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:

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• 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

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• 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 Leiponenaija.leiponen@cornell.edu

16 Dyson | Cornell SC Johnson College of Business

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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