Even “Snowflake” Foundations Collect And Share Data On Agricultural Investments

This post was written by Gabriela Fitz, Director of Knowledge Management Initiatives at Foundation Centre. It is part of a blog series, where Initiative for Open Ag Funding partners share findings from their recent consultations with NGOs, donors, and foundations to understand the development community’s data needs and challenges in the agriculture and food security sector. 

Join the conversation and learn more at: https://www.interaction.org/project/open-ag-funding/overview. The full reports from the consultations are available here.

Sometimes we hear philanthropic foundations described as “snowflakes.” They each have their own unique set of strategies, their own orientation towards change, their own impact measurements, their own proposal formats, and even their own unique language for describing the work they do, (as one foundation so succinctly told me, “ag funding isn’t just ag funding”).

But when we look past their – sometimes well-deserved, sometimes exaggerated – reputations for going-it-alone, we also see a number of commonalities in how foundations work and in how they use data and knowledge to support that work. It is these commonalities that are so important to the  Initiative for Open Ag Funding, as they allow the initiative to make agricultural investment data more useful for all stakeholders.

First, foundations have a remarkably similar set of questions and knowledge needs when making decisions about where to grant money. Foundations want and need to know:

  1. Who else is funding on this topic and who are the implementers being funded?
  2. What kinds of activities are being funded and what approaches are other donors taking?
  3. Where are the current funding gaps and where could we make the greatest difference?
  4. What have other donors learned from their work to date and what should I be looking out for?

The similarities don’t stop there. Beyond sharing the need for similar data, foundations also share similar challenges in getting the data they need at the scale that is most useful to them. Currently, they face challenges in accessing granular-enough data about funding while at the same time lacking access to sufficient aggregate-level data about impact. Stakeholders described their frustration to us in the following ways:

”It’s easiest to find out who is funding on general thematic categories but, in terms of drilling down to the level of grantees, I find that piece a little harder. [Who] are the peer funders who are supporting the same people as we are? [That] is a little bit more piecemeal. I have to find it through conversations and meetings with peer funders and talking to grantees.”

“From my experience the aggregate level data in terms of dollar amounts is more robust than the drilling down and the regularity in terms of grantee organisations. Being able to do analysis of which funders have dropped or increased funding across time in X region, analyses beyond the aggregate, is not always totally apparent.”

In the end though, the thing that may be the biggest driver for improving the way we collect and share data on agricultural investment – even more than similar data needs or similar frustrations – is that, despite their uniqueness, foundations are increasingly motivated to coordinate their efforts with those of other donors. In one of our stakeholder conversations a participant summarised this motivation as follows:

“Knowing what other donors are funding is becoming more important because foundations are wanting to do more to leverage their resources, collaborating to leverage more change and impact.”

From land sovereignty issues to transportation infrastructure to market demand for specific crops, foundations don’t just want to coordinate with other foundations or donors across different parts of a complex system; they need to coordinate. In some sense it is this basic commonality, the need for greater awareness and coordination across complex systems, which underpins the growing need for aggregate and disaggregate data that can be compared, analysed, and shared. “Snowflakes” maybe, but ones that recognise the power of a blizzard.

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