The collection and use of data in welfare governance in India has seen a rapid increase over the last two decades. What began as an exercise of digitisation of manual records, soon expanded into a series of policy actions that now stand to revolutionise how day-to-day governance decisions are made based on data.
Data can be used for three types of governance decisions: (i) strategic decisions that set a long-term vision of a developmental outcome, (ii) tactical decisions to implement the long-term vision in pre-defined time periods, and (iii) operational decisions that define day-to-day activities to achieve the vision.
Data governance frameworks
Data governance frameworks prove useful when generating a systematic approach towards decision-making processes. They involve defining the data, setting permissions and rules of usage, facilitating data exchange across departments and tiers of government, and encouraging storage and use of data in various ways.
These frameworks must be based on the government’s local context, and designed to ensure accessibility for data-users within and outside the government. To safeguard the privacy of citizens from whom the government intends to collect data, informed consent must be sought, and data should be anonymised before it is made public. Principles of open data should also be adopted to ensure accessibility for all stakeholders.
In late 2022, Accountability Initiative at the Centre for Policy Research (CPR) conducted a study for the Chief Minister’s Rajasthan Economic Transformation Advisory Council, to understand the state’s experience with data for evidence-based decision-making in policy. The study aimed to unpack experiences of engaging with data of functionaries within the government departments of education, health, and rural development.
A framework was developed to understand four aspects of data governance across the lifecycle of a data point from when it is generated, made usable, used, and finally how it feeds back into policy improvements. Structured interviews with key persons, focus group discussions, and case studies were conducted to understand how Rajasthan navigates the collection and use of data for welfare governance.
In this note, we reflect on findings from the study through examples of how Rajasthan has attempted to solve developmental challenges through better data. Our observations – covering the state level to the grassroots – can provide insights for other state governments to strengthen their data systems, as well as for practitioners and researchers involved in data governance across sectors.
Rajasthan has been a frontrunner in terms of innovations in data collection methods and use. The state has developed a range of portals, platforms, and management information systems (MIS) for welfare governance.
While some of these are designed specifically for citizens to seek information or share grievances such as the Jan Soochna Portal, others are used internally for management. Below we describe key learnings from the three departments we studied.
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Real-time data on education
Rajasthan’s experience with data in governance differs from sector to sector. Within each sector, initiatives are largely taken by individual departments. For example, the Department of Education (DoE) ensures that data pertaining to school education are collected, stored, and used. Different units within the department engage with subsets of the data that relate directly to their work.
The DoE’s experience stands out as it was among the first in the country to collect data on education in real time. Through the Integrated Shala Darpan Portal, the department collects over 100 data points regularly, ranging from student attendance to school infrastructure.
The portal also gathers information on short-term schemes that the state government may run like the distribution of scholarships or sanitary napkins. The data are made actionable for various officers through a dashboard where it can be downloaded in different formats and visualised through graphs.
The award-winning platform is run by a team at the state level that receives technical support from the National Informatics Centre. Decisions on what indicators are to be collected are made by state-level bureaucrats depending on the state’s priorities.
The data collected reflect what is considered important to understanding the progression of education and what can support decision-making to improve education outcomes. Hence, the data remains of high value to the state.
Interoperability of datasets
While the DoE has paved the way for prioritising education data based on needs and making it accessible to stakeholders through dashboards, the Department of Health and Family Welfare (DoHFW) has been successful in bringing together different datasets across government departments. An example that stands out is the e-Detailed Accident Report Portal (e-DAR).
When a road accident occurs, concerned officials enter data into the portal. Such information is collated to help identify accident-prone locations and encourage investigation into what makes the location risky. Further, it enables the department to understand requirements for medical facilities such as trauma centres and ambulances in proximity to these sites.
Besides the DoHFW, the portal is used in varied ways by stakeholders such as the police and Public Works Department (PWD). For instance, the PWD uses the data to put in place better signage or improve road safety.
In our study, we identify other such avenues for datasets that speak to one other. These include linking data on children younger than six years of age from the Pregnancy, Child Tracking & Health Services Management System (PCTS) to that collected under the Integrated Shala Darpan Portal, thereby targeting out-of-school children and ensuring the provision of education, nutrition and health services to children right from birth.
Similarly, linking data on Direct Benefit Transfers to that of beneficiaries on PCTS could ensure that all pregnant or lactating mothers get access to schemes they are eligible for such as Pradhan Mantri Matru Vandana Yojana and the Janani Suraksha Yojana. Further, health and education data can be linked through Jan Aadhaar – a Rajasthan government initiative which seeks to make the benefits of various schemes more accessible, transparent, and easy for residents.
Improving interoperability stands to benefit both citizens, that is, the users of public services, and service providers like doctors, nurses and teachers. However, special arrangements would be needed to integrate citizens who do not have Jan Aadhaar cards or have chosen to opt out of the identification.
