## shaping-a-data-economy-wef-sonmez

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### Overview
- The Fourth Industrial Revolution (4IR) is driven by computing power and connectivity, producing technologies such as AI, robotics, and the Internet of Things that fuse physical and digital worlds.
- 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."
- Central questions: Who owns data? What can you do with it? Who derives the economic benefits?

### Proposed data operating system (data OS) — four-tiered approach
- Goal: enable transparent and equitable mechanisms to unlock economic value of data while protecting privacy.
- Four components:
  - Reimagined notice and consent mechanisms:
    - Allow owners to specify permitted purposes, duration, and payment entitlements.
    - Rules can be attached to data sets (analogous to media digital rights management) to prevent out-of-bounds use.
  - Certification mechanism for applications:
    - A trusted agency to certify applications and their compliance with consent protocols and restrictions (a 4IR app store concept).
    - Ensures data-mining algorithms are trustworthy.
  - Transparent valuation mechanism:
    - Treat data as a tradable asset with prices driven by supply and demand.
    - Market-based pricing for specific uses via an exchange mechanism.
    - Recognizes that, unlike single-use commodities, data can be reused for multiple purposes.
  - Cross-border data flow and digital remittance mechanism:
    - Bilateral treaties to share data across borders for agreed purposes, pooling data assets securely.
    - Digital cross-border payments to ensure timely payment to data owners.
    - Taxation: owners pay taxes as they receive income; users pay taxes at consumption, producing transparent taxation and a new government revenue source.
- Potential outcomes:
  - Continuous income stream for individuals.
  - Companies can mark-to-market data holdings as balance-sheet assets.
  - Early detection of system breakdowns and risks, enabling preventative responses.
  - Boost to economic growth while minimizing negative societal impacts.

### Case studies illustrating benefits and gaps
- Treating rare diseases
  - Context and scale:
    - "Worldwide, 400 million are affected by a rare disease, more than cancer and AIDS combined."
    - "There are 7,000 identified rare diseases so far."
  - Challenges:
    - National approaches fail due to limited, fragmented data; treatments in one country may be unknown elsewhere.
  - Opportunity:
    - With "an estimated 15.2 million people expected to have clinical genomic testing for a rare condition within the next five years," a federated database system could interconnect autonomous databases without merging them.
    - Federated databases allow voluntary sharing via a uniform interface while keeping each data set under local control and security.
    - Ethical policies, regulations, and standards are needed to prevent misuse and mishandling of genomic data.
  - Fit with data OS:
    - A federated system addresses consent, certification, valuation, and cross-border flows as outlined in the data OS framework.

- Feeding the world
  - 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."
  - 4IR contributions:
    - Agricultural AI can improve yields, reduce pollution, and eliminate waste by training on large datasets.
    - Example: Prospera collects "50 million data points from 4,700 fields every day" to identify pest and disease outbreaks and boost yields.
    - Plant-based meat innovations: companies such as NotCo and Fazenda Futuro use AI to analyze plant data; Firmenich introduced the first flavor made entirely with AI.
    - Meat production accounts for "almost 50 percent of the world’s agricultural emissions," so plant-based transitions have large environmental benefits.
  - Policy implication:
    - Data often trapped behind borders; a data OS with defined ownership and rewards could enable global scaling of food solutions.

- Building trust
  - Trust is foundational for cross-border and cross-industry data sharing.
  - Blockchain/distributed ledger technology can provide tamper-proof, time-stamped transaction records, peer-to-peer security, transparency, smart contracts, and tokens.
  - Examples of blockchain use:
    - StaTwig piloted blockchain to track vaccine deliveries to children.
    - Anheuser-Busch InBev used blockchain in Zambia to facilitate transparent pricing for cassava.
    - Colombia is exploring blockchain to improve oversight in public procurement.
  - Blockchain is nascent but promising for ensuring data authenticity and trustworthiness.

### Policy recommendations and governance implications
- Adopt forward-looking, enabling policies rather than backward-looking, punitive measures.
- Implement the data OS framework to:
  - Protect privacy through robust consent mechanisms and ethical standards.
  - Certify trustworthy applications and algorithms via an independent authority.
  - Create transparent markets or exchanges to value data for specific uses.
  - Facilitate cross-border data sharing with digital remittance and clear taxation rules.
- Promote cooperation among government officials, business leaders, civil society, and international organizations to ensure 4IR benefits people and the planet.
- Action now can keep economies competitive and support a sustainable recovery that provides fair access to global market opportunities.

*Murat Sonmez, "Shaping A Data Economy," Finance & Development, December 2020.*

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_Source: https://www.imf.org/-/media/files/publications/fandd/article/2020/december/shaping-a-data-economy-wef-sonmez.pdf_