Non Personal Data:

• Rapid digitisation of government operations is accompanied by increasing volumes of citizen data. Such data is typically of two kinds — Personal Data and Non-Personal Data (NPD).
• Any data which is not personal data (data pertaining to characteristics, traits or attributes of identity, which can be used to identify an individual) is categorised as non-personal data. 
• In terms of origin, non-personal data can be data which is never related to natural persons (such as data on weather or supply chains), or data which was initially personal data, but has been anonymised (through use of certain techniques to ensure that individuals to whom the data relates to cannot be identified).  
• Non-personal data can further be classified as: 
  • Public non-personal data: Data collected or generated by the government in the course of publicly funded works.  For example, anonymised data of land records or vehicle registration can be considered as public non-personal data.   
  • Community non-personal data: Raw or factual data (without any processing) which is sourced from a community of natural persons.  For example, datasets collected by municipal corporations or public electric utilities.  
  • Private non-personal data: Data which is collected or generated by private entities through privately owned processes (derived insights, algorithms or proprietary knowledge).
• Regulation of NPD in India is very less as compared to the personal data. To fill this gap ,the Ministry of Electronics and Information Technology (MeiTY) released the draft National Data Governance Framework Policy (NPD Framework) which was touted as the first building block of the digital architecture being conceived to maximise data-driven governance.

Applications of Non-Personal Data (NPD):

• Urban Planning: Analysing NPD related to infrastructure usage, mobility patterns, and housing trends to optimise city planning and development.
• Disaster Management: Utilising NPD such as meteorological data to forecast and prepare for natural disasters, ensuring effective response and mitigation strategies.
• Public Health: Leveraging NPD on disease outbreaks, healthcare access, and demographic trends to enhance public health policies and resource allocation.
• Employment Policies: Analysing NPD on employment trends, job vacancies, and skill requirements to formulate strategies for workforce development and job creation.
• Transportation Management: Using NPD on traffic flow, public transportation usage, and commuter patterns to improve transportation infrastructure and services for citizens.

Issues with the Draft NPD Framework:

• Abstract Principles without Tangible Guidance: The framework formulates abstract high-level principles and objectives for NPD governance but lacks tangible, actionable guidance to achieve them.
• Lack of Legislation and Operationalization: While legislation for NPD governance is expected, practical operationalization is overlooked, leaving questions unanswered regarding stakeholder rights and obligations across sectors.
• Absence of Pricing Mechanisms: Mechanisms for pricing of data and are not addressed in the framework.
• Missing Standardised Governance Tools: The absence of standardised governance tools compounds challenges in implementing the framework effectively.
• Limited Scope for Inter-Sectoral Integration: The framework’s limitations hinder effective inter-sectoral integration of NPD, which is crucial for maximising its potential benefits.
• Data Exchange issues: Appropriate legal structures for data exchange are inadequate. This can lead to unprotected inter-flow of NPD across government departments, third-parties, and citizens causing inefficiencies and vulnerabilities.
• Lack of Clarity on Stakeholder Rights and Obligations: The framework lacks clarity on stakeholder rights and obligations, which is essential for fostering trust and cooperation in data exchanges.
• Inadequate Safeguards against Privacy Breaches: The framework does not provide sufficient safeguards against privacy breaches, leaving sensitive aspects of NPD vulnerable, particularly in the context of increasing digitization of public services.
• Need for Regulatory Design and Enforcement: There is a need for a regulatory design for data exchanges in India to address these issues comprehensively and ensure effective governance of NPD.

What is Data Exchange?  the process and the platform:

• • Data exchange refers to the process of sharing and transferring data between different organisations, systems, or applications. 
  • It involves exchanging data in a structured format, typically using standardised protocols and interfaces, to enable seamless data integration and interoperability between disparate systems.

• Data exchange platform is a software platform which facilitates the process of data exchange. 

• These exchanges serve as a centralised platform for companies to share, access, and monetize data assets with other organisations, while maintaining control over their data privacy and security.
• In India, the State of Telangana has designed an agriculture data exchange, while India Urban Data Exchange has been established by the Ministry of Housing & Urban Affairs in collaboration with the Indian Institute of Science. Similarly, the Department of Science & Technology has announced its intention to set up data exchanges to implement aspects of the National Geospatial Policy.

Steps to address these concerns:

• Develop and operate a comprehensive legislation specifically addressing NPD governance, including clear guidelines for its collection, use, sharing, and protection.
• Define pricing mechanisms for NPD to ensure fair compensation for data contributors and encourage participation in data exchanges.
• Establish legal structures for data exchange, including frameworks for data ownership, licensing, and liability to facilitate secure and transparent data sharing.
• Strengthen privacy and security safeguards through robust encryption, anonymization, and data protection measures to safeguard sensitive information and uphold citizen trust in data exchanges.
• Foster collaboration and partnerships among different stakeholders for knowledge sharing, capacity building, and best practice exchange to maximise the value of NPD across diverse sectors and enhance decision-making processes.

A critical evaluation of the NPD Framework to address the existing gaps will be beneficial. This will supplement MeiTY’s effort to regulate NPD and will help forge data exchanges as suitable media to make NPD interoperable across sectors.