Understanding the need for accessibility
The case of data collected by the Rural Development and Panchayati Raj Department sheds light on why it is important to make dashboards accessible to promote use of data. Currently, the department collects data on over eight platforms that are designed by both the Centre and the state government.
These range from portals for specific schemes, portals where training programmes are uploaded and tracked, to those where meetings for rural local governments are planned. Very few of these platforms have been integrated and none of these allow functionaries to visualise data in graphs that can aid analytical decision-making.
The portals only allow users to download select datasets as Excel or PDF files. Functionaries shared that they would benefit greatly from predictive analyses generated on the platforms. This could include data inputs into target-setting, and tracking progress of implementation, among others. Upon integration, enhancing accessibility of the platforms can improve how data are used in evidence-based decision-making.
None of the platforms listed above currently make data publicly available. Adopting principles of open data and making anonymised data available in different formats can further promote usage by a range of stakeholders such as civil society organisations and academia.
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Insights for data governance
Through the examples explored earlier, it is clear that the volume and velocity of data have increased and that there are ways for departments to move past silos to generate and share data for common uses. However, data are rarely analysed or used by those that input it; use is largely confined to elite echelons of the state offices with accessibility across all other levels remaining limited.
As other states continue to design their data policies and MIS, we believe the following features are important to consider to address these challenges:
Constituting a state data stewardship body: As we observed in Rajasthan, data systems vary across departments and their usage is usually determined by the capacity that exists within that department. Moreover, while there have been some successful attempts at interoperability of data systems within the same department, interaction and integration of datasets across departments remains limited.
There is a need to constitute a data stewardship body that can work specifically on data-related matters across departments. In consultation with experts from outside the government, this body can help develop the data governance framework wherein standards, rules and regulations are defined. The body can also help cut across departmental silos and identify pathways for data sharing from generation to use.
The body can also reach out to the private sector to access missing pieces of information or collaborate on developing innovative, problem-solving welfare initiatives. A body at the state level constituted for this purpose can learn from other institutions that have done the same effectively.
Boosting data use by all tiers of administration: As we have noted in the context of Rajasthan, while data are being used within the government, it is limited to elite functionaries. Other tiers, especially those closest to the citizens at the frontline, only support data generation or verification.
Data on school infrastructure for example, was collected from schools but did not feed into decentralised decision-making even at the cluster/block level. Instead, decisions were made based on aggregations at the state level. With access to data, schools can make choices for themselves, such as best practices for managing infrastructure or remedial learning programmes for specific age groups, based on learnings drawn from other schools’ data in the same district or block.
Data use will differ at different levels of government based on needs, and sustained efforts must be made to enhance the accessibility of systems.
Effective legal frameworks: In India, Acts such as Public Records Act, 1993, Information Technology Act, 2006, and National Data Sharing and Accessibility Policy, 2012 primarily focus on governance structures related to privacy, public record maintenance, and classification of government data.
Further, landmark judgements such as the K.S Puttaswamy vs. Union of India (2018) identified key factors that must be justified to collect private data on individuals. However, to operationalise these, changes are required to our laws. To supplement these, various states have designed their own data policies too, but these remain unenforceable.
Three types of laws could prove useful in ensuring that there is an effective legal framework behind how data are used in decision-making, particularly when using data pertaining to an individual’s privacy. These laws can be implemented at the Centre or state level, depending on existing laws that need to be amended. The first type is to ensure that citizen rights are protected.
Here, it is important to ensure that data collected are encrypted and stored on a secure platform. There should be clear detailing of applicable penalties in case of data leaks, and efforts to ensure we are able to identify an individual or group of individuals who have broken the law. The second type of law pertains to open data that allows for anonymised data to be made public.
By doing this, the government opens data for use by any interested party that may support the common good by providing insights from the data and/or utilising these in their developmental work. It also ensures that the government is committed to high standards of transparency.
The third type of law relates to provisions and regulations for using data in evidence-based policymaking. Such a law should detail how data can be used in governance and in the design of evidence-based policies.
The benefits of using data in decisions in governance outweigh the costs of collecting, storing, and managing data. As governments expand the scope of data in social welfare sectors such as education and health, it is important that they take a step back and define a clear vision of what they intend to achieve.
This vision and an action plan to achieve it must be compliant with laws that regulate the generation and use of data. Governments must continue to learn from other experiences that stand to inform how they plan and implement reforms in data governance.
Further Reading
Accountability Initiative, Centre for Policy Research. (2023). Policy Study on Using Data for Better Policy Formulation and Evidence-Based Decision-Making, Chief Minister’s Rajasthan Economic Transformation Advisory Council, Department of Planning, Government of Rajasthan.
This article was also published on the Ideas for India website.