Governance of Data Sharing in Agri-Food - towards common guidelines
Big Data is becoming a new asset in the agri-food sector including enterprise data from operational systems, sensor data, farm equipment data, etc. Recently, Big Data applications are being implemented, aiming at improving farm and chain performance. Many companies are refraining from sharing data because of the fear of governance issues such as data security, privacy and liability. Moreover, they are often in a deadlock or afraid to take the first step even though they expect to develop new business with data. To accelerate the development of Big Data applications, this paper analyses governance issues and introduces a set of guidelines for governance of data sharing in agri-food networks. A framework for analysis was derived from literature and used to identify lessons learned from recent projects or initiatives. From these results, a set of draft guidelines was developed. The framework and guidelines were evaluated in a workshop. The framework consists of factors that are related to governance on data sharing in networks. Internal factors are: efficiency, effectiveness, inclusiveness, legitimacy & accountability, credibility and transparency. External factors are: political, economic, social, technological, legal and environmental factors. For each of these factors, guidelines are provided in terms of: issues to be addressed, best practices and lessons learned from other projects and initiatives. It is concluded that the framework is complete in covering all relevant issues on governance in data sharing but the guidelines must be considered as a first set, which can be further improved and extended in the future. A wiki-type-of-website could help to upscale the guidelines at a global level. The guidelines could also be further refined accounting for different maturity levels of agri-food networks. The guidelines in this paper are considered to be a valuable step into the direction of solving governance issues in data sharing, which is expected to accelerate Big Data applications in the agri-food domain.
Governance of Data Sharing in Agri-Food - towards common guidelines
- 1. 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
- 2. 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
- 3. 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
- 4. 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
- 5. 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
- 6. 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
- 7. 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
- 8. 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
- 9. 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
- 10. 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
- 11. 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
- 12. 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
- 13. 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
- 14. Thank you for your attention Questions? Discussion? Contact: sjaak.wolfert@wur.nl
Editor's Notes
- Met de geschetste ontwikkelingen (IoT met name) wordt het mogelijk om grote hoeveelheden (big) data, real-time te verzamelen dit geeft ongekende mogelijkheden zoals: Risicomanagement (early warning, alerts, etc.) Allerlei vormen van bedrijfsvergelijking (benchmarking) Traceerbaarheid en ketentransparantie Ontwikkeling van geavanceerde dashboards ... (dingen die we nu nog niet kunnen verzinnen!) Op dit moment willen allerlei partijen hierop inspringen: Agri-food bedrijven bouwen hun eigen platforms (‘mijnBusiness.nl’) Op basis van de data die in die platforms zit, willen veel bedrijven en bedrijfjes (start-ups) innovatieve apps en services maken – dit is op zichzelf een goede ontwikkeling, maar... Gevolg: er ontstaat een wirwar aan platforms, apps, etc. die slecht met elkaar samenwerken de boer wordt geconfronteerd met ‘tig’ platforms waar ingelogd moet worden, etc. innovatie wordt juist geremd Oplossing: Ontwikkel een onderliggende open architectuur die de verschillende platforms, apps en services aan elkaar kan verbinden zodat er Een Open Software Ecosystem ontstaat waarin de verschillende stakeholders met elkaar samenwerken op basis van solide Platforms Afspraken aangaande security, privacy en trust Eerlijke verdienmodellen Goede nieuws: deze architectuur en organisatie is grotendeels al ontwikkeld! Wat moet er dan nog gebeuren? Een project ontwikkelen (PPS Data-FAIR) waarin via een aantal concrete pilots/trials deze architectuur geïmplementeerd en uitgebouwd kan worden rondom een aantal concrete platforms (zoals in de figuur aangegeven