We often say data drives decisions.
But the quality of those decisions depends entirely on the kind of data we choose to collect.
One of the important conversations during our recent was around gender data and what it really means to design agricultural systems that work for everyone.
Most programmes collect sex-disaggregated data; telling us who participated. But good gender practice goes further. It helps us understand how differently people experience opportunities, barriers, markets, information, and decision-making and why those differences exist.
One of the officers noted during trainings most of the women leave by 3pm, not because they are disinterested but because they have cows to milk. The same women who carry significant labour in the value chain post-harvest, homestead, livestock, are the ones walking out of the rooms where market knowledge, digital tools, and trade information are being shared. Not by choice but by design.
That’s the problem with collecting only sex-disaggregated data. It tells you who was in the room at 9am. It doesn’t tell you who was still there at 4pm, or why.
Gender-disaggregated data goes further, it captures how differently people experience the same value chain, and why. Intersectional data reveals who falls through the cracks entirely. Qualitative data gives you the norms and power dynamics that no headcount will ever surface. And sometimes, it gives you a 3pm moment that reframes the entire conversation.
That is why during our trainings, we continue to explore the importance of moving beyond numbers toward data that captures lived experiences, barriers, and intersectional realities. Because without understanding the “why” behind the gaps, it becomes difficult to design interventions that are truly inclusive.