The Hindu Editorial Analysis- 14th March 2023 | Current Affairs & Hindu Analysis: Daily, Weekly & Monthly - UPSC PDF Download

Context

In recent years, India has made great strides in its digital strategies and data governance. India has embraced technology and digitalisation to drive economic growth and to improve the lives of its citizens. However, as the country continues to evolve, it must also ensure that its digital strategies and data governance are inclusive, transparent, secure, and conducive to sustainable development.

What is DEPA?

• DEPA is a joint public-private effort for an improved data governance approach.
• DEPA creates a digital framework that allows users to share their data on their own terms through a third-party entity, who are known as Consent Mangers (In India, Account Aggregators).
• DEPA went live in the financial sector in 2020 under the joint leadership of the Ministry of Finance, the Reserve Bank of India (RBI), Pension Fund Regulatory and Development Authority (PFRDA), Insurance Regulatory and Development Authority (IRDAI), and Securities and Exchange Board of India (SEBI).

DEPA advantages

• DEPA is being seen as the next techno-legal solution that will empower users by giving them control over their data, allowing them seamless sharing and therefore inducing competition and enabling new services.
• Using DEPA, individuals and small businesses can use their digital footprints to access not just affordable loans, but also insurance, savings, and better financial management products.
• DEPA marks another step in a decade-long journey building digital infrastructure designed to improve private service delivery.

DEPA services

• Account Aggregators: The RBI-supported Account Aggregator (AA) mechanism follows the DEPA model, which allows a user to access their financial data at one place.
• Open Credit Enablement Network (OCEN): Another manifestation of DEPA has begun with the OCEN in the financial sector. OCEN allows a user to share their financial data from various sources, such as the Goods and Services Tax (GST) and the Tax Deducted at Source (TDS) return from the income tax system—to prove their creditworthiness without having to show assets

Data categorisation

• What is personal data?
  • Personal contains explicit information about a person’s name, age, gender, sexual orientation, biometrics and other genetic details.
• What is non-personal data? 
  • Any set of data that does not contain personally identifiable information can be classified as “non-personal data”.
    • Public non-personal data:
• All the data collected by the government and its agencies such as census, data collected by municipal corporations on the total tax receipts in a particular period or any information collected during execution of all publicly funded works has been kept under the umbrella of public non-personal data.
• Community non-personal data:
  • Any data identifiers about a set of people who have either the same geographic location, religion, job, or other common social interests will form the community non-personal data. 
  • For example, the metadata collected by ride-hailing apps, telecom companies, electricity distribution companies among others have been put under the community non-personal data category.
• Private non-personal data:
  • Private non-personal data can be defined as those which are produced by individuals which can be derived from the application of proprietary software or knowledge.

Challenges

• Data sharing with Big Techs:
  • Large repository of India-specific datasets that will be created under the National Data Governance Framework Policy (NDGFP) will not be available for commercial operations of Big Techs.
• Indian startups:
  • The repository of datasets will only be available for Indian startups.
  • There is no clarity on whether Indian startups meant those registered in India or those with operations in the country.
• Non-personal data identification:
  • Concerns are raised on the definition of non-personal data. 
  • The issue is, if there would be an 'absolute boundary' for defining non-personal data citing that many non-personal datasets when combined with other datasets can lead to identification. 
• Data centralisation:
  • The concerns are raised regarding the data centralisation aspect of the NDGFP when the world was moving towards decentralised frameworks like Blockchain.
• Operations of the IDMO:
  • As of now there is no clarity on the operations of the IDMO.

Way Ahead

• Data has grown to be an important economic and social resource. 
• In India, the government is the biggest data repository. 
• A policy framework is essential not only because currently there is no legal measure to protect non-personal data in the country, but also to streamline government data sharing which faces several bottlenecks today.
• The NDGFP demonstrates the Indian government's recognition of the immense value that can be unlocked through harnessing non-personal data.