- Data Custodianship Tools: Ensure continuous updates and corrections for high-value datasets like Aadhaar.
- Data Interoperability Framework: Facilitates seamless data exchange across platforms like UPI and PM-Kisan.
- Automated Data Entry and Validation Tools: Reduce errors in programs like PM-Kisan by preventing incorrect data entry.
Recommended Way Forward
The report proposes actionable strategies to improve data quality:
- Institutionalizing Data Ownership: Appoint custodians at national, state, and district levels to ensure accountability.
- Incentivizing Quality: Reward accuracy and completeness through performance metrics like error rates and timeliness.
- Enhancing Interoperability: Standardize data formats using frameworks like IndEA and NDGFP for seamless integration.
- Automating Data Validation: Implement real-time checks to minimize errors at the point of entry.
- Promoting Data Stewardship: Foster a culture of shared responsibility for data quality across government levels.
- Ensuring Data Security and Privacy: Adopt global best practices to protect high-value datasets.
- Regular Audits and Quality Checks: Verify databases like Aadhaar to maintain consistency.
Conclusion: A Call for Precision-Driven Governance
As India enters a new phase of digital maturity, the India’s Data Imperative report underscores that data quality is no longer a backend concern—it is central to public trust, efficient service delivery, and the success of India’s AI ecosystem. “Data quality must become a frontline governance imperative, integral to building Digital India on a robust foundation of trust,” said Debjani Ghosh, Distinguished Fellow at NITI Aayog. By institutionalizing ownership, incentivizing accuracy, and fostering interoperability, India can ensure that its digital infrastructure delivers equitable, precise, and efficient services to every citizen.
Frequently Asked Questions (FAQs)
1. What is the main focus of NITI Aayog’s India’s Data Imperative report?
The report emphasizes the urgent need to shift from scaling India’s digital public infrastructure to prioritizing data quality. It highlights that high-quality data—defined by accuracy, completeness, timeliness, consistency, validity, and uniqueness—is critical for fortifying digital governance, ensuring efficient service delivery, preventing fiscal leakage, and building public trust.
2. Why is data quality important for India’s digital governance?
Data quality is essential to prevent fiscal leakage (4–7% of welfare budgets lost annually due to errors), enhance policy precision, improve service delivery, and maintain public trust. It also supports AI-driven governance by ensuring accurate data for reliable predictions, avoiding issues like AI hallucinations caused by poor data.
3. What are the key challenges in India’s data ecosystem identified by the report?
The report outlines challenges such as systemic design flaws prioritizing speed over accuracy, fragmented data storage across siloed systems, outdated legacy IT systems, lack of clear data ownership, and a culture accepting “80% accuracy as good enough,” leading to persistent errors and inefficiencies.
4. What tools does the report propose to improve data quality?
NITI Aayog introduces the Data Quality Scorecard to track attributes like accuracy and timeliness, and the Data Quality Maturity Framework to assess data practices across seven dimensions. Other tools include a Starter Kit for quick wins, Data Custodianship Tools, a Data Interoperability Framework, and Automated Data Entry and Validation Tools.
5. What are the recommended strategies to enhance data quality in India?
The report suggests institutionalizing data ownership with custodians at national, state, and district levels, incentivizing accuracy through performance metrics, enhancing interoperability with standardized data formats, automating validation, promoting a data stewardship culture, ensuring data security and privacy, and conducting regular audits of high-value datasets like Aadhaar.
