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Co-operatively growing farmer data


Very productive day with the good folk from Birchip Cropping Group yesterday. It was our second workshop in the Farming Together sponsored project being undertaken by BCCM to help grain growers from the Wimmera-Mallee explore a data coop.

In a new land speed record, by the end of the workshop we had crossed everything off our wish-list in terms of things we needed to draw up a draft set of Co-operative Rules – once again proving that farmers and nurses are the most likely to survive the zombie apocalypse.

The data coop can be a big concept, and there are some key pieces of the puzzle that are still formative in their shape and readiness, so it was really encouraging that the farmers present were beginning to have concrete ideas about how it may work. There were some cracking specific examples that are likely to prove fertile ground for testing of the data coop mechanics and economics.

Perhaps more encouraging still was that we got the sense that the farmers were starting to take formal ownership of the project, thinking that they could build it from the ground up (rather than having a structure imposed on them). Notably, it was suggested that the data coop could leverage the BCG infrastructure to provide a springboard to get started.

Also, really interesting discussion about farming practices, quality assurance and compliance. Noting that the thinking to date has been that precision agriculture would lead the use cases for the data coop, we discussed the rising cost of compliance and increasing accuracy of testing and standards and whether this pushed ‘traceability’ higher up the priorities.

We were also lucky to have Leanne Wiseman and Jay Sanderson from Griffith University so we could untangle some of the trickier questions around rights to data. We sketched out some of the key structural elements that could be developed for the coop – such as members agreeing to an “Ethical Good Governance Code” that would be updated every year and would require things like disclosure of conflicts of interest, disclosure of 3rd party data sharing arrangements and agreement to minimum standards around data being shared, accuracy and types.

Finally, the emerging view seemed to be that the data coop would be akin to social infrastructure and would be best structured as a non-distributing coop. This would better align member and community interests and also enhance the collaborative business model with customers.



Why haystacks need needles


One of the challenges of a co-operative is balancing the interests of the group with those of each individual member. Unlike a proprietary limited company, stakeholder interests are not beholden to the profit motive. Rather the primary focus is on return to members – which can be delivered in a variety of ways including via dividends, rebates and patronage. The difficulty is that without a return on capital signal, the cooperative itself has to determine how value is distributed among its members.

A data co-operative provides a simple illustration of this challenge. On the one hand, members come together to aggregate market power – as individuals they are weakly positioned to bargain the terms on which they share their data, while as a collective their individual control is amplified. On the other, one of the principal objectives of a data coop is to enable individual members to have more control over their data. For this reason, a data coop will create permissioning mechanisms that enable the individual to differentially share their data with others. The data coop leverages group market power to deliver value for individual members.

But that is only part of the story. By aggregating control over data, the data coop will also enable sharing of data in aggregate. And there are likely to be circumstances where it is in the collective’s interests to share aggregate data. This is after all why Big Data and the Algorithms have been dominating the pop charts over recent years. For this reason, it may be a condition of membership that coop members agree to authorising the coop to share their data. You can see the challenge here…

For this reason, it is really important that cooperatives are very clear about how a cooperative distributes value amongst its members. If interests are not aligned, fractures across the points of difference are likely to occur when the structure is stressed.




Can I borrow a synapse?

Annie Duke, retired poker champion, talks about strategies to accelerate learning in her book “Thinking in Bets: Making Smarter Decisions When You Don’t Have All the Facts”. A key one is drawing on your community – learning from the experience of experts, testing boundaries with peers, and simplifying ideas through teaching others. Really interesting question here about how to institutionalise these types of practices within an online cooperative community…