Data Models for Digital Food System Innovation

“Strategy” is sometimes defined as “the art of the general,” referring to the role of a leader in helping an organization navigate uncertainty to reach desired outcomes. Using data to guide innovation strategy in food systems might be described as developing the “art of the generalizable”: finding common concepts, map their connections, and draw conclusions that can guide actions and navigate uncertainly on a large scale.   Data—particularly data in context—is also important for targeting specialized solutions meeting the needs of particular stakeholders (e.g. smallholder farmers), research domains (nutrition and health), or use-cases (carbon capture in soil). 

Building common understanding of applicable, extensible data models can better equip us to target specialized solutions, which can in turn improve the general models, building mutually-reinforcing capabilities for agile, data-driven discovery, action, and adaptation in food systems.