Several factors feed perceptions of decline. 

First and foremost, the demand for data and data-driven dialogue has increased rapidly, while the supply of timely statistical data has not kept pace. 

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Second, government agencies have, at times, idiosyncratically delayed or withheld data releases. For instance, the 2017-18 Consumption Expenditure Survey was withheld without adequate explanation. The initial releases of the Periodic Labour Force Survey (PLFS) and Annual Survey of Unincorporated Enterprises were also delayed. The much- anticipated Comprehensive Annual Modular Survey (CAMS), intended to be an annual exercise, was limited to just one round. 

Some of these shortcomings are now being addressed—for example, through the announcement of an advance release calendar, which could help mitigate delays and build public trust.

These challenges are further compounded by the fact that digital data has become embedded in the very infrastructure of governance. From tax returns to public services, the government is now often just an online form away. Aadhaar has become a key credential for accessing government services. Health records, tax filings and civic service data are all being converted into administrative data-sets that increasingly shape how the state understands and serves its citizens.

Some government departments are making productive use of this wealth of administrative data. A notable example is the tax authorities' use of the Annual Information Statement, which discloses to taxpayers a wide-ranging financial profile drawn from multiple data streams. However, this wealth of data has not yet been fully integrated into statistical systems for broader public analysis or policy use.

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In earlier columns, I have written about the potential of GST data. Similar potential exists across many administrative databases. Administrative data often covers large segments of the population, if not the entire population, allowing for granular analysis almost in real time. For example, linking data on the provision of public services such as piped drinking water or sanitation to health outcomes would allow for a richer understanding of their impact. Similarly, mapping time-use survey data to rural road connectivity could yield insights into how improved transport infrastructure affects work and mobility patterns.

Yet, realizing this potential remains difficult. These data-sets are often managed in silos, governed by transaction-of-business rules and subject to the administrative boundaries of ministries and departments. 

The objectives of different government entities frequently conflict, limiting cooperation and integration. As pithily observed in Yes, Prime Minister, a British TV show, "It would be different if the government were a team—but in fact, they are a loose confederation of warring tribes." A recent illustration of this dynamic is the tug-of-war between the departments of drinking water and sanitation, and health and family welfare, over how to frame survey questions on toilet availability and use.

Many scholars have offered thoughtful solutions to these institutional challenges. Unfortunately, most of these remain unimplemented, largely because they conflict with the existing paradigm of governance in India, which emphasizes departmental autonomy over integration.

An instructive precedent lies in the success of Pro-Active Governance and Timely Implementation (Pragati), an e-governance initiative launched by the Prime Minister on 15 March 2015.

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Pragati was designed to enhance coordination, expedite decision-making and track implementation across various tiers of government using real-time video conferencing, geospatial data and integrated dashboards. Crucially, Pragati was built within the governance framework defined by existing rules and departmental responsibilities. 

During the first term of the incumbent government, it emerged as a high-impact tool for unblocking stalled infrastructure and development projects. Between 2015 and 2018, the Prime Minister chaired 29 sessions, reviewing 257 projects worth over ₹12 trillion. 

Pragati also took up issues related to provident funds, the Central Government Health Scheme, student grievances and exam logistics—issues that cut across ministerial domains. It was widely seen as successful, with media stories highlighting the completion of long-delayed projects.

However, the 2019 general election and subsequent covid pandemic disrupted this momentum. Only 16 Pragati meetings were held between 2019 and 2024, compared to 29 in the previous four-year period—a clear deceleration. Media coverage of Pragati declined accordingly.

Beyond operational challenges, there is also the risk that India's statistical system is losing the narrative battle. Much of the public discourse still relies on perceptions shaped by older failures, rather than recognizing recent improvements in data availability, programme coverage and administrative digitization. Without timely and compelling evidence to support how government initiatives are improving lives today, legacy narratives persist unchallenged.

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This is where better use of administrative data—especially through cross-silo links—could make a significant difference. By integrating health records with service delivery data, or matching education outcomes with infrastructure investments, government analysts could construct data-backed stories that illustrate the current impact of flagship schemes. These stories would not only support evidence-based policymaking, but also help restore credibility and public engagement with the statistical system itself.

Reviving the original spirit of Pragati—focused on inter-agency coordination, accountability and outcome-driven review mechanisms—is essential. A renewed version of such an initiative could focus not just on physical infrastructure, but also on data infrastructure. We must improve the use of administrative data to enhance public policy analytics and performance monitoring.

Such an approach would go a long way towards bridging the credibility gap in the statistical system and making governance truly data-driven.

The author is a visiting professor at the Institute for Studies of Industrial Development and former chief statistician of India.