Farm data is only useful if you can find it, get to it, combine it with other data and use it again. That is the idea behind the FAIR data principles: Findable, Accessible, Interoperable and Reusable. They were first published in 2016 for research data and are now widely used in industry. They also sit behind EZI's position that farm data belongs to the producer who paid for it. Here is what each principle means in practice.
Findable
For data that is still on paper or in electronic documents (#farmrecordsaredatatoo), findable may mean a catalogue that outlines what data exists and where. An audit or mapping exercise is often used by industrial businesses to understand where their data is and what shape and format it is in. For data that is already digital, well-organised and well-populated metadata, which is data about the data, becomes crucial to making it findable.
Accessible
Accessible is not about data being free and freely downloadable. It is partly about the shape and form the data is in. A bunch of PDFs on a thumb drive, or a filing cabinet full of historical records, is not very accessible. The other part is the mechanism by which data can be accessed. This may include a layer of trust and consent: access in a particular way, for a particular purpose, for a particular period of time, for a particular value exchange (something for something).
Interoperable
In this current burst of technology and platforms, it is highly unlikely that any one provider or platform will universally win the day. In some arenas, production areas or markets perhaps, but data is so widely usable that there will always be someone sitting outside any given platform. It is far more likely that the technology market will evolve into a series of interconnected platforms, with data flowing from one to another according to its use case. In that scenario, interoperability and APIs (application programming interfaces, which allow data to be served from one piece of software to another) will be the order of the day. To make this work, data will need to be better aligned through community-accepted formats, vocabularies, identifiers and units in both the data and the metadata.
Reusable
Reuse is where the rubber hits the road in terms of value, and the quality of the data directly affects how ready it is for reuse. Pat Kennedy, developer of an industrial operational data framework used around the world, held the mantra that "data is one of the few things that becomes more valuable the more times it is used". Reuse can take many forms: internal analysis and benchmarking, external analysis and benchmarking, certifications and compliance, machine learning and automation, plus ways that have not yet been imagined. Reusability is helped by well-organised, well-structured data with rich contextual metadata.
What this means on your farm
Four questions to ask about your own records:
Findable: Do you know what data you hold, and where it lives?
Accessible: Can you pull up a paddock record quickly? Who else can, and on what terms?
Interoperable: Can you export your data in a common format, such as a spreadsheet, to use somewhere else?
Reusable: Is it recorded consistently (same paddock names, units and dates) so you can compare across seasons?
EZI's philosophy is that farm data belongs to the producer who paid for the equipment, subscriptions, maintenance and staff that collect it.