Shaping a Data Economy

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Shaping a Data Economy

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• Authors: MURAT SONMEZ
• Published: December 1, 2020

Harnessing data’s economic power and the Fourth Industrial Revolution

• Data fuels AI, precision medicine, robotics, and the Internet of Things and will determine the success or failure of the 4IR.
• Technology adoption accelerated during the global pandemic:
  • "More than 80 percent of business executives are accelerating plans to digitalize work processes and deploy new technologies."
  • "By 2025, employers will divide work equally between humans and machines."
• The valuation of technology companies demonstrates data’s economic value; a transparent and equitable mechanism could unlock value for individuals and organizations while protecting privacy.

Four-tiered "data operating system" (data OS) — core policy recommendations

• Reimagine notice and consent mechanisms:
  • Allow owners of data to specify purposes, duration, and payment rights.
  • Attach rules to data sets (analogous to media digital rights management) so data "cannot be used out of bounds."
• Certification mechanism for applications using data sets:
  • Certify data-mining algorithms and applications via a trusted agency (a 4IR "app store") for compliance with consent protocols and restrictions.
• Transparent mechanism for valuation of data:
  • Treat data as a tradable asset; use market forces to price it for specific uses.
  • Recognize that data can be used repeatedly and for a variety of purposes, unlike single-use commodities.
• Cross-border data flow and digital remittance mechanism:
  • Countries could form bilateral treaties to share data for agreed purposes and pool data assets via a secure mechanism.
  • Facilitate cross-border payments digitally, ensure timely payment to owners, and align taxation: owners pay taxes on income received; users pay taxes at consumption in their jurisdictions.

Expected benefits and systemic effects

• Continuous income stream for individuals from data.
• Companies can mark-to-market data assets on balance sheets, creating assets benefiting stakeholders.
• Early detection and prevention: the data OS could "reverse-engineer breakdowns and glitches before they occur," alerting to risks and guiding responses.
• If designed correctly, the system could boost economic growth and minimize negative societal impacts.

Case study 1 — Treating rare diseases

• Scope and challenge:
  • "Worldwide, 400 million are affected by a rare disease, more than cancer and AIDS combined."
  • "There are 7,000 identified rare diseases so far."
  • National research approaches fall short due to limited national pools of data and lack of cross-border awareness of treatments.
• Opportunity in genomic testing:
  • "With an estimated 15.2 million people expected to have clinical genomic testing for a rare condition within the next five years," a global sharing system is urgent.
• Technical and governance solution:
  • Federated database system: autonomous databases interconnect without merging; users access voluntarily shared information through a uniform interface while each data set remains under local control and security.
• Risks and safeguards:
  • Weak genomic data policies risk extraction and misuse of genetic and biological information.
  • Ethical policies, regulations, and standards are needed to support research and guard against abuses.
• Conclusion: "A federated data system ticks all four boxes of the data OS and has the potential to accelerate benefits safely and to all of society."

Case study 2 — Feeding the world (agriculture and food systems)

• Scale of the food challenge:
  • "Current unsustainable agricultural practices could lead to the degradation of 95 percent of the world’s land by 2050."
  • "Some 2 billion people do not have access to safe, nutritious, and adequate food."
• Examples of AI in agriculture and food:
  • Prospera collects "50 million data points from 4,700 fields every day" to identify pest and disease outbreaks and improve yields, reduce pollution, and eliminate waste.
  • NotCo and Fazenda Futuro use AI to develop plant-based meats; Firmenich introduced "the world’s first flavor made entirely with AI."
  • Meat production accounts for "almost 50 percent of the world’s agricultural emissions."
• Policy implication:
  • Data trapped behind borders limits global solutions; a data OS and defined data ownership could enable cross-border scaling and financial rewards for data sharing.

Case study 3 — Building trust with blockchain and distributed ledgers

• Trust is fundamental for cross-border and cross-industry data use; authenticity and integrity of data are essential.
• Blockchain/distributed ledger characteristics:
  • Tamper-proof time-stamped transactions, peer-to-peer security, transparency, smart contracts, and tokens.
• Practical pilots and applications:
  • StaTwig piloted blockchain to track vaccine deliveries to children.
  • Anheuser-Busch InBev used blockchain in Zambia for transparent pricing of locally sourced crops such as cassava.
  • Colombia is exploring blockchain to improve oversight in public procurement and reduce corruption.
• Role: Blockchain is a promising platform to ensure data is trustworthy and accurate, enabling broader expansion of data uses.

Governance, inclusivity, and the path forward

• 4IR technologies are evolving without guidelines; future-oriented, enabling policies are required.
• Multi-stakeholder cooperation needed: government officials, business leaders, civil society, and international organizations.
• By acting now and adopting the data OS framework, governments can:
  • Keep economies competitive.
  • Boost citizens’ well-being.
  • Build trust and accelerate beneficial progress.
  • Support a sustainable recovery that provides fair access to global market opportunities.

Shaping a Data Economy — Murat Sonmez, F&D Magazine, December 2020

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