India’s silent data revolution will arm policymaking with evidence

There are four key aspects of this exercise: timeliness and frequency of data releases, data diversity to expand the statistical universe, harmonization of administrative data-sets and a user-centric shift in producing official statistics.

The most visible transformation is the higher speed and frequency of data dissemination. India’s Consumer Price Index (CPI) was already on par with global standards in timeliness, while the Periodic Labour Force Survey (PLFS) has evolved from an annual exercise to a monthly pulse -check of employment. What once took 5-6 months to publish now emerges within 45-90 days of completion.

Also Read: GDP’s dirty little secret: Why we should be tracking GVA instead

This acceleration extends across indicators. The Index of Industrial Production now appears within 28 days from the end of its reference month, compared to 42 days previously. The extension of quarterly PLFS to rural areas represents a leap that gives policymakers near real-time insights into rural employment trends that used to be invisible for months. Moving the Annual Survey of Unincorporated Sector Enterprises to a quarterly schedule will provide better insights into informal sector activity.

This is a shift in governance philosophy. When data arrives months after the fact, policy responses are reactive at best. When statistics flow with the rhythm of economic activity, governance becomes genuinely responsive to ground realities.

Beyond speed, India is expanding the diversity and scope of its statistical apparatus. New surveys of company capital expenditure and service sector enterprises offer a view of business investment patterns and service economy dynamics. An effort to measure household income is underway too. 

Also Read: Himanshu: India’s economy is too complex to afford less-than-robust statistics

To better leverage data, work is afoot on new frameworks to harmonize different data-sets and set standards for data quality. These initiatives reflect the fact that in today’s digitally connected economy, isolated statistics can be both inefficient and ineffective. Equally welcome are efforts to roll online prices into the CPI, along with online rail fares and fuel prices. MoSPI’s creation of a dedicated research analysis division signals a shift from data collection to interpretation. Working papers from this division promise to transform raw statistics into actionable intelligence.

The statistical system’s growing focus on user needs is even more significant. Data awareness is being enhanced through platforms like e-Sankhyiki and the GoIStats app. Our system is evolving from a distant repository of numbers into a dynamic, responsive and user-friendly ecosystem. 

We must keep this momentum going towards our goal of becoming an advanced economy. Yet, significant challenges remain. The push for sub-national statistics, particularly at the district level, is a frontier. India’s diversity demands granular data that can inform targeted interventions rather than one-size-fits-all policies. MoSPI’s initiative to modify sample designs of surveys to enable district-level estimates is encouraging.

Also Read: TCA Anant: A household income survey will be valuable if clarity beats confusion

The long-delayed Census looms as both an opportunity and necessity. Population data underpins virtually every aspect of government planning, from resource allocation to constituency delimitation. Delays here can cascade through the entire statistical ecosystem. Meanwhile, state-level statistical capacity needs to improve, for which we need more statisticians and data science experts at the state level.

The use of AI in survey methodology and response processing can be the next leap. As traditional survey response rates decline globally, AI-assisted data collection and validation could maintain quality, reduce costs and improve speed.

We still lack official seasonally-adjusted data for key indicators, which most developed countries have. The challenge of calculating GDP deflators persists, with volatile swings causing sharp differences between nominal and real values. 

While the government’s data website and download systems have improved, navigating our data systems remains difficult, compared with the seamless experience offered by some private providers. We need an institutional system to quickly and carefully respond to data queries. 

More complex issues need attention too. Demand-side measures of GDP are often estimated from production-side indicators; and quarterly GDP statistics rely on an ‘allocation’ of annual data, rather than actual collection. Large and frequent revisions in GDP statistics remain problematic; the Indian GDP series still reports five versions.

Also Read: GVA data haze: Has India been overcounting the output of its informal sector?

Finally, easing micro data access for researchers to answer key questions on the Indian economy with due diligence is crucial. Stepped up R&D and innovation are a critical input for our economic progress, for which data feeds are key contributors.

To speed up data modernization, we need added monetary and non-monetary resources, plus the right incentives at all levels. India’s data revolution can transcend its technical aspects to redefine the relationship between the state and citizens, and between evidence and policy. When employment data is frequent, price indices capture digital transactions alongside traditional markets and administrative records smoothly integrate with survey data, the space for responsive governance expands dramatically. 

The true measure of this revolution will not be found in technical specifications or processing speeds, but in policy outcomes. As our statistical infrastructure matures, it promises to become one of India’s most valuable public goods. Data-driven governance could then help deepen democratic accountability. It can transform how India thinks about itself and its future.

The author is professor of economics, and director and head of the Isaac Center for Public Policy, Ashoka University.