Data Integration Challenges in Agri-Tech

Another excellent edition of topsoil from Ariel Patton of Mineral.ai that asks the right question about data - is it the new oil or a new byproduct? Read the whole newsletter for the context, here are my key takeaways for AgTech startups: 1) Digitization is not an objective - being able to do new things or old things in better news ways - is. AgTech startups would do well to remember this - just getting analog to digital takes work and sometimes money, so get to the benefit to the user without just expecting them to come along. 2) Data in (data sources) - Ariel groups these into 6 source types: (1) public (weather and soil); (2) remote sensing (satellite/drone imagery); (3) equipment (machine data, monitors, sprayers, applicators, irrigation systems; (4) lab (soil samples, soil sequencing, tissue sampling (NPK and micro-nutrients); (5) sensors (weather stations, pest traps, mobile device - different than #2 above); (6) non-digital (pen and paper). As Ariel points out, farmers do not like data for it's own sake - it has to help them do something. It's the job of the AgTech startup to help them take that journey - often through the narrative of a farmer of similar size and crop mix that is comparable to their operation. 3) Data challenges - connectivity is required and often a challenge, interoperability via APIs and common data standards is not universal (at all), data quality can be suspect (quality, accuracy, and completeness), and trust and privacy are causing more concern than it used to (farmers recognize the value of their data more than ever - and are hesitant to give it up without a really good incentive that aligns with their objectives - they are not so worried about the startup's data objectives). 4) Now to the heart of the matter - is data the new oil (a new revenue stream that could be bigger than the actual crops) or a new byproduct (valuable but nowhere near as valuable as the crops)? I'm with Ariel - data fits into the byproduct column - for now. Improved yield was the top value proposition for 800+ corn and soy growers - it would be interesting to see how this compares with specialty crops. There are a lot of things data can do, but there's no integrated package out there just yet. Plug and play is still launch and guess for too much of AgTech, and it always takes longer than planned. I think right now we have point solutions like Microsoft Word, Excel, and PowerPoint in the early 90s - it took a while for them to get integrated into Microsoft Windows and pre-installed with Microsoft Office pricing bundles. That is still a ways off in the distance, but as we collectively do a better job of delivering data value to growers the integrations will get easier to build and get value out of - that will help all AgTech startups as it starts to happen. And yes - we need massive amounts of data to really get AI going in earnest (topic for another post). #agriculture #data #agtech Ann Donahue Julia Nellis Kara Timmins Emily Lyons

Excellent piece, Matt Waits. Your framing of interoperability and the need to shift from siloed applications to adaptable capabilities resonates deeply. I’ve been lamenting this for the last few months, as many of my AgTech friends have heard. Having spent the first part of my career banking mobility and enterprise data companies, and the past decade deeply entrenched in AgTech, I now find myself operating the largest fruit and nut tree nursery in the country. That vantage point has sharpened my view that adoption isn’t just about features—it’s about integration, data context, and implementation discipline. We’re actively building our own nursery tech stack, but progress has been frustrating. Not due to resistance to technology, but because the systems don’t align with how our data is structured or how decisions are made on the ground. Most of our tech partners still design in isolation, without a clear understanding of what it means to interoperate in a mixed-system environment. This is where I see real potential for AI—not as a silver bullet, but as a practical bridge. AI agents can help translate between systems, map data semantics, and deliver more usable insights. The potential to streamline onboarding and implementation of new systems is even more tantalizing. But they can’t do it without access to structured, machine-readable data. I appreciate Matt’s call to action for AgTech software companies: publishing clear data structures, exposing APIs thoughtfully, and documenting reference data in a way machines (and humans) can understand is foundational. #agtech #ai Sachi Desai Shane Thomas Rhishi P. Walt Duflock F3 Innovate