The first evaluation I ever led was for The Doula Project. My relationship to The Doula Project started with being a volunteer as a graduate student, and joining the board of directors a few years later.

Based on feedback from the organization’s Leadership Circle, there was a gradual decrease in volunteer shift sign-ups, and as a volunteer-led organization, the board wanted to understand why.

We learned that, despite having over 100 volunteers at the time and expanding to more clinics in most of New York City’s boroughs, volunteer engagement decreased. Why?

Once I graduated and began working full time, I didn’t have time to volunteer anymore. Majority of the volunteers that engaged in the evaluation had the same experience. Also, majority of the volunteers were transient. Many New York City college and graduate students tend to leave the city after graduation. For the ones that stay, transitioning from school to employment presents barriers on volunteering. And for The Doula Project, many of the shifts were during the work week.

Another thing the board and the Leadership Circle wanted to explore was the impact of the organization’s recruitment efforts. As a volunteer, most of the women I helped were women of color, and this was reflected in the experiences of the volunteer base. This was important because, while the organization served mostly people of color, majority of the volunteer base identified as white.

The evaluation’s recommendations stressed stronger outreach efforts to attract volunteers of colors and attract local residents to increase racial diversity and increase retention of volunteers who make New York City their home.

During this process, I learned that being data driven requires being proactive versus reactive. In hindsight, this evaluation was a reactive way of understanding what was happening with The Doula Project’s volunteer base.

Building a volunteer base to meet the needs of thousands of women and pregnant people in New York requires revising a volunteer recruitment, training, engagement, and retainment model that provides flexibility in volunteer hours, options for where to volunteer and how one wants to volunteer, and a base that is reflective of the diversity of New Yorkers. Though it was reactive, it provided valuable insights, and I was pleased to see the Leadership Circle and board act on the recommendations in an intentional way.

Key takeaway

On the road to becoming data driven, organizations move beyond prioritizing decision-making that appeases funders. Making intentional learning a priority, questioning, reflection, and action planning is embedded in all organizational areas especially programming. This process begins before problems arise.

Whether you’re conducting a program evaluation, starting a new strategic plan, gearing up for a new research project, or revising a program’s curriculum, start by ask yourself these questions:

  • Why do we need new data, and why now?
  • What‘s occurring in our organizational landscape that requires this data?
  • Who is asking for this data?
  • How will we use this data?
  • How will we share this data?
  • How often will we refer back to this data?

Getting clear on these questions (and keeping them at the forefront of your work) shifts your organization from being reactive to becoming a more proactive data driven organization.

Raise Your Voice: How do you prep for a data collection process? Share your thoughts below in the comments section.

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