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A framework for data sharing for open innovation

A framework for data sharing for open innovation
A framework for data sharing for open innovation
Motivation
The data assets of organisations are under-exploited. Data sharing to multiple users, allowing organisations to provide permissioned access to datasets so that others can find value, are one way to exploit that potential. Existing mechanisms, such as opening data, data commons or data marketplaces do not necessarily mean value will be created for the data holder, and therefore may not provide a complete opportunity for open innovation.

Objective
We seek to develop a framework that identifies the conditions which enable value to be created through a data sharing format.

Theory
Over the past decade the growth of open data and scientific data commons has made innovating with third party data more accessible. However, the data that can be shared openly is limited, and commons call for data pooling and constraints on value capture and direction. We suggest that data sharing from one or more holders to multiple users provides the greatest potential for open innovation with data. The key aspects of data sharing must therefore be defined.

Methods
By comparing 4 different types of data access – commercial marketplaces, data commons, open data and data sharing - we identify key points of difference. Each of these is developed into a dimension in a data sharing for open innovation framework.

We then analyse 4 data sharing implementations against this initial framework. Two of these data sharing instances (Horizon2020 project Data Pitch and Interreg project SCIFI) explicitly identify as ‘open innovation’ programmes. One (European Data Incubator) utilises a challenge and funnel methodology. The final case study, the Turing Data Study groups, offers ‘challenge owners’ the opportunity to have their data worked on by university data scientists in intensive ‘collaborative hackathons.’

Findings
In the initial framework we identify 5 key dimensions of data sharing for open innovation. At its core, any decisions concerned with sharing data have to consider two aspects: the rights and consents attached to the data, which steer how the data may be used; and the potential or suggested uses of the data, including the parties acting as data controllers or processors. The combination of the two defines the space of possible uses of the data, which direct the related actions, here named ‘purpose’.



Purpose
The purpose of the use of data must comply with the purposes consented when the data was collected, which requires a mechanism that enables this to be approved.
Access
How the data is physically accessed and shared. It may be that the data is sensitive and may only be accessed through specific platforms. The time period for sharing must be defined, and the end of the period managed. Reducing the movement of data must also be considered.
Permission
The legal framework for accessing the data. This may be a license that is granted, a legal trust, a contract or an intellectual property agreement.
Privacy
Compliance with GDPR in terms of pseudonymisation/anonymization where necessary.
Value
Innovation in goods, services

Fig 1: Dimensions and Roles in a Data Sharing Framework

Testing against the 4 case studies we find that in order to accurately capture how data sharing supports open innovation it is necessary to subdivide ‘value’ to establish that the locus of value is in two or more places (data holder, data provider, customers/clients of both/either). We also define the ‘value instrument’, which is the core of the decision-making process for assessing eligibility of data users for open innovation engagement.


Conclusion
These seven dimensions provide a framework for developing open innovation using data sharing, whether that be through existing mechanisms such as competitive challenges, incubators or hacks, or emerging formats such as data trusts.
112-113
Walker, Johanna, Catherine
aef93dc8-1936-4dd8-9921-64bd811b4a01
Carr, Leslie
0572b10e-039d-46c6-bf05-57cce71d3936
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67
Walker, Johanna, Catherine
aef93dc8-1936-4dd8-9921-64bd811b4a01
Carr, Leslie
0572b10e-039d-46c6-bf05-57cce71d3936
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67

Walker, Johanna, Catherine, Carr, Leslie and Simperl, Elena (2019) A framework for data sharing for open innovation. 18th Open and User Innovation Conference<br/>, Utrecht, Netherlands. 08 - 10 Jul 2019. pp. 112-113 .

Record type: Conference or Workshop Item (Other)

Abstract

Motivation
The data assets of organisations are under-exploited. Data sharing to multiple users, allowing organisations to provide permissioned access to datasets so that others can find value, are one way to exploit that potential. Existing mechanisms, such as opening data, data commons or data marketplaces do not necessarily mean value will be created for the data holder, and therefore may not provide a complete opportunity for open innovation.

Objective
We seek to develop a framework that identifies the conditions which enable value to be created through a data sharing format.

Theory
Over the past decade the growth of open data and scientific data commons has made innovating with third party data more accessible. However, the data that can be shared openly is limited, and commons call for data pooling and constraints on value capture and direction. We suggest that data sharing from one or more holders to multiple users provides the greatest potential for open innovation with data. The key aspects of data sharing must therefore be defined.

Methods
By comparing 4 different types of data access – commercial marketplaces, data commons, open data and data sharing - we identify key points of difference. Each of these is developed into a dimension in a data sharing for open innovation framework.

We then analyse 4 data sharing implementations against this initial framework. Two of these data sharing instances (Horizon2020 project Data Pitch and Interreg project SCIFI) explicitly identify as ‘open innovation’ programmes. One (European Data Incubator) utilises a challenge and funnel methodology. The final case study, the Turing Data Study groups, offers ‘challenge owners’ the opportunity to have their data worked on by university data scientists in intensive ‘collaborative hackathons.’

Findings
In the initial framework we identify 5 key dimensions of data sharing for open innovation. At its core, any decisions concerned with sharing data have to consider two aspects: the rights and consents attached to the data, which steer how the data may be used; and the potential or suggested uses of the data, including the parties acting as data controllers or processors. The combination of the two defines the space of possible uses of the data, which direct the related actions, here named ‘purpose’.



Purpose
The purpose of the use of data must comply with the purposes consented when the data was collected, which requires a mechanism that enables this to be approved.
Access
How the data is physically accessed and shared. It may be that the data is sensitive and may only be accessed through specific platforms. The time period for sharing must be defined, and the end of the period managed. Reducing the movement of data must also be considered.
Permission
The legal framework for accessing the data. This may be a license that is granted, a legal trust, a contract or an intellectual property agreement.
Privacy
Compliance with GDPR in terms of pseudonymisation/anonymization where necessary.
Value
Innovation in goods, services

Fig 1: Dimensions and Roles in a Data Sharing Framework

Testing against the 4 case studies we find that in order to accurately capture how data sharing supports open innovation it is necessary to subdivide ‘value’ to establish that the locus of value is in two or more places (data holder, data provider, customers/clients of both/either). We also define the ‘value instrument’, which is the core of the decision-making process for assessing eligibility of data users for open innovation engagement.


Conclusion
These seven dimensions provide a framework for developing open innovation using data sharing, whether that be through existing mechanisms such as competitive challenges, incubators or hacks, or emerging formats such as data trusts.

Full text not available from this repository.

More information

Published date: 7 August 2019
Venue - Dates: 18th Open and User Innovation Conference<br/>, Utrecht, Netherlands, 2019-07-08 - 2019-07-10

Identifiers

Local EPrints ID: 434708
URI: https://eprints.soton.ac.uk/id/eprint/434708
PURE UUID: 1da18278-331c-44ef-8a5f-2df57cf0ac87
ORCID for Johanna, Catherine Walker: ORCID iD orcid.org/0000-0002-5498-8670
ORCID for Leslie Carr: ORCID iD orcid.org/0000-0002-2113-9680
ORCID for Elena Simperl: ORCID iD orcid.org/0000-0003-1722-947X

Catalogue record

Date deposited: 07 Oct 2019 16:30
Last modified: 08 Oct 2019 00:57

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