Overview

The Tony Blair Institute for Global Change (TBI) released a report titled “From Digital Scale to Data Power: A Data Operating Model for India's States” on 12 October 2026. The report notes that India has enrolled more than 1.4 billion individuals in Aadhaar and that the Unified Payments Interface (UPI) processes over 20 billion transactions each month, representing roughly 49 % of global real‑time payments. Despite this digital scale, government data remains fragmented across ministries and state administrations, limiting the ability to use data for policy formulation and service delivery.

Data Fragmentation and Fiscal Impact

The study cites NITI Aayog’s 2025 assessment that poor‑quality data leads to fiscal leakage estimated at 4‑7 % of annual welfare expenditure. It provides concrete examples of past data clean‑up efforts: removing 17.1 million ineligible names from the PM‑KISAN farmer support scheme saved approximately ₹90 billion; eliminating 35 million bogus LPG connections saved about ₹210 billion over two years; and deleting 16 million fake ration cards is saving roughly ₹100 billion each year.

Digital Infrastructure Achievements

Aadhaar has supported more than 24.5 billion e‑KYC transactions and has helped raise formal banking inclusion from roughly 25 % in 2008 to over 80 % in 2023. DigiLocker has issued and verified nearly 10 billion documents for over 685 million citizens. However, the report stresses that these achievements do not translate into interoperable data across government functions.

Recommendations

TBI proposes seven recommendations that constitute a “state data operating model”:

1. Establish clear leadership and mandate for data governance.

2. Define data ownership and stewardship responsibilities.

3. Adopt common data standards across departments.

4. Build shared data capabilities and platforms.

5. Implement independent quality‑assurance mechanisms.

6. Introduce legal and procurement safeguards.

7. Create a State Data Balance Sheet to manage data assets and risks similarly to financial balances.

States that implement these measures are expected to improve decision‑making speed, enhance citizen service delivery, and build public trust in official statistics.

Existing Initiatives

The report aligns with ongoing Union initiatives such as the National Data Management System (NMDS 2.0), the Standardised Quality Assurance Framework (SQAF), the AI‑Readiness Framework, and the Quality‑Performance‑Reporting (QPR) Portal. It also references state‑level programmes: Karnataka’s “Kutumba” social registry, Odisha’s Social Protection Delivery Platform, Rajasthan’s “Pehchan” portal, as well as efforts in Andhra Pradesh, Uttar Pradesh, Tamil Nadu, and Telangana.

Voices

Vivek Agarwal, Country Director of TBI and co‑author, emphasized that the fundamental question is whether data quality is sufficient for AI or human officials to act upon. Ott Velsberg, former Government Chief Data Officer of Estonia and co‑author, noted that India’s next test is building trustworthy, connected data rather than merely scaling digital infrastructure.

Conclusion

The report concludes that without addressing data fragmentation, AI deployments risk amplifying existing blind spots. A robust data operating model is presented as the prerequisite for India to translate its digital scale into effective, data‑driven governance.