Bringing Data to Market: Data Property as Economic Goods
Yuri Demchenko, Wouter Los, Cees de Laat (University of Amsterdam), Leon Gommans (KLM)

From FAIR Principles to STREAM Properties

Data Driven Economy and Data Markets(Leveraging FAIR data principles for Data commoditisation)

FAIR Principles in Research Data Management
Findable – Accessible – Interoperable – Reusable

STREAM Data Properties as Economic goods
Sovereign – Trusted – Reusable – Exchangeable – Actionable - Measurable
Sovereignty: Data sovereignty allows companies, data owners to remain
control over their data. It is important for business to enter the data market with
their proprietary business data
Trusted: Using data in decision making or in the processes control requires thatdata is trusted and verifiable. Trust in data is achieved by the whole process ofdata collection and by using verified models of the processes.

The establishment of the Open Data Markets is a necessary stage in making Digital
Single Market (DSM) in Europe reality• Facilitate the digital transformation of European economy• New trend related to Industry 4.0• Data driven technologies.
Factors to facilitate Data Markets• IoT sensor network and farms that continuously produce data that potentially may
be used by different organisations and produce secondary data that may haveadded value;• Use of personal data for advanced market research and services development;• Earth exploration data collected over years (such as from mining or oil/gascompanies) that can be also offered on the market;• Existing data archives which value may increase if data traded in more flexible andmeasurable way;
• Secondary data created from Open Data.

Reusability: Data reusability allow multiple uses of data, even if not for original
purposes data created. Data re-suability can create multiple opportunities fordata economy actors

Data Markets Components

Exchangeability: Data exchangeability ensures that data can be exchanged
between data producer and data consumer in general and be used for target
applications or intended purposes.
Actionability: Data must serve the business purposes and contain necessary
information to derive actionable decisions about operations or processes
optimisations
Measurability: Data measurability is used for data valuation and exchange as
economic goods, and a part of data handling on the data infrastructure
platforms.
Other Data properties for commoditisation and data trading and exchange for goods:• Quality, Value, Auditability/Trackability, Branding, Authenticity, as well as original
FAI(R)properties Findability, Accessibility, Interoperability.
• Special features that must be managed in all data transfer and transformation are data
ownership and IPR.
• The data property originated from its digital form of existence defined as not-Rivalry, on one
hand, makes data exchange (copying, distribution) easy, while protecting proprietary, privateor sensitive data or IPR

Data Lifecycle Workflow and Data Model Variability

Modern data architecture vs Data Market

Characteristics ofmodern dataarchitecture
1. Customer-centric
2. Automated
3. Smart
4. Adaptable, Agile
5. Cloud based
6. Elastic
7. Collaborative
8. Governed
9. Secure, Trusted

Data Reusability, Data Models Variability, Data staging and continuous storage(Data Lakes), Distributed infrastructure

Multiple Data Models and structures•Data Variety and Variability•Semantic Interoperability

Data Model (1)Data Model (1)Infrastructure is a necessary component of any service architecture. The following
are essential components of the Data Market infrastructure:• Architecture and conceptual model of the Data Market space, including
technological, organisational, legal and commercial aspects;• Shared/Federated infrastructure to access and operate the Data Market;• Federated hybrid cloud based Big Data infrastructure to support data storage,
processing and exchange in a secure and trusted way;
• DataHubs support for generic services for data suppliers such as caching,streaming, containerised delivery;• Support for on-demand connectivity and bandwidth provisioning between data
handling services/hosts in the data lifecycle;• Gateway based and computational enforcement of market policies and rules.

