India must urgently standardise, update, and broaden urban data systems to enable coherent analysis and more effective policy interventions across its rapidly growing cities
Image Source: Pexels
Home to over 500 million people today, India’s cities are projected to accommodate 600 million by 2036. Yet the institutional capacity to accurately measure and understand the populations, economies, services, and vulnerabilities has not kept up. Many cities lack accurate, structured data. Where it exists, it cannot be compared across cities. Critical datasets are updated once in a decade, or they are held in formats that differ so fundamentally from one state to another that aggregation becomes guesswork rather than analysis. Researchers often work with data proxies and estimates. This is not a fringe problem or a technical inconveniency encountered in research projects. Data gaps hinder accurate policymaking, often blunting the effectiveness of urban programmes. India must urgently address its persistent data issues to make informed policy decisions and ensure that its policies and programmes yield maximum benefits, fostering trust in urban governance.
The data challenges confronting Indian cities arise from a convergence of three primary constraints: gaps in what is measured, mismatched update frequencies, and incompatible data formats.
These data gaps reveal a structural flaw: designed around formal, registered, and recognisable entities, official measurement systems consistently overlook the significant growth in the informal, unregistered, and invisible sectors. Consequently, research and tools developed from these systems inherit these blind spots.
Gaps in India’s urban data landscape are not randomly distributed but concentrated among groups and systems already marginalised in official records. For example, although informal waste pickers handle between 60 and 90 percent of recyclables in most Indian cities, official waste management data only tracks formal municipal collection. As a result, the primary actors in waste management are absent from the official data that informs researchers and policymakers. The 2016 Solid Waste Management Rules required the integration of waste pickers into formal systems; however, most cities have not implemented this mandate. The lack of data on informal waste flows renders the sector’s contributions invisible in budget allocations and administrative processes necessary for implementation.
These data gaps reveal a structural flaw: designed around formal, registered, and recognisable entities, official measurement systems consistently overlook the significant growth in the informal, unregistered, and invisible sectors. Consequently, research and tools developed from these systems inherit these blind spots.
The most fundamental constraint is the lack of a standardised, city-level tracking system for critical dimensions, such as social cohesion, communal tensions, or civic trust, which are central to understanding how a city functions. While the National Crime Records Bureau publishes crime data for 53 metropolitan cities, even this is reported with inconsistent disaggregation, making cross-city comparison unreliable. Citizen perceptions of safety, satisfaction with services, or confidence in local government are captured sporadically in one-off surveys with no regular cadence.
Mental health infrastructure is another near-complete blind spot. The National Family Health Survey-6 (NFHS-6, 2023–24), the primary source of health data for most researchers, dropped several biomarker indicators in its latest round and added no mental health module, leaving the episodic National Mental Health Survey as the only available instrument. For anyone attempting to understand urban health in any comprehensive sense, the absence is striking.
The invisibility of the informal health sector is the most consequential gap. NFHS-6 asks respondents about their ‘source of health care,’ but categorises informal providers, who serve the majority of residents in low-income urban neighbourhoods, either under the generic label of ‘private clinics’ or excludes them entirely. The National Sample Survey’s 75th round demonstrated that explicit tracking of informal providers is methodologically feasible, yet this approach has not been institutionalised.
Urban data in India is not updated in a timely manner, and combining them requires assumptions that can conflate and complicate the entire analytical exercise. The Census, the most comprehensive source of demographic and housing data, operates on a ten-year cycle. The 2021 enumeration has been delayed, with the exercise now scheduled in 2027. The National Family Health Survey (NFHS) follows a 3-year cycle. The Periodic Labour Force Survey (PLFS), which shifted to monthly employment estimates from January 2025, runs on its own calendar. Climate monitoring from the India Meteorological Department is available daily. Municipal service delivery reports, when published, appear annually or less frequently.
Urban data in India is not updated in a timely manner, and combining them requires assumptions that can conflate and complicate the entire analytical exercise.
Such misalignments are methodologically disabling for the emergence of any accurate and coherent understanding of a city at a given point in time. Combining a 2011 Census denominator with 2023 health survey data and 2024 employment figures produces a composite that is internally incoherent. The populations considered by these datasets are distinct, shaped by different economic conditions, and living in cities with different configurations.
Urban Local Bodies (ULBs) across India maintain financial and administrative records in formats that differ not just between states but sometimes between cities within the same state. The 15th Finance Commission and supporting studies indicate that the transition to double-entry accrual-based accounting remains incomplete, with many ULBs continuing to rely on cash-based systems. While Karnataka operates through centralised portals, Maharashtra follows its own Municipal Account Code. Smaller municipalities in many states maintain records manually or in proprietary formats.
Urban Local Bodies (ULBs) across India maintain financial and administrative records in formats that differ not just between states but sometimes between cities within the same state.
For anyone building a tool that needs to ingest municipal data, for example, a budget transparency platform, a service delivery tracker, or a public finance dashboard, this fragmentation poses a fundamental obstacle.
The data problem in Indian cities is significant and deeply rooted, but it is not unavoidable. It results from choices about what to measure, how often, what standards to use, and what resources are allocated. These choices can be made differently. Several directions should be explored.
Urban India’s data problem is not solely a technology issue. Improved dashboards, smarter analytics platforms, and better visualisation tools cannot replace missing or non-comparable data. Cities must urgently invest in robust, reliable data collection, standardisation, and in creating outcomes that support consistent, complete, and publicly accessible measurements. Without these foundations, the tools developed will be no more than sophisticated ways to handle incomplete information.
Nandan H Dawda is a Fellow with the Urban Studies programme at the Observer Research Foundation.
Aditi Dixit is a Research Intern at the Observer Research Foundation.
The views expressed above belong to the author(s). ORF research and analyses now available on Telegram! Click here to access our curated content — blogs, longforms and interviews.
Dr Nandan H Dawda is a Fellow with the Urban Studies programme at the Observer Research Foundation. He has a bachelor's degree in Civil Engineering and ...
Read More +
PREV
