Exciting as your work might be, especially as data grabs the spotlight, are you ever a tad dissatisfied? Wondering, really, whether you're doing enough with your talents?
You can do more -- just let Jake Porway, founder and executive director of DataKind, be your inspiration. DataKind, as I've written previously, hosts events bringing together data experts and local charitable organizations in need of help understanding, mining, and analyzing their data.
Porway, who joined AllAnalytics.com for an e-chat yesterday (read the transcript here), said he had asked himself such questions last year while working as a data scientist for New York Times R&D and crunching data on the weekends at local hackathons. As he explained during our e-chat:
Like lots of you guys, being involved in the data world meant that I was witnessing daily how important data was going to be in business, government, and beyond. It seemed like every day I was reading a new blog post about how big data was disrupting healthcare or radically changing data-driven decisions in business. It was really exciting.
I was even more inspired to see that people were working with data in their spare time, leveraging powerful machine learning tools and scraping public datasets to build interactive visualizations and new data-driven products at nights and on weekend.
Jumping into hackathons and innovation challenges was a reprieve from the constraints of corporate work, but ultimately those activities took a toll on his psyche. He explained:
I was really dismayed that most of the work we were coming up with was "more of the same" -- parking apps, restaurant finders, daily deal sites. Just more mobile apps that made comfortable lives just a tiny bit more comfortable… I wanted to solve global warming, not help people buy cheaper iPads, and that was when I went off in search of a way to make that happen.
Now, Porway takes pride in work like an interactive visualization his DataKind volunteers developed for DC Action for Children, a nonprofit advocacy organization that collects statistics about child well-being. DC Action for Children is "super awesome, the team that's working with them from DataKind consistently blows my mind, and I'm just amazed at all the hard work both sides have done."
While the D.C. Kids Count prototype shows how the data comes together in an interactive visualization -- a "big step up" considering the organization usually just puts out a 200-page PDF of child stats, Porway noted -- the full product will have some spatial analysis and statistical analyses built in, too. Plus, this project "may end up being scaled out to all Kids Count programs, allowing national use of the tool."
Porway also cited work DataKind volunteers undertook to help an arm of the United Nations create a visualization of cellphone survey responses. The UN liked the results so much, Porway said, it kept working with the team and ultimately presented its work at the UN General Assembly. "The fact that six people who spend their days working at bit.ly and Amazon can go out, work on a project for a week, and show it to the UN is just incredible to me."
More broadly, Porway said, he's really in awe of the volunteer community at large:
I'm always amazed that even one person comes to spend their time using their skills for good, but seeing rooms full of 100+ people spending their weekends hacking to solve poverty or help with humanitarian crises is just breathtaking. And having them come up and say, "Man, I didn't know I could do these things -- how do I keep helping?" gives me the chills every time.
Attend a DataDive and get yourself some chills. And no worries if one isn't coming to a city near you. DataKind even plans to offer DataDive kits. After all, Porway said:
The goal of all of this is not to be a crutch for organizations, but to engender a world where every non-profit is data-driven. As such, we are meticulously collecting the lessons we've learned from these projects so we can establish common tools, best-practices, and resources that we can hand over to organizations so they can become data-driven themselves.