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1 Dyson | Cornell SC Johnson College of Business
Data Market Strategy?value of datadata marketsblockchains for data
Aija Leiponen, Cornell UniversityPantelis Koutroumpis, Oxford UniversityLlewellyn Thomas , LaSalle Universitat Ramon Llull
2 Dyson | Cornell SC Johnson College of Business
(1) Determinants of data value
3 Dyson | Cornell SC Johnson College of Business
Determinants of data value• Metadata – what are the data about?
• Provenance – how were the data created, by whom?
• In/alienability – what/who are the data connected to? To whom are there ongoing implications?
• Connected data – what data complement these data?
• Judgment/models/analyses – how can insights be inferred from data?
• Context – where/how are the data used?
Value of data depends on several complementary (digital) assets
4 Dyson | Cornell SC Johnson College of Business
Data value appropriation –excludability
How to appropriate value (profit) from data? I.e. what aspects of data can generate market power?
– Control the data resource– Control the metadata or format/standard– Control the connected data– Control the analytical tools, models, intelligence– Control the enabling platform
5 Dyson | Cornell SC Johnson College of Business
How to control data AND maximize its value?
• Practically NO (Intellectual) Property Rights for data• Secrecy – embed data in a service
can’t license data itself
• Database right (EU) – prevent others from selling the whole database or substantial parts thereof
doesn’t apply to small subsets? difficult to enforce? No precedent outside of betting data.
• Contracts – license the data via contractual agreementcan sue for contractual breach; not prevent third parties from using data
• Verification technologies – attach a Distributed Ledger to the data and trackits trading
works 100% with parties who care about provenance. maybe not with others
• Closed network of partners – share data within a consortium through a combination of contracts, trust, reputation effects, monitoring, consortium rules
small network/market in order to effectively monitor & governno broader legal recourse in case of breach
6 Dyson | Cornell SC Johnson College of Business
Collateral Analytics v Nationstar Mortgage(N.D. Cal., No 18-cv-019):
Collateral Analytics offers a searchable database of real estate information to its customers. According to the complaint, Nationstarwas one of the customers and abused that relationship by downloading much of the database and is preparing to use that information to start a competitor (Quantarium).
7 Dyson | Cornell SC Johnson College of Business
(2) Data Market Design
• Market efficiency requires (A. Roth)– Thickness/liquidity– Low transaction costs– Limited strategic behavior by participants
• Provenance• Excludability
– Stable matching: there are no more preferred potential matches
– Lack of “repugnance” (appropriateness/fairness)
8 Dyson | Cornell SC Johnson College of Business
Types of market matching mechanisms
Matching Marketplace design
Terms of Exchange
Examples
One-to-one 1. Bilateral Negotiated Data brokers: Acxiom
One-to-many 2. Dispersal Standardized Twitter API
Many-to-one 3. Harvest Implicit barter Google Services
Many-to-many 4. Multilateral“platform”
Standardized or negotiated
InfoChimps, Microsoft Azure
9 Dyson | Cornell SC Johnson College of Business
Data market design
Marketplace design
Liquidity Transaction costs
Provenance Excludability
Bilateral Low High Clear Medium Dispersal High Low Medium Low Harvest High Low Variable Low Multilateral High Low Medium Low
Market liquidity and stability inversely related to transaction costs and excludability (strategic behavior)
With current data market mechanisms, you can achieve large markets with little control or small markets with greater control
Marketplace design
Liquidity
Transaction costs
Provenance
Excludability
Bilateral
Low
High
Clear
Medium
Dispersal
High
Low
Medium
Low
Harvest
High
Low
Variable
Low
Multilateral
High
Low
Medium
Low
10 Dyson | Cornell SC Johnson College of Business
(3) How might DLTs influence data markets?