Data
Filter/Enrich,Classification

Data
Analytics,Modeling,Prediction

DataDelivery,
Visualisation

Consumer
applications

DataSource

Infrastructure – Catalog - Trust - Brokerage

Data Model (3)Data Storage (Big Data capable)

Data
Collection&Registration

1. Customer-centric
2. Automated
3. Smart
4. Regional/sectoral specialised
5. Cloud powered/integrated
6. Collaborative
7. Governed
8. Secure, Trusted
9. Auditable
10. Transparent
11. Commoditised/Monetised
12. Combining data and algorithms (aspart of containers)

Data Markets Infrastructure components

Data Model (4)Data (inter)linking•
PID/OID•
Identification•
Privacy, Opacity

Characteristics of emerging datamarkets

Data repurposing,
Analytics re-factoring,
Secondary processing

Data exchange protocols
Provided as a part of the Data Exchange and use layered model• Based on reliable and secure modern Internet protocols• Upper layer data exchange protocols• Network and cloud virtualisation technologies allow building
programmable virtualised networks as part of Virtual Private Clouds (VPC)and support data applications that produce and consume data and involve
data exchange internally inside secure VPC and with external parties.
• Mechanisms for effective and consistent data exchange and applications:
Persistent data Identifiers (PID), Data Factories, Metadata and data types
registry, data annotation and data discovery mechanisms

References
Yuri Demchenko, Wouter Los, Cees de Laat, Data as Economic Goods: Definitions, Properties, Challenges, Enabling
Technologies for Future Data Markets, ITU Journal: ICT Discoveries, Special Issue “Data for Goods”, To be published
December 2018
FAIR data [online] https://www.dtls.nl/fair-data/fair-data/
Data Management Maturity (DMM) Model, CMMI Institute, 2018 [online] https://cmminstitute.com/data-managementmaturity
White Paper Industrial Data Space [online] https://www.fraunhofer.de/content/dam/zv/en/fields-of-research/industrialdata-space/whitepaper-industrial-data-space-eng.pdf
Daniel Moody, Peter Walsh, Measuring the value of information: An asset valuation approach. Proc. European Conf on
Information Systems, ECIS’99 [online] https://www.semanticscholar.org/paper/ Measuring-the-Value-Of-InformationAn-Asset-Moody-Walsh/677d018aa724aef71e2ba4a363f7ba1748ea5bfe

Contact: Yuri Demchenko <y.demchenko@uva.nl>

Bringing Data to Market: Data Property as Economic Goods
Yuri Demchenko, Wouter Los, Cees de Laat (University of Amsterdam), Leon Gommans (KLM)

From FAIR Principles to STREAM Properties

Data Driven Economy and Data Markets(Leveraging FAIR data principles for Data commoditisation)

FAIR Principles in Research Data Management
Findable – Accessible – Interoperable – Reusable

STREAM Data Properties as Economic goods
Sovereign – Trusted – Reusable – Exchangeable – Actionable - Measurable
Sovereignty: Data sovereignty allows companies, data owners to remain
control over their data. It is important for business to enter the data market withtheir proprietary business data
Trusted: Using data in decision making or in the processes control requires thatdata is trusted and verifiable. Trust in data is achieved by the whole process ofdata collection and by using verified models of the processes.

The establishment of the Open Data Markets is a necessary stage in making Digital
Single Market (DSM) in Europe reality• Facilitate the digital transformation of European economy• New trend related to Industry 4.0• Data driven technologies.
Factors to facilitate Data Markets• IoT sensor network and farms that continuously produce data that potentially may
be used by different organisations and produce secondary data that may haveadded value;• Use of personal data for advanced market research and services development;• Earth exploration data collected over years (such as from mining or oil/gas
companies) that can be also offered on the market;• Existing data archives which value may increase if data traded in more flexible andmeasurable way;
• Secondary data created from Open Data.

