Why in the news?

The Ministry of Electronics and Information Technology (MeiTY) released the National Data Governance Framework Policy (NPD Framework) which was touted as the first building block of the digital architecture being conceived to maximize data.

Context:

• The role of digitization in realizing India’s vision of becoming a $5 trillion economy cannot be overstated.
• As per a NASSCOM report, data and artificial intelligence (AI) can add approximately $450-500 billion to India’s GDP by 2025.

Types of data:

• Personal Data – Data containing identifiers that can be used to identify specific individuals.
• Non-Personal Data (NPD)- data excluding personal data. It constitutes the primary type of citizen data obtained by the government and holds the potential to serve as a ‘public good’.

Significance of Non-personal data-

• NPD as a Public Good: NPD (Non-Personal Data) is considered the primary type of citizen data collected by the government. It holds the potential to serve as a ‘public good’, implying its utility and value to society as a whole.
• Integration of NPD in Public Services: Advocates for integrating NPD into the delivery of public services to create synergies and scalable solutions. Integration aims to enhance the effectiveness and efficiency of public service delivery.
• Application of Advanced Analytics and AI: Utilizing high-value advanced analytics and artificial intelligence (AI) on NPD can lead to predicting socially and economically beneficial outcomes. Such applications can span across various sectors of the economy.
• Key Sectors for Data-Driven Insights: Meteorological and disaster forecasts: Utilizing NPD to enhance predictions and preparedness for weather-related events and disasters. Infrastructure capacity and citizen use patterns: Understanding how citizens interact with infrastructure to optimize usage and planning.
• Mobility and housing patterns: Analyzing data to inform transportation and housing policies.
• Employment trends: Using NPD to predict and address changes in employment patterns and workforce needs.
• Informing Governance and Public Functions: NPD-driven insights can better inform decision-making in governance and public functions. Data analytics can provide valuable information for policy formulation and resource allocation.

Challenges related to NDP:

• Privacy and Security Concerns: The unprotected inter-flow of NPD across government departments, third parties, and citizens can lead to privacy breaches and make sensitive data vulnerable. This vulnerability can disproportionately benefit capacity-carrying actors such as Big Tech.
• Risk of Faulty Decision-making: Imperfect analysis of crucial public trends resulting from the exchange of NPD can lead to faulty decision-making. The inefficient exchange of data fails to unlock the power of interdisciplinary legislative and policy-making.
• Gaps in the NPD Framework: The NPD Framework lacks actionable guidance and practical operationalization, focusing on abstract high-level principles and objectives. It overlooks mechanisms for pricing data, appropriate legal structures for data exchange, and standardized governance tools.
• Lack of Legislation and Operationalization: While legislation is expected, the practical implementation and operationalization of the NPD Framework are overlooked. Questions remain unanswered regarding stakeholder rights and obligations across sectors.

Steps by Government:

• Agriculture Data Exchange in Telangana: Telangana State has developed an agriculture data exchange platform. The platform aims to facilitate the exchange of agricultural data among various stakeholders. It is likely designed to enhance decision-making, productivity, and innovation in the agriculture sector.
• India Urban Data Exchange (IUDX): The Ministry of Housing & Urban Affairs, in collaboration with the Indian Institute of Science, has established the India Urban Data Exchange (IUDX).
  • IUDX enables better urban planning, infrastructure development, and governance through data-driven insights.
• Data Exchanges for Geospatial Policy: The Department of Science & Technology has announced plans to establish data exchanges to implement aspects of the National Geospatial Policy.

Measures to address these challenges:

• Need for Critical Evaluation and Enhancement: A critical evaluation of the NPD Framework is necessary to address existing gaps. Enhancements to the framework can supplement MeiTY’s efforts to regulate NPD and facilitate interoperability across sectors.
•  Learn from International practice: countries like Australia, the UK, and Estonia highlight the adoption of data exchange frameworks and protocols. These frameworks have been applied across various sectors such as housing, employment, aged care, and agriculture to address specific issues like unemployment.
• Regulatory Design for Data Exchanges: Creating a regulatory design for data exchanges in India can digitize and automate public welfare functions. It can reduce administrative burden, facilitate inter-sectoral integration, and build safeguards for using and sharing NPD, making civic functions more participatory.
• Stakeholder Consultation: Engage stakeholders from government, industry, academia, and civil society in the evaluation process. Gather feedback on practical challenges faced in implementing the framework and areas needing clarification or enhancement.

Conclusion: A comprehensive evaluation and enhancement of the NPD Framework are imperative. Learning from international practices, establishing regulatory designs for data exchanges, and fostering stakeholder consultations will pave the way for effective governance of non-personal data.

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