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The Collective for Health Equity and Well-Being

Cooperative Extension’s Collective for Health Equity and Well-Being is a community of Extension personnel and their partners united by their shared commitment to advancing health equity and well-being. Members work together to support the implementation of Cooperative Extension’s National Framework for Health Equity and Well-Being (2021) to ensure that all people can be as healthy as they can be.

Using Data in Collective Action; Focusing on What Matters


Collective impact initiatives utilize shared measurement systems to identify  key metrics of success that align partners toward a common vision. But a recent article by Justin Piff in the Stanford Social Innovation Review suggests that the specific data we chose to track loudly signals what we believe to be important.

In the article, the author shares four lessons from his work that can help collective impact initiatives  use data more effectively for social change.  They are:

1. Prioritize the learning, not the data system.

2. Be clear about whose lives you hope to improve.

3. Use qualitative data (in addition to quantitative).

4. Keep the short and long games in view.

"Data can play a transformative role in setting, refining, and evaluating collective impact strategies. Shared measurementβ€”grounded in a culture of learning and focused on equitable processes and outcomesβ€”may be our greatest hope for shared understanding of the societal challenges communities face (Piff, 2021)

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Roger, thank you for sharing this view about using data effectively for social change.  It's a quick reminder to focus on why the data is being used and the importance on both the numbers and stories and their meaning; on the people we are serving. And to proceed with the near and long term in mind. 

Periodically asking questions that arise from the cited author could be very useful. Here's a list that came to me:

Why are we using the data?                                                                                                                  What does it mean?                                                                                                                                  How can we learn from the data?                                                                                                    What's important in the short and long run.

Bonnie Braun

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