Data is the new water 💧
Prime Future 243: the newsletter for innovators in livestock, meat, and dairy
“Data is the new oil, they say; data is the new gold. Very well, then: oil costs eighty dollars a barrel, and gold is twenty-three hundred dollars an ounce. How much does data cost? It’s a meaningless question.”
That’s my new favorite quote from my new favorite article on all things related to data pricing and data business models.
In the world of broadacre and specialty crops, any discussion about data pricing or business models is usually only relevant for technology companies — either those capturing proprietary data or those aggregating data for visualization and analytics.
But in the world of livestock, meat & dairy, data pricing and business models are becoming increasingly relevant to large-scale producers and processors who are building out their own internal data infrastructure. Data science capabilities are as central to their longevity as their animal science expertise.
So this is relevant context for the many who are aggressively building those capabilities:
“The factors driving oil prices may be complex, but there’s a well-established consensus on transaction criteria: volume, location, grade, and date. There are exchanges which specify delivery rules for benchmark contracts like WTI, Brent and Dubai. When you buy a barrel of crude, you know what you’re getting.
Data ... is not like that. Every barrel of WTI crude is identical; no two datasets are identical.
Data has no innate value. The value of data comes from the value of what can be done with it. Therefore, every discussion of price has to begin with understanding that value: how the data will be used, and by whom.”
The broad, hand wave-y agtech category of data & analytics is no stranger to the valley of despair — also known as the phase in the innovation hype cycle where initial excitement wanes and reality begins to sink in.
That reality has largely been that — over a decade into agtech — it’s still unclear what business model creates value for data creators, data aggregators, and customers. What was supposed to be the silver bullet for producers and the up-and-to-the-right growth path for founders & investors has instead proven fairly disappointing all the way around.
Having spent 5+ years commercializing data and analytics products, the article above spoke deeply to my soul. It is 100% accurate.
But here’s the paradox — data business models have struggled, and yet we all inherently know through our spidey-senses that the importance of data, especially in an AI world, is increasing.
From that paradox, I have two hypotheses specific to our highly-consolidated, increasingly-aligned, evermore-concentrated animal protein segments: