In a speech to a generation of technologists at the SXSW Conference in March 2015, Mikey Dickerson made the choice for future pioneers clear: “Some of you, not all of you, are working right now on another app for people to share pictures of food or a social network for dogs. I am here to tell you that your country has a better use for your

“You need ways to reward success that are better than we have right now. You need ways to attract people that are better than we have right now. If you want to make government better you need to improve your hiring and your people management processes.”4

Much of this work is unseen—fixing something that is tedious, speeding up a slow process, making sure that government works the way it said it would—so telling the story of the work is critical to the continued success and growth of the field. Teams need to be actively pitching their work, cultivating relationships with reporters, or bringing a

People will need to work in new ways, governments will need to hire differently, and the public sector will need to embrace a culture that rewards collaboration and sharing across agencies, sectors, and locales. There is transformative work already under way that does all of the above in places large and small. Some of these stories are described

In Michigan, from 2013 to 2015 over 50,000 people were wrongly denied their unemployment benefits by an algorithm that was found to be wrong 93 percent of the time.20 The Michigan Integrated Data Automated System (MiDAS) was built by a private company and cost $47 million to implement. One part of its failure was that it relied on a single data

Data on its own provides only the what. In order to understand what the data is actually saying, it is important to speak with end users to learn the why. Data used in this way becomes a constant feedback loop between people and policy makers or program managers. It informs problem solvers about who needs what, and whether they are getting those

“What was key, most important for us to think about whenever we had a data set, was not what kind of analysis can we do with the data, but what is missing in this data?” Mashariki concludes. “I need to first find out what I don’t have.”17 Many organizations have data, but having data is not enough. Data needs to be the right kind, and put to use in

Had Mashariki not grown up in a primarily low-income neighborhood he might not have seen the gaps in the 311 data. But thanks to his background and curiosity, he was able to bring a perspective to the project that was sorely needed. Mashariki found blind spots in the data and elevated how neutral-seeming data of one type can amplify the voices of

“I have family members who speak English, but it’s not their first language,” she explains. “The complicated legalese of what you’re asking them is just beyond where they’re comfortable. I understand what people are going through when they’re using these systems—they are nervous. They don’t want to answer the question incorrectly. They don’t want