Hyderabad: The lack of information alongside a largely compliance-driven, makeshift approach to prior data-collection drives has negatively impacted governance in a large way. The COVID-19 pandemic specifically highlighted inadequate urban service delivery brought about by information gaps in three areas: public health, transportation and the protection of migrant populations. To overcome these issues, formulating long-term systematic city data collection policies is key.
The importance of mobility
- During India’s lockdown, the coronavirus showcased how cities remain ill-equipped to respond to citizens’ public health needs. The drastic measure of the lockdown was intended to buy time to scale up the response. Instead, city officials spent days trying to get basic data on the location of facilities and bed capacities. Data on deaths and cluster outbreaks was suspect.
- Over time, progress by some city governments that built platforms to track case numbers, and geographic information system (GIS) portals to analyse containment zones and allocate resources.The use of alternative sources of information by government officials and researchers alike due to data gaps in traditional sources. Citizen volunteer efforts such as covid19india.org and movement trackers such as Google Mobility became single sources of truth in caseload and mobility respectively and were actively used for making policies in real-time.
- While COVID-19 highlighted how data gaps impede disaster response, it is important to remember that information on the demand and supply of healthcare services is needed to improve the day-to-day functioning of urban public health systems as well. This requires systems to be set up for the real-time collection and collation of information on disease incidence, services demanded, quality of existing facilities and personnel, etc. Such systems should accommodate the integration of alternative sources of information too, given their successful use during the COVID-19 pandemic.
- The COVID-19 pandemic will possibly change commuting choices, with residents picking modes of transport with smaller footprints such as private vehicles and motorcycles. With that in mind, it is crucial the current data-collection methods are replaced. To monitor how urban travel demand is changing, setting up systems that collect transport data in frequent intervals is key. For example, if authorities can collect data on commuting choices and behaviour of residents, they will be able to understand what patterns develop after the COVID-19 pandemic and, in turn, be able to respond with favourable policies.
- The plight faced by migrant workers in urban India was brought to the forefront of policy discussions during the COVID-19 pandemic. Before the crisis, not much data was available on the actual number and type of migrants in cities, and the services they access. As a result, when the country was in lockdown, state governments and academics provided various estimates on the number of stranded migrant workers, ranging from 2.6 to over 30 million. This lack of information led to ad-hoc delivery of key public services such as healthcare to this population during the crisis. That the government was still using the 2011 census as the point of reference for a highly mobile population led to large-scale exclusion from social protection systems.
- Going forward, data on out-of-state sections of society should be collected via regular urban surveys that provide a real-time overview of who actually lives in Indian cities, how many have access to services, where they come from, etc. Such an exercise would inform city policies and improve delivery of government schemes such as housing and adequate infrastructure for formal and informal migrant workers. In the longer term, it will help in making cities more accessible, comfortable and safer for vulnerable populations.
- There are recent positive developments with the Ministry of Housing and Urban Affair launching schemes such as the National Urban Digital Mission and the Indian Urban Data Exchange.These schemes aim to allow a free flow of datasets between cities, removing data silos and encouraging collaboration on data-driven policies going forward. Moreover, these new initiatives aim to base data-collection efforts on relevant and actionable use cases of such information. If the method of data – including metadata – collection is transparent and can be reproduced, less-factual data could be exposed for what it is.
The experience of India’s cities and their global counterparts suggests that if cities are to institutionalise and strengthen the use of smart solutions, certain actions need to be prioritised and include:
- Ensure standardisation and interoperability of data and smart city systems by formulating city-based technology management policies.
- Enhance data security and protection through the development of municipal data policies in compliance with national laws and guidelines that ensure citizens’ data security.
- Strengthen coordination across agencies through the establishment of new mechanisms for information sharing among city and state governments.
- Establish adequately staffed technology and data management organizations beginning with the appointment of a chief city data officer and city data contributors.
- Adopt objective indicators for monitoring service delivery by developing assessment frameworks.
- Leverage multiple delivery channels for public outreach through implementation of connectivity-related smart solutions.
It is often said that data is the new oil or that data is the oxygen that will power the Fourth Industrial Revolution. It is important to note that both oil and oxygen once consumed are depleted. On the other hand, data gains value as it is consumed more and patterns are drawn from aggregated data.To drive better governance and urban services for their citizens, cities need to generate, capture, analyse and share better data. Doing so would not only tackle structural issues impacting the governance of Indian cities, but also help them reach their growth potential.
World Economic Forum
