governance of data sharing in agri-food - towards common guidelines
TRANSCRIPT
Governance of Data Sharing in Agri-Food Networks: towards common GuidelinesSjaak Wolfert, Marc-Jeroen Bogaardt, Lan Ge, Katrine Soma, Cor Verdouw
Forum on Food System Dynamics, 15 February 2017, Igls, Austria
Background and objective
(Big) Data is an upcoming issue in Agri-Food Several projects/initiatives started/starting on sharing
data between several stakeholders Governance and business models are a main hurdle that
has to be taken, especially in the starting phase
Objective: Prepare a set of guidelines for governance of data
sharing in agri-food networks
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What is governance?
General: interactions between actors and/or organization entities
aiming at the realization of collective goals
Two inter-related processes (Soma et al., 2016; Termeer et al., 2010): governing based on steering principles, on how to
influence a group of actors towards reaching collective goals
changing formal and informal institutional settings, which provide shifts in incentives for governing
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Governance issues on data in agri-food
Am I owning my own tractor? (IPR on software)?
Do I own my data? Who has access?
Does the government have insight?
Do certain companies get much power in the market?
Is there a lock-in situation? Can I transport my data?
Do I become a franchiser carrying the risks and limited returns?
Code of Conduct
See also: Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.-J., 2017. Big Data in Smart Farming – A review. Agricultural Systems 153, 69-80.
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Cloud DATA platform
The object system: projects/initiatives
E.g. Smart Dairy Farming
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Farmer
Supplier C
Supplier A
Supplier B
Customer X
feed
sperm milk
milking
robot
data
data
datadatadata
data
data
data
data
data
data
data
data
Network Administrative Organization
DATA-FAIR:Open Software
EcosystemStakeholders
PlatformsApps + services
Knowledge models
GovernanceBusiness models
Data sharing
DATA-FAIR – value creation by data sharing in agri-food business
Farmer
Open Architecture & InfrastructureEvent-driven, Configurable, Customizable
Standards & Open Datasets
Real-time data sharing
IoT layer
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Approach
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Scan literature data-sharing (in
Agri-Food)
Scan past and current projects on data-sharing
Agri-Food
Workshops
(Final) Guidelines
Scientific Paper
Draft Guidelines
Framework Governance
AspectsLiterature
review
Current results:This paper
DATA-SHARING
Framework for Governance of data sharingbased on literature, a.o. PESTLE framework
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Governing possibilities for data chain processes
(storage, transfer, transformation, analytics,
marketing)
Institutional Setting(formal rules, regulation & control, perceptions, trust,
motivation, encouragement)
Stakeholder Network
External factors
Political
Economic
SocialTechnological
Legal
Environmental
Efficiency
Effectiveness
InclusivenessLegitimacy & Accountability
Credibility
Transparency
Internal factors
DATA-SHARING
Framework for Governance of data sharingbased on literature, a.o. PESTLE framework
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Governing possibilities for data chain processes
Institutional Setting
Stakeholder Network
External factors
Political
Economic
SocialTechnological
Legal
Environmental
Efficiency
Effectiveness
InclusivenessLegitimacy & Accountability
Credibility
Transparency
Internal factors
• Agricultural policies• Restrictions on
cross-country information flows
• Resource use• Pollution• Climate change
• Data access• Digital divide
• Technological developments
• Security
• Regulations on privacy
• Public access• Consumer rights
• Demand/supply• Competition• Globalization
• Cost reduction• Profit increase
• Decision making• Response time
• Participation: voluntary or forced
• Enter/leave • Who makes
decisions
• Members’ feeling about decision-making structure
• Trust/support in management
• Ownership feeling
• Data Quality• Quality of use
• Communication• Organization of
data chain process• Quality of
effectiveness
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What are guidelines?
Issues that have to be addressed●Steps to be taken
Best practices with pro’s and con’s●Checklists ●If relevant, references to examples, templates,
etc. Lessons learned from and references to other
projects and initiatives
Legal
Issues Formal contracts are needed at
data level, personal level and product level.
Be aware of impacts of intellectual property rights.
Prepare for liability in case of data hacking.
Do not make the legal contracts too complicated; can be culture/ country dependent.
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Political
Environmental
SocialTechnological
Legal
Economic
Best practices Use a data code of practice
between stakeholders e.g.: New Zealand Farm Data Code of Practice BO-Akkerbouw: Gedragscode
Datagebruik Akkerbouw American Farm Bureau Federation:
Privacy and Security Principles for Farm Data
...
Lessons learned: NZ: code is used for awareness
raising, not as a formal contract Micheal Sykuta (2016):
● Codes can also mystify issues on data value, transparency, etc.
● Codes can obstruct new market entrants and innovation
● Data transparency can influence commodity markets
Conclusions and discussion
Scope of the framework seems to be complete, but can be further validated
Guidelines are a first attempt and should be extended/refined●For businesses: should not become too detailed or an
‘academic exercise’●Setup a (post-graduate) course?●WIKI-type of website – use power of the crowd
Framework could account for different ‘maturity levels’● focus more on start-up of networks (could be included in
factors e.g. ‘efficiency’)
Is the framework complete and appropriate? (see slide 5) Are the elements of the guidelines complete? (see slide 8)?
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Relationship with Blockchains
No 3rd party needed for Network Administrative Organization Distributed Automated Organization
●Higher transparency and credibility●No current agri-food/ICT player is dominating●Attractive/easy for small players to step in
(inclusiveness)●Less personal
Smart contracts: data is automatically exchanged according to pre-set agreements and rules
General: privacy and security can be better guaranteed ....more ideas are welcome
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