Governance of Data Sharing in Agri-Food - towards common guidelines

Governance of Data Sharing in Agri-Food
Networks: towards common Guidelines
Sjaak 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
2

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
3

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.
4

Cloud DATA platform
The object system: projects/initiatives
 E.g. Smart Dairy Farming
5
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
Ecosystem
Stakeholders
Platforms
Apps + services
Knowledge models
Governance
Business models
Data sharing
DATA-FAIR – value creation by data
sharing in agri-food business
Farmer
Open Architecture & Infrastructure
Event-driven, Configurable, Customizable
Standards & Open Datasets
Real-time data sharing
IoT layer
6

Approach
7
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
Aspects
Literature
review
Current results:
This paper

DATA-SHARING
Framework for Governance of data sharing
based on literature, a.o. PESTLE framework
8
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
Inclusiveness
Legitimacy &
Accountability
Credibility
Transparency
Internal factors

DATA-SHARING
Framework for Governance of data sharing
based on literature, a.o. PESTLE framework
9
Governing possibilities
for data chain processes
Institutional Setting
Stakeholder Network
External factors
Political
Economic
SocialTechnological
Legal
Environmental
Efficiency
Effectiveness
Inclusiveness
Legitimacy &
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

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
10

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.
11
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’)
12

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
13

Thank you for
your attention
Questions?
Discussion?
Contact:
sjaak.wolfert@wur.nl

Governance of Data Sharing in Agri-Food - towards common guidelines

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