Transcript
Speaker: This episode of the PolicyViz podcast is brought to you by JMP, Statistical Discovery Software from SAS. JMP, spelled J -M -P, is an easy to use tool that connects powerful analytics with interactive graphics. The drag and drop interface of JMP enables quick exploration of data to identify patterns, interactions, and outliers.
Speaker: JUMP has a scripting language for reproducibility and interfacing with R. Click on this episode's sponsored link to receive a free info kit that includes an interview with DataVis experts Kaiser Fung and Alberto Cairo. In the interview, they discuss information gathering, analysis, and communicating results.
Speaker: Welcome back to the Policy Vis podcast. I'm your host, John Schwabisch. On this week's episode, we are going to go out to the west coast of the US and talk to Meredith Lee, who is the executive director of the West Big Data Innovation Hub and sitting at the University of California at Berkeley. Meredith, how are you? Welcome to the show. Hi, John. Thanks for having me. Good to talk to you again. It's been a while. I think the last time I saw you was at the Big Data Summit here in DC.
Speaker: Yeah, that sounds about right. Cool. So you were here for a while and now you're out in California. Maybe we can start by having you give your background for folks and just go right into what is the West Big Data Innovation Hub. Yeah, absolutely. So I have a very diverse background before joining Berkeley and becoming the executive director of the West Big Data Innovation Hub. When we had met John, I was a science and technology policy fellow.
Speaker: in DC working with the Department of Homeland Security and then had all sorts of
Speaker: great experiences working on the federal big data R &D strategic plan. I was leading the Innovation for Disaster Response and Recovery Initiative under the Obama administration and sort of on my nights and weekends doing all sorts of things like producing the first National Maker Faire. So there's no shortage of exciting things going on in DC. I think if I look back on my resume, you usually have
Speaker: one place that you're at for a year or two or something like that. And it was like, Oh, I had, you know, five or six different roles. So it was great for, you know, making those connections, um, getting an idea, uh, cause I had never worked in the government ecosystem before. Um, and I think, you know, the government is probably known more for bureaucracy than a creativity. So, uh, you know, it was, it was great to have these,
Speaker: underground sub -basement level support groups where you're allowed to paint the walls green and have moving whiteboards and
Speaker: People don't look at you like you're crazy when you brought in a bucket of Post -its. Yeah. That's what innovation is in the federal government, right? It's like Post -it notes and paints. So OK. No, I'm familiar. Those small things definitely helped add some flavor to the time in DC. I was unsure of the whole, can I do this wearing a suit thing? Because before that, I was a grad student and post -doc, very, very much in the lab working with hardware.
Speaker: And then thinking back, I think the motivation for developing data science partnerships really started probably during internships in college and grad school. I always found myself most excited when we were working at the intersection of fields and I was making the rounds, you know, physically to different departments and silos.
Speaker: and figuring out how we could essentially engineer something that would stick, would be robust and incorporate all these different constraints and end users. And so I end up doing actually quite a bit of that still in my current role. An example is when I was doing my PhD, I was actually looking at life or death situations, real -time monitoring of people's blood. And so that was where I really got the bug of
Speaker: applications focused science, you know, whether it was in surgery or out in the battlefield, wanting to base your actions on reliable data was really the key there and being able to, you know, for a patient, add blood centers or add coagulants and take corrective measures. That was really, you know, my first foray into this, you know, decision as a grad student you have and are you looking at, you know,
Speaker: systems or that meta view of things? Are you looking more at the technology or the human interface? And how comfortable are you with being sort of out of your comfort zone and working across disciplines? Yeah, I should actually go visit my PhD advisors and thank them for making me very comfortable in those, you know, I'm sitting in a room with biologists and I'm an electrical engineer. What is this?
Speaker: Right, and so you try to bring over the course of the last few years, you try to bring all these different groups together. Have you found that there's a certain uniting theme or concept that easily sort of helps those different types of groups and still sets blend together?
Speaker: Yeah, I work with designers, I work with folks who identify as data scientists, folks who essentially are doing data science but don't really know to call it that, policy makers, entrepreneurs, et cetera. We try to be really inclusive.
Speaker: I think, you know, at the outset when I was recruited by Mike Franklin from Berkeley to lead the hub, and he introduced me to our other principal investigators from San Diego and the University of Washington, I was really struck by, even though there's a three -hour time difference from, say, the West Coast to D .C., there's definitely
Speaker: you know, barriers in terms of culture or just terminology and comfort level of working across sectors. So I think when we met, when Mike and I met at the White House Data to Knowledge to Action event, there was a breakout session. And that was a time when the big data initiative under the Obama administration was just ramping up. And most federal agencies didn't have, you know, Silicon Valley presence or really that much
Speaker: engagement as we do now with the West. And so, you know, we saw this opportunity and the shared interest to explore ways to collaborate and it really is like this sort of catch all of you've got data science, you know, that tech bread and butter that I grew up on and that, you know, you're quite comfortable with as well. And then the partnerships angle has a lot to do with the human centered design elements and looking at
Speaker: the broader ecosystem and then putting both of those together to work on compelling applications. Luckily, our applications are societal benefit. I don't run into that many people who are like, oh, no, we shouldn't work on health care or precision medicine, smart cities. We really want to have that dumb city. Actually, that could be a whole other podcast in and of itself, that interface. But I think in general, people are on board with applying data for social good.
