We Need to Define Who gets Access to What Type of Data

Context: Government-appointed Committee for non-personal data governance, which released its draft report in July, has called out some systemic flaws.

Problems with existing data governance

• Driven by a market-centric approach

• Based on the concepts of data ownership, and treat data as property. 

• This leads to concentration of profits and power in the hands of a few 

• Value of data remains trapped in silos, For, E.g. healthcare data.

Some findings of Committee

• Recognized the necessity for addressing collective harms
• Suggested ways to unlock advances in AI and public interest.

Three main areas that require a pragmatic rethink by the Committee.

• Reconsider mandatory data sharing across all sectors: 

• Challenges due to mandatory data sharing includes – 

• It distorts the business incentive structures, creates a first-mover disadvantage and raises the compliance burden.

• Likely to upend the incentives for innovation.

• Likely to increase with the need for extensive reporting and responding to data requests.

• It has been termed as -  “appropriation of intellectual property” and “nationalization of data”.

• The goal of data sharing for public interest can be met by creating a bottom-up, narrow and well-defined framework for who gets access to what type of data and why.

• For, E.g. it is reasonable for private charter buses that operate public transport to be mandated to share data for urban planning, as part of licensing requirements. 
• The UK’s Bus Services Act of 2017 helped implement this and made data available to improve commuting across the country.
• Legislation for portability for data—similar to mobile number portability— can help aid competition and incentivize innovation, without imposing high costs.

• Need for a policy framework for data trusts:

• The report introduces the concept of trustees and data trusts without clearly defining the role, accountability and legal basis of such institutions. 
•  The Committee should recommend sandbox experiments at academic centres that can help create policy and tech framework for data trusts.

• Arriving at a fair valuation of Data: The Committee’s conclusions on the valuation of data are inconsistent and unnecessary.

• While the report rightly reasoned over the complexity and untested framework for data price discovery, it nevertheless recommends creating a digital marketplace for the same. 
• Non- adequately account for costs of liabilities such as re-anonymization and privacy breach
• The recommended compensation on the basis of FRAND (fair, reasonable and non-discriminatory) is not a tested methodology for data markets;

Conclusion: India’s goal of unlocking the value of non-personal data can benefit from transparent consultations on foundational questions related to the value of data, incentives in data markets, competition laws, the role of trusteeship, data trusts, communities and a framework for prioritization of public interest use cases.