Jake Porway uses big data to tackle big social problems. As the founder of DataKind, he connects—and acts as an accessible bridge between—data scientists and not-for-profit companies, which often don’t have the know-how or resources to benefit from data analysis. A former New York Times Data Scientist, Porway wants all of humanity to benefit from the gains of big data.
With DataKind, “We’re connecting nonprofits, NGOs, and other data-rich social change organizations with data scientists willing to donate their time and knowledge to solve social, environmental, and community problems,” Porway explains. Porway was named a 2012 National Geographic Emerging Explorer, and he currently hosts The Numbers Game on the National Geographic channel, in which he tackles one of life’s most daunting topics each episode (such as how to live longer, combating road rage, the best negotiation methods), and reveals the surprising science behind them.
“Jake Porway is a [data] matchmaker ... Porway is part of a new genre of National Geographic explorer in that his expeditions are occurring digitally.”— National Geographic
Irrepressibly curious, Porway is both a scientist and a coder, equally excited to pioneer the next machine learning algorithm as his is to optimize the code to run it. He hopes to make machines smarter—and he looks for new ways to help machines make sense of things, for the greater good.
The Age of Big Data
Jake Porway says: There are dozens of apps to help us find movies or choose restaurants. Nice, but isn’t that really just making very comfortable lives slightly more comfortable? What if we also used the power of data analysis to do something that could change the world? In this talk, Porway explains what big data is, how it’s being used around the world—and why we should care. Porway also explore how to become data-driven: engaging and deeply accessible, he offers practical wisdom, drawing from cool forward-thinking projects, on how organizations and individuals can adapt in the big data age. He illustrates the importance of asking good questions before looking at data, acknowledges and outlines the inherent biases in data, and advocates for subject matter experts. He shows how the use of data visualization is a process instead of just for “pretty pictures.”