Reusability: Data reusability allow multiple uses of data, even if not for original
purposes data created. Data re-suability can create multiple opportunities fordata economy actors

Data Markets Components

Exchangeability: Data exchangeability ensures that data can be exchanged
between data producer and data consumer in general and be used for target
applications or intended purposes.
Actionability: Data must serve the business purposes and contain necessary
information to derive actionable decisions about operations or processes
optimisations
Measurability: Data measurability is used for data valuation and exchange as
economic goods, and a part of data handling on the data infrastructure
platforms.
Other Data properties for commoditisation and data trading and exchange for goods:• Quality, Value, Auditability/Trackability, Branding, Authenticity, as well as original
FAI(R)properties Findability, Accessibility, Interoperability.
• Special features that must be managed in all data transfer and transformation are data
ownership and IPR.
• The data property originated from its digital form of existence defined as not-Rivalry, on one
hand, makes data exchange (copying, distribution) easy, while protecting proprietary, private
or sensitive data or IPR

Data Lifecycle Workflow and Data Model Variability

Modern data architecture vs Data MarketCharacteristics ofmodern dataarchitecture
1. Customer-centric
2. Automated
3. Smart
4. Adaptable, Agile
5. Cloud based
6. Elastic
7. Collaborative
8. Governed
9. Secure, Trusted

Data Reusability, Data Models Variability, Data staging and continuous storage(Data Lakes), Distributed infrastructure

Multiple Data Models and structures•
Data Variety and Variability•
Semantic Interoperability

Data Model (1)Data Model (1)Infrastructure is a necessary component of any service architecture. The following
are essential components of the Data Market infrastructure:• Architecture and conceptual model of the Data Market space, including
technological, organisational, legal and commercial aspects;• Shared/Federated infrastructure to access and operate the Data Market;• Federated hybrid cloud based Big Data infrastructure to support data storage,
processing and exchange in a secure and trusted way;
• DataHubs support for generic services for data suppliers such as caching,streaming, containerised delivery;• Support for on-demand connectivity and bandwidth provisioning between data
handling services/hosts in the data lifecycle;• Gateway based and computational enforcement of market policies and rules.

DataFilter/Enrich,Classification

Data
Analytics,Modeling,Prediction

DataDelivery,
Visualisation

Consumer
applications

DataSource

Infrastructure – Catalog - Trust - Brokerage

Data Model (3)Data Storage (Big Data capable)

DataCollection&Registration

1. Customer-centric
2. Automated
3. Smart
4. Regional/sectoral specialised
5. Cloud powered/integrated
6. Collaborative
7. Governed
8. Secure, Trusted
9. Auditable
10. Transparent
11. Commoditised/Monetised
12. Combining data and algorithms (aspart of containers)

Data Markets Infrastructure components

Data Model (4)Data (inter)linking•
PID/OID•
Identification•
Privacy, Opacity

Characteristics of emerging datamarkets

Data repurposing,
Analytics re-factoring,
Secondary processing

Data exchange protocols
Provided as a part of the Data Exchange and use layered model• Based on reliable and secure modern Internet protocols• Upper layer data exchange protocols• Network and cloud virtualisation technologies allow building
programmable virtualised networks as part of Virtual Private Clouds (VPC)and support data applications that produce and consume data and involve
data exchange internally inside secure VPC and with external parties.
• Mechanisms for effective and consistent data exchange and applications:
Persistent data Identifiers (PID), Data Factories, Metadata and data types
registry, data annotation and data discovery mechanisms

References
Yuri Demchenko, Wouter Los, Cees de Laat, Data as Economic Goods: Definitions, Properties, Challenges, Enabling
Technologies for Future Data Markets, ITU Journal: ICT Discoveries, Special Issue “Data for Goods”, To be published
December 2018
FAIR data [online] https://www.dtls.nl/fair-data/fair-data/
Data Management Maturity (DMM) Model, CMMI Institute, 2018 [online] https://cmminstitute.com/data-managementmaturity
White Paper Industrial Data Space [online] https://www.fraunhofer.de/content/dam/zv/en/fields-of-research/industrialdata-space/whitepaper-industrial-data-space-eng.pdf
Daniel Moody, Peter Walsh, Measuring the value of information: An asset valuation approach. Proc. European Conf on
Information Systems, ECIS’99 [online] https://www.semanticscholar.org/paper/ Measuring-the-Value-Of-InformationAn-Asset-Moody-Walsh/677d018aa724aef71e2ba4a363f7ba1748ea5bfe

Contact: Yuri Demchenko <y.demchenko@uva.nl>