Last week, I shared some highlights of outputs from A2DataDive 2013 and outcomes from A2DataDive 2012. Now I want to take a deeper look into the motivations that bring volunteers and nonprofits together to devote their weekend to the great experiment that is a datadive. After this year’s final presentations, Dave and I had an opportunity to sit down with one of the client representatives and one of the returning community volunteers to learn more about why they chose to participate in the event and what they learned from the experience.
Interview: Amy Wilson from 826Michigan, A2DataDive 2013 Nonprofit
Now that you’ve been through your first datadive, what did you think of the experience?
In a nutshell, I’m very impressed. I’ve been working with our data ambassador since mid-October. The A2DataDive team contacted 826Michigan last fall to recruit us as a client for the event. As a nonprofit, we have to be economical about staff time and resources. We were a little skeptical at first, but realized in the back of our minds that this is valuable. As we continued our conversations with the data ambassadors, the value became clearer to us. I admit, I still wasn’t entirely sure what this weekend would look like when I showed up on Saturday.
As the event progressed and I saw the concrete projects that were coming out of volunteers, I became increasingly blown away. I had neutral expectations coming in – the data ambassador did a great job at managing expectations. The event exceeded my expectations. I’ve been impressed with the volunteers, their quality of work, and with the organizers.
What are your key takeaways from this event?
This gave me new insight into the community of data and the diversity of approaches to collecting and interpreting data. The volunteers approached it many different angles – everything from looking at numbers and words in various ways to analyze and to inform how we engage our customers. The level of detail necessary for collecting normalized, meaningful data was new to me. It will change how I approach data in my work and how the organization approaches data collection and analysis in the future.
What was the most interesting finding from your group’s final presentation?
There were a lot of interesting findings. I was really impressed. One that stood out in particular to me was the one where a volunteer did a comparison of pre and post surveys of student tutors. There was a side-by-side bar graph of responses to a question about whether students felt comfortable for asking for help. There was a huge jump in confidence after the tutoring. Before the event, I suspected that was true but I didn’t have such a clear way to demonstrate or quantify that. That’s huge for us, because so much of our mission is about the idea that there should be no stigma to asking for or needing help. This data demonstrates that 826michigan is a place where that stigma can start to fall away for our students.
Now that you have the volunteer analysis from the event, what’s next?
It’s going to be interesting to see how this is implemented. I learned some new techniques, but I’m also really hoping that some of the people who volunteered will continue to volunteer and help our data collection and analysis.
Interview: Mandar Gokhale, A2DataDive 2012 and 2013 Volunteer
What motivated you to come back a second time?
I’m a network engineer by profession. I’m an alumnus of the University of Michigan College of Engineering. I enjoy creating interesting visualizations of data in my spare time. I thought the objectives last year were accomplished fairly well, and the client derived some value. I decided to come back this year to contribute more and to have fun.
To me, the datadive means I get to do some something fun that also helps the community.
What did you work on for the datadive?
This year, I worked with one of the same volunteers as last year. Both times I worked with visualizations, last year with networks for the African Health OER Network and this year with time-based data from Food Gatherers. This time I did more statistical and predictive analysis.
What new data sleuthing techniques did you learn this year?
This year I re-learned linear regression and a few more tricks in R, including how to analyze and present time-based information in calendar heat map For the statistical analysis in R, I mostly looked up instructions and examples on the Internet. I had read the profiles of some of the participating nonprofits, and skimmed through their objectives beforehand, so I had some sort of idea of what I wanted to do before I showed up for the event.
What is your biggest takeaway from participating in both years?
The event brings together people from many specialties. But all these groups need to talk to each other more often, to produce more meaningful data. Typically what happens is the clients collect data in a way that makes it really hard for the processing people to work with it later. We made some recommendations in our final presentation to try to address this. There’s a need for getting more knowledge out there about how to collect good data and how to analyze it in order to make it easier.
Nonprofits should talk to data scientists more often.