Review of "Data revolution: emerging new business models in the agri-food sector"
This post discusses the different business models that might be relevant data governance options for improving European agri-business, as reported from a recent symposium. The author believes the discussion is relevant to other disciplines and industries as well given the universal importance of supply chain models, fundamental similarities in how data are managed, and the role of data access in fostering innovation.
This is a brief review of and commentary of the seminar report EIP-AGRI seminar ‘Data revolution: emerging new business models in the agri-food sector’.
This report discusses the findings and recommendations of an EU-focused seminar convened in Bulgaria in June 2016. The seminar considered how best to take advantage of advances in information and communication technologies (ICT) in agriculture. The report discusses various "data driven business models" and how they relate to innovations and improvements throughout the entire agricultural supply chain "from farm to fork."
Different business models are discussed as well as (1) the role of data and ICT in promoting innovation, (2) factors that may limit taking advantage of increasing data availability, and (3) potential actions that trigger improvements in agriculture based on improved data availability and use.
Why this approach matters
This report and the symposium focused on "agri-business." Given that focus, are the contents of this report also relevant to other endeavors involving data such as health care, financial regulation, environmental research, or transportation?
I think so, for several reasons:
- Business models are important regardless of industry. A serious analysis of business models requires consideration of the factors -- economic and otherwise -- that influence performance of the system. That's a good thing, especially when the financial motivations of different groups can influence outcomes.
- Data supply chains are useful constructs. How system participants work together can make or break how a system performs. Adopting a supply chain model or a data lifecycle model can help surface different choke points and critical path considerations. That's true whether we are discussing on getting food on the table or delivering better health care.
- Different disciplines do share fundamental similarities. I learned this in my first research project which examined the communication behaviors of cancer researchers and astrophysicists. The structures of the two areas were significantly different yet the motivations of participants were remarkably similar.
- Innovation should be encouraged not discouraged. One way to foster innovation is to ask the question, "How would someone in a different industry (or research area, or marketplace, etc.) address this same type of problem?"
Issues discussed by the report
The report concentrates on EU-related agriculture. However, the issues discussed will be familiar to anyone working in data governance and open data. These include:
- The need for strategy to help guide action.
- The value of taking action without waiting for development of a comprehensive strategy.
- The benefits of a "supply-chain" approach that addresses how different sectors, systems, and participants interact.
- The importance of addressing data ownership issues.
- Recognition that innovation is not exclusively technology-based but also requires consideration of innovative and disruptive business models.
- The importance of standardization and system interoperability.
- The foundational roles of collaboration, open research, and knowledge sharing.
- Recognition that data by itself has little value.
Two basic business models
I liked the report’s identification of two very different models for improving agriculture through better use of data:
- The captive prescriptive model. In this model the farmer becomes part of an integrated supply chain with limited freedom.
- The open collaboration model. Here the markets for services, apps, and data are developed on common open platforms with fewer lock-in effects.
Listing these as two alternative strategies does raise the important issues of how such strategies should be managed and how they should be funded and paid for. Several business models involve the buying and selling of data. The organizational infrastructure via which such buying and selling would take place is -- understandably -- not addressed in any detail.
A sophisticated view
What I found most appealing about this report is that it does address the implications of different business models for managing and generating value from agricultural data. While it does promote efforts at moving more towards open access to agricultural data in the EU, it does not fall into the naïve and outmoded "if you make the data open users will come" trap.
Unanswered here are the fundamental issues that any data program governance strategy must address: how to manage and pay for the program so that it provides value on an ongoing and sustainable basis? It's in connection with "value generation" that my main criticism of the report lies. It's not clear to me what the end goal is that we are pursuing here:
- Feed more people at lower cost?
- Help preserve family farms in the face of buy-out threats by conglomerates?
- Provide a platform outside of current channels that can serve as the basis for disrupting current or traditional business models?
- Reduce dependence on US-based big businesses?
- All of the above?
Of course it's impossible to address also such issues in the course of a single symposium. I only raise them here since developing and implementing an implementable and sustainable strategy -- one of the key recommendations of this report -- requires that they be addressed so that all participants are marching in the same direction.
Copyright © 2016 by Dennis D. McDonald