India must strengthen its statistics for a new era of data-driven governance

Under the stewardship of pioneering statisticians like C.R. Rao, Debabrata Lahiri and V.M. Dandekar, the NSS evolved into a world-class statistical system. In 1951, its scope was expanded to urban India. In 1955, NSS crop cutting experiments (CCE) became the gold standard in estimating agricultural yields. 

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Data collection was institutionalized by the formation of the National Sample Survey Organisation (NSSO) in 1970. Since the 1980s, the NSS has widened its ambit to include health, education and informal-economy studies.

Surveys such as the Annual Survey of Industries (ASI), Time Use Survey, Periodic Labour Force Survey (PLFS), Household Consumption Expenditure Survey (HCES) and the Annual Survey of Unincorporated Sector Enterprises (ASUSE), for instance, have informed our welfare and industrial policies. Now, surveys on service-sector enterprises and corporate investment intentions will offer richer insights into the Indian economy. 

Aligning with global statistical standards, the independence, transparency and accessibility of NSS data has aided governments, researchers, private enterprises and civil society alike. The NSS has ensured that data remains a public good in India.

India’s economic success in the coming decades will depend on how well we collect, interpret and use data to drive progress. Rapid urbanization, climate change, a digital transformation and evolving labour markets are posing new challenges that require informed decision-making. The NSS must adapt and innovate to remain at the forefront of statistical excellence.

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We need more local data: Evidence-based policymaking is crucial in a country as diverse as India, where socioeconomic differences can vary significantly between districts and even within them. NSS data has always provided rich insights at the national and state levels, but not the district level. 

While estimates can be generated, they do not necessarily represent the districts’ populations. The success of our Aspirational Districts Programme has demonstrated how granular data can drive targeted development. 

Expanding the focus of surveys to district-level data is now imperative. District-level estimates can enable policymakers to address regional disparities better. Estimates of output, consumption, employment and unemployment are some avenues. It is time for a more detailed and localized approach to data collection.

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AI and the future of statistical excellence: As India strides toward its vision of a Viksit Bharat, the future of the NSS lies in harnessing technological advancements. Artificial intelligence and machine learning could revolutionize statistical surveys. 

For instance, the UK is leveraging AI for data collection and the US Census Bureau is exploring the use of AI in data validation and structuring of datasets. The NSS must also seek to be at the forefront of this technological revolution. 

Leveraging AI to ensure data quality, data validation and processing can speed up processes and improve the quality of our statistics. This, in turn, can free up valuable resources, allowing the NSS to expand its scope of data collection and analysis.

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The next frontier in data collection: Traditional sample surveys, while robust, may face challenges in capturing rapidly changing economic activities. Integrating real-time data from satellite imagery, mobile networks, financial transactions and internet usage patterns can provide valuable insights. 

Incorporating non-traditional data sources could further enhance the richness and accuracy of NSS studies. In countries like the Netherlands, AI-driven analysis of aerial images has improved agricultural statistics, setting an example that India could follow. Improving data processing capabilities through a digital transformation could lead to faster and more reliable statistical outputs.

Data accessibility and the case for improved data visualization: Public engagement and data democratization should also be key priorities for the NSS. Open-access, interactive data visualization platforms can make complex economic data more accessible to journalists and students, apart from policymakers. 

Countries like Canada and the US have data storytelling portals with interactive maps and graphs for people to understand socio-economic trends. The NSS could develop similar tools, fostering greater public awareness and engagement with statistical insights.

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High-frequency data can offer real-time insights: The plan of monthly unemployment data releases from April 2025 is a step in the right direction, but broader adoption of real-time economic indicators is necessary. Fast-response online surveys of businesses and households can further bridge data gaps. By embracing technology for high-frequency data collection, the NSS can provide more dynamic and policy-relevant insights, strengthening India’s economic decision-making framework.

Closing time gaps would mean better decisions: Reducing the time lag between conducting surveys and releasing data is crucial for ensuring that the data remains relevant for policymaking. Technology-driven automation can expedite data processing and significantly cut delays. 

The government must also increase NSS funding to modernize data infrastructure and hire skilled statisticians. By investing in faster data collection, real-time validation and automated reporting, India can make its statistics more dynamic and actionable.

As India moves towards becoming a global economic powerhouse, the role of data in decision-making will become even more critical. The NSS has upheld the highest standards of statistical integrity for 75 years. With the right investments in technology, methodology and institutional capacity, it will continue to lead the way.

The authors are, respectively, India’s G20 Sherpa and senior policy specialist at the Office of G20 Sherpa.