Over the last few months, we’ve received many suggestions from the DATA4Philanthropy Network for future programming and resources. One of the topics we heard most was the need for new approaches to understand and evaluate impact. There have been many great impact and evaluation efforts across the philanthropic sector, but the majority remain in silo and there lacks consensus around what shapes our understanding of impact, the methods to measure it, and what it means in different contexts. 

On June 17th, 2024, Prof Ingrid Burkett and A/Prof Joanne McNeill published an article on the Griffith Centre for Systems Innovation (an innovation center within Griffith University in Australia) blog entitled, “Now we are all measuring impact – but is anything changing?” In this article, the authors provide several learnings based on their own experiences measuring impact at the Griffith Centre for Systems Innovation and a new framework to synthesize the breadth of impact and evaluation efforts that already exist.

The authors propose a matrix summarizing the many different approaches for impact and evaluation they have seen across domains. As shown in the image below, the matrix is segmented based on when the measurement ought to take place (x axis) and the goal (y axis) – where the top quadrants focus on approaches using quantitative data and the bottom on both qualitative and quantitative approaches.

“Making Sense of the Impact Measurement Soup: A Matrix as a Starting Point”

Below we provide three takeaways from the piece relevant for the DATA4Philanthropy Network:

• The way we measure impact is influenced by our culture, beliefs, and worldviews. There is an opportunity for funders to reassess how their own values are reflected within impact and evaluation processes and what is being left behind. 

• In times of uncertainty, there is a tendency for individuals to seek out methods and approaches that specifically aim to create certainty. It is important to consider what exactly these methods are bringing certainty to and avoid the over-simplification of complexity. 

• Quantitative methods that focus on “cause and effect” are valuable, but there is also a need for other impact measurement approaches that focus on making sense of complexity. These methods for complexity need granularity in order to make them relevant for different contexts.

The full article can be found here.

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