Ten Tactics Internal Evaluators Can Use to Build Organizational Learning within Nonprofits

[I wrote this piece for the American Evaluation Association’s AEA365 (a daily blog with tips about evaluation from evaluators) during a special Organizational Learning-Evaluation Capacity Building week in April 2024; it was published April 15, 2024].

Most of my work focuses on evaluation capacity-building with nonprofits. I’ve been immersed deeply in building culture and practices for organizational learning for 16 years, both as an internal evaluator and as a coach to many internal evaluators. Based on my experiences in a range of settings, here are ten (among many more) tactics to build organizational learning:

  1. Form a data committee that includes representatives from different departments and roles. The purpose of this committee might vary depending on where your organization is in its data journey, but some functions might include: inspiring champions in non-evaluation roles to bring learning and information to others in your organization; data governance; and identifying areas of overlap and opportunity between evaluation and other projects.

  2. Fold data/results and reflection into existing meetings and structures. To help reduce the silo-ing of evaluation, look for opportunities to bring a data point naturally into staff and other regular meetings.

  3. Use consistent reflection questions and/or protocols. Consider using some predictable questions and structures that support non-data folks to have a “way in” to interpret data during longer data reviews.

  4. Set, share, and keep to a data schedule. To help avoid that “black box” feeling, create and share a data schedule with your organization that includes when tools will be ready (e.g., when a survey will open), when data will be collected, and when results will be available in which format(s).

  5. Be proactive. Share updates, findings, check in about data needs in your organization. People will appreciate hearing small wins or challenges you are working on and that you have their interests in mind.

  6. Express appreciation. Honor those who contribute to evaluation work (e.g., who help collect data) or who use your data. Not only will this contribute to a positive data culture and make people feel good, but it will also foster learning through examples.

  7. Share examples from other organizations/nonprofits. Our colleagues, who are not evaluators, put a lot of faith in us. Finding and sharing examples of how similar organizations have done what you propose can build confidence.

  8. Ask leaders in a range of roles to feature/share how they use evaluation in their work. This helps people see a broader vision of how they could leverage data and inspires data use.

  9. Learn by doing together. Work on tools like surveys, and get input on types of analyses, with folks who are not evaluators. Describe the why behind your thinking and ask about their rationales too. You’ll learn from each other, and the work will be more meaningful and fun.

  10. Add capacity with an evaluation advisory board. Bringing some outside evaluators and community members in can help advance learning, adding diverse perspectives, experiences, and wisdom.

Hot Tips

Curious about how to set up an evaluation advisory board? Here’s an article about it in the Journal of Youth Development that I co-wrote with fellow advisory board colleagues. Although we focus on the context of youth development programs, the tips and examples are applicable to other settings.

Rad Resources

Here’s a helpful protocol that includes questions for data reviews from the Network for College Success.

I’ve found The Facilitator’s Guide to Participatory Decision-Making to be a powerful and practical book to help me frame meetings, data reviews, and other gatherings effectively. You can find more information about it, as well as related publications, here.

Flowers blooming from a recent visit to the Oak Park Conservatory with a bestie who has been a teacher for 30 years—she is always learning and promoting learning.