Why the industry that feeds 8 billion people still can't read its own data

"Agricultural data is fragmented, distributed, heterogeneous, and incompatible. Research institutions publish trial results in inconsistent formats, product manufacturers use proprietary naming systems, farmers record observations with local terminology and retailers track sales without connecting them to agronomic outcomes. The result is an industry sitting on massive amounts of information it can barely use."

"Agriculture doesn't have a data problem-it has an intelligence problem. The data exists. What's missing is infrastructure that understands what it means. According to a McKinsey report, implementing data integration and connectivity in agriculture could add $500 billion in value to global GDP-a 7 to 9% improvement over current projections."

Agricultural data remains fragmented across incompatible systems, creating an intelligence problem rather than a data problem. Unlike healthcare and financial services, agriculture lacks universal data standards, with research institutions, manufacturers, farmers, and retailers using inconsistent formats and proprietary systems. This siloed information prevents effective AI implementation despite enormous volumes of available data. McKinsey estimates that implementing data integration and connectivity could add $500 billion to global GDP. General-purpose AI platforms struggle with agriculture's complexity, as large language models cannot effectively process the domain-specific knowledge required for farming decisions without proper data infrastructure and standardization.

Read at Fast Company