July 22nd, 2013 Data Scientists and the Non-Profits Who Love Them

 
http://www.meetup.com/DataKind-NYC/events/129284232/
 
On Monday, July 22, 2013, OLC attended DataKind’s event, Data Scientists and theNon-Profits Who Love Them at Culture Shock in DUMBO. Eric Ren of charity: water and Vlad Dubovsky of DonorsChoose.org were the two keynote presenters.
It was revealed at this event that there aren’t many data scientists in non-governmental organizations due to lack of resources. Even so, there were two representatives (data scientists) from such organizations that broke that mould.
 
 
http://www.charitywater.org/
 
charity: water was founded in New York City by Scott Harrison. Today, the organization has water projects in over 12 countries, all driven by data. Eric Ren joined charity: water after his employment at Bain Capital as a consultant left him wanting something else. He joined charity: water nine months ago. His role is less technical than what others believe. He works with Excel and analyzes data. At charity: water, he works with financial, website and programs data. They use Google Analytics for website data and the programs data is pulled from the field in 12 countries.
 
charity: water was formed to end the water crisis around the world. Over 800 million people lack drinkable water sources. They’re reinventing charity: a promise to the public that all donations—100%—go to the projects, prove that the donations are used to fund water projects, and committing to the creation of the charity: water brand.
 
“In 6 years, we’ve raised $100 million with 400,000 donations and helped 3.3 million people with clean, drinkable water,” Ren said. “We take the money that’s been donated to us and we invest them in local organizations. We also fund a variety of solutions: handdug wells, rain catchers, water filters, but it all depends on the geography.”
 
“We’re also excited about data from remote monitors. Google recently rewarded us with $5 million [Global Impact Award] to install 4,000 remote water sensors to obtain realtime data from them,” he said.
 
 
http://www.donorschoose.org/
 
Vlad Dubovsky presented DonorsChoose.org, a New York City grown organization that is paving the way for public education. Charles Best, the founder of DonorsChoose, realized how difficult it was for teachers in public schools to get materials for their students. He started the organization in The Bronx and it has taken off. With DonorsChoose, people can directly donate resources to teachers and the materials are delivered by DonorsChoose to the teachers. The proof of delivery and the proof that the materials are used by students are letters and thank-you notes from the students themselves.
 
The way DonorsChoose works is: 1) Teachers ask for resources on DonorsChoose. 2) Users choose who they want to help. 3) Students get to learn. 
 
“The resources that teachers ask for ranges from math to field trips,” Dubovsky said. “Users can choose from a variety of projects and students write thank-you notes for the donations.” At DonorsChoose, transparency and accountability are high on their list and avoid gimmicks at all costs.
 
“Partnerships are not just citizen donors, but from corporations as well. We have Skype, Chase, Kia, Chevron.... Chevron donated $4 million to DonorsChoose and they use data to develop their ROI and that ends up with them giving more to charitable causes.”
 
Fast Company named DonorsChoose as one of the top 50 most innovative companies. DonorsChoose has raised approximately $190 million, helped 9 million students and 150,000 teachers. 
 
“55% of high poverty schools have participated in DonorsChoose. 81% of the projects have been funded. The average cost of the projects are about $570. In general, almost 80% of all projects are all reaching their goals.”
 
To Dubovsky, a data scientist’s role in a non-profit ranges from being tech oriented, but also being able to communicate. “Data scientists are trusted advisors to CEOs,” he said. He also mentioned that job descriptions for data scientists are “so far from reality.” Another thing that a data scientist must understand is productization and KPIs.