DLTs lower the cost of verification and networking (Catalini & Gans)
Could enhance clarity of provenance and access to complementary digital goods
11 Dyson | Cornell SC Johnson College of Business
Example:
11
• Decentralized low-cost data storage protocol that can scale
• Pay $$$ once to store data permanently; provide storage and retrieval access in exchange for payment share.
• Proof-of-access consensus mechanism
• Focus on administering (permanent and distributed) storage and access
• Applications – Proving authenticity of documents or IP (DE court: blockchain evidence is admissible)– Journal of Raw Data – permanent biomedical research data repository
Creates an immutable record of access and changes to digital assets
What about out-of-network sharing?
12 Dyson | Cornell SC Johnson College of Business
Data and currencies differ in terms of intrinsic (use) value
12
• Value of cryptocurrencies depends on the state of the network/market
• Value of data depends BOTH on the market and demand for the content and its complements
• When the intrinsic value of data grows, incentive to use/trade/share off-ledger grows too
• No property rights for data; currently difficult to enforce (smart) contracts about taking data off ledger. Analytics require offline manipulation.
• Data may not be unique suppliers may claim independent collection
DLT-based data markets work iff incentives to trade on-ledger exceed those to trade off-ledger
Penalties for trading off ledger? Relationship breach. Shrug. (cf. CA)
13 Dyson | Cornell SC Johnson College of Business
Efficiency of multilateral marketplaces?
Marketplace design
Liquidity Transaction costs
Provenance Excludability
Multilateral Centralized High Low Medium Low
Multilateral Decentralized with DLT
High Low/ Medium Clear ??
Collective action/ consortium
Medium/ low High Clear Medium
Distributed Ledger Technologies could conceivably enable large-scale,
anonymous multilateral data markets by authenticating provenance
BUT only for on-ledger tradingNo solution to excludability!
Marketplace design
Liquidity
Transaction costs
Provenance
Excludability
Multilateral Centralized
High
Low
Medium
Low
Multilateral Decentralized with DLT
High
Low/ Medium
Clear
??
Collective action/
consortium
Medium/ low
High
Clear
Medium
14 Dyson | Cornell SC Johnson College of Business
Conclusions
• Data goods substantially differ from other intangible goods in terms of how their value is affected by:– Provenance (metadata)– Excludability (protection)
• Trading regimes: secrecy & trust or verification technology (DLT) – or ‘FREE’– Bilateral trading sets up a complex relationship with
remedies, audits, subscriptions as contractual features– Decentralized Multilateral based on DLT – solves
provenance and authorization of on-ledger sharing but not excludability off-ledger
15 Dyson | Cornell SC Johnson College of Business
Aija [email protected]
16 Dyson | Cornell SC Johnson College of Business
16
17 Dyson | Cornell SC Johnson College of Business
Cambridge Analytica• Cambridge University researcher Aleksandr
Kogan created a personality quiz “thisismydigitallife” for academic purposes
• App acquired user consent to share their profiles and their friends’ profiles with the app
• App data were shared with CA
• Social networks of participants were shared with CA
– Up to 87 million US users of Facebook
• FB finds out contract had been breached in 2015
• NOT CLEAR IF ANY OF THIS WAS ILLEGAL or CRIMINAL
18 Dyson | Cornell SC Johnson College of Business
There is no ownership of data!
19 Dyson | Cornell SC Johnson College of Business
Communication revolutions• Printing press• Steam engine• Telegraph • Telephone• Radio• Television
• Networked data? IoT?
Data Market Strategy?�value of data�data markets�blockchains for data(1) Determinants of data valueDeterminants of data valueData value appropriation – excludability How to control data AND maximize its value?Slide Number 6(2) Data Market DesignTypes of market matching mechanismsData market design(3) How might DLTs influence data markets?Example:Data and currencies differ in terms of intrinsic (use) valueEfficiency of multilateral marketplaces?ConclusionsSlide Number 15Slide Number 16Cambridge AnalyticaThere is no ownership of data!Communication revolutions