Making sense of data

If you can’t explain it simply, you don’t understand it well enough
Albert Einstein

Earlier on twitter I shared a picture on enrollment data, with the text: the story of girls’ education….

I hoped it would trigger some reflection: What do I see? Does this surprise me? What do I know about the reasons behind these figures? What is it I do not know?

I like the picture because it tells a story; a story of girls’ education in a Maasai populated area in Kenya. In this area girls are married off at a young age, sometimes even at the age of 12.  This picture shows that girls’ enrolment drops from 30 in class 5 to 7 in class 8. I got triggered immediately: how is this related to the practice of female circumcision among the Maasai in Kenya? How can education contribute to changing this harmful practice?

enrollmentI strongly believe in the importance of exploring realities and stories behind data.  Simple reflection questions should be posed more often when making sense of monitoring, evaluation and research data.

I observe that often effort and resources are invested in collecting the data, and less time is reserved for giving meaning to the data; by posing the right questions and looking for answers combining perspectives of different stakeholders.

Luckily I am also involved in some good practices of using data to inform learning and action planning. Recently I facilitated a sense making session of mid-term data on the outcomes of a training of trainers program: working towards Evidence and rights based Sexual and Reproductive Health and Rights and HIV prevention interventions for youth. The training program aims at building capacity on programming for behavioral change among civil society organizations. The session took less than three hours and resulted in useful input for the mentoring and support sessions to come, such as stressing the importance of organizational support for change.

It was a good experience for the participants, who are all involved in behavioral change programs with a high level of complexity. I trust and hope that this experience inspired them to organize sense making sessions with different stakeholders in their programs. Because understanding data about behavioral change can really contribute to the quality and effectiveness of such programs.