By Chris Allan and Atalie Pestalozzi

As we noted in Part 1 of this blog, graphs showing the number of outcomes can sometimes be misleading. So, what do we recommend?

Work collaboratively: Acknowledging that many of the decisions we make are subjective, we try to work collaboratively to decide what constitutes an outcome, how they are weighed, grouped, or split. Working as a team helps curb individual biases and interpretations of outcomes and tell the story from more perspectives.

Explain what the methodology does and doesn’t do: To avoid any confusion around what Outcome Harvesting is, make the qualitative nature of the methodology explicit. Along with talking about what’s great about Outcome Harvesting, explain the shortcomings of the methodology and the biases in collecting data that make it great for painting a picture, but not for statistical analysis. To manage expectations with the commissioning organization, we do this from the get-go. We walk them through these same examples, and often decide with them what qualifies as an Outcome, whether to lump or split, and how to present the patterns that emerge.

Let simple images get the idea across

Simple representations that deemphasize the numbers are the clearest ways to present results. Below are some alternatives that give a visual snapshot of the story rather than just counting up results.

With this example, a quick glance tells you that the program had more influence at international level than at national or local. The actual number of outcomes that produced this do not add depth or quality to this story, so while the relative proportions of the bubbles reflect the actual totals, the numbers were not included.

Alternatively, the graphic below shows which groups changed the most in the program by varying how many boxes are stacked up. Though it’s more numerical, the goal is still for viewers to get the overview without scrutinizing numbers:

Tell a story:  When we can, we present patterns of outcomes as infographics. By creating a visual “pathway to change” that tells the story about how an outcome actually happened, we can show how the program works when it is successful and highlight the contributions of key players. Numbers alone don’t capture the complex process or collaborations that go into producing an outcome.

These images work best when they are accompanied by a brief narrative to guide the reader through the story.  As an example, the infographic below traces the timeline, actors, and strategies involved in shifting state budgets in Nigeria to better support smallholder farmers.

A second example traces a ten-year effort to promote gender equality in land inheritance in Ghana.

In conclusion, remember that Outcome Harvesting is a qualitative evaluation tool that’s meant to help assess complex programs and their progress toward the expected and unexpected. Quantifying outcomes doesn’t do us any favours, but some data lends itself better to quantitative methods like surveys and can complement outcome data well. What we take away from an Outcome Harvest should get us thinking about programs less linearly and more creatively, and stories are better at doing that than numbers.

Note from the OH blog committee: Do you agree with the author’s take on the value and challenges of quantifying outcomes? What strategies do you use for sharing data in ways that resonate with users? We’re looking forward to your comments or blogs! Barbara Klugman and Awuor Ponge