Speaker: I want to talk about some of the projects you've been working on from the Data Hub. Can you talk a little bit about what it is and how it came out of the Obama administration and what your group focuses on? Yeah, absolutely. The Big Data Innovation Hubs are a new venture. We've only been around for about 16 months or so. It was launched with seed funding from the National Science Foundation.
Speaker: And our mission is really to build and strengthen partnerships across industry, academia, nonprofits, and government at all levels. We address societal challenges, as I mentioned, and it's really about accelerating the adoption of a data -driven solution. So, you know, as you know, it's not always just the feasibility of the tech side of things. It's, you know, about understanding the humans, the policies, the real world factors that are
Speaker: you know, really essential to making something stick and be sustainable. So we do a lot of convening and coordinating and building that like connective tissue. Yeah. It's of regional and national priority. Yeah. And it seems like having done all this sort of networking connecting over the last few years that you've been doing lends itself pretty well to doing this across different states in the West and all those industries and academia.
Speaker: Is there a particular project that you really enjoy either because it's such a challenge of trying to get these different groups together or because some of the technology or data constraints or both? Yeah, absolutely. I think the networking and connectivity practice has definitely, as you said, helped, but I've never really done it at
Speaker: you know, this scale. We have 13 different states. You know, that's what was so exciting and drew me to this challenge. You know, everyone from the folks in Alaska and Hawaii to Montana, Wyoming, Colorado, New Mexico. And before this gay guy had never been to, you know, Wyoming or New Mexico, now I can say I have.
Speaker: And we're making it out to Montana soon. But I think in terms of our thematic areas, when you look at transportation or justice data, like our police data initiatives, that has been something that's been really interesting because it has that mix of
Speaker: you know, the technical, you know, launching an open data portal with the city for the first time, but also really deep roots in terms of the social and community building aspects of it. So, you know, that's something where, you know, we're always looking from the leadership perspective of how do we measure progress? You know, you've got the numbers.
Speaker: from how many people have participated in different workshops, how many people are getting inspired by our community gatherings and leading their own types of events. But a lot of the really powerful testimonials, and we started talking about that real connection with our stakeholders, have been more anecdotal, at least to start. And so one of my things that's at the top of the radar right now is
Speaker: Our hub has been working with the public policy folks in Boise, Idaho. Haven't been to Idaho yet, but I've had lots and lots of phone calls with them. We'll get out there soon. It's a great team. They're working at the intersection of data and the criminal justice system. How might sharing police data increase community trust and community engagement?
Speaker: And I think this is a really powerful example because it's something, it's a connection there that you could really argue probably would not have happened if it weren't for the West Big Data Innovation Hub. I literally got a call from the director and assistant police chief from Tempe, Arizona.
Speaker: a long ways away from Boise, Idaho and a series of emails and video chats and different roundtables ensued and now we're all collaborating together on the city's first open data portal. The first data set will actually be police data. We've got industry partners that the hub is bringing into the fold. There's volunteer data visualization consultants and our friends who are data journalists also coming into
Speaker: that mix. So it's a really interesting combination of all of those different elements into a project that has a real tangible result for the community.
Speaker: What's the relationship like between someone who's sort of working for the federal government and then going into, or not going into, I don't want to make it sound like that, but partnering with places that are more state and local. Does that relationship change or does it work out in odd or unique ways that you're coming from the federal government and in trying to help local areas work with data in better ways?
Speaker: That's a really great question and having worked with all these stakeholders and having sort of had that hat on at various points.
Speaker: In my career, I can say that one of the real benefits of the Hub is that we have this neutral, safe place, essentially, where everyone, whether you're federal, state, local, territorial, tribal, or you're an academic or industry, you feel that we're all
Speaker: Deeply well deeply care about these topics everyone can contribute in a valuable way And so, you know one example of that is you know, if you get competing companies at the same table you know, that's something where you know, maybe some of the motive or all of the motivations from
Speaker: you know, a given stakeholder might not align with the person you're sitting next to, but recognizing that there's this wide range of stakeholders who can all contribute and engage is really like one of our strong points as as this neutral hub. So I think, you know, the ties to universities that are well respected, the, you know, openness to bringing
Speaker: all sorts of stakeholders to the table has really helped us. Right. Well, it sounds great. And because March is the month of storytelling for me, I want to turn our attention to the science of data -driven storytelling that you have been working on. Can you give me a sense of what those meetings have been like, and then we can talk about stories and data and whether we're going to have to fight or not?
Speaker: I'm thinking maybe we're not going to have to fight. But maybe we will. We'll throw down a little bit. But I think you're going in. You have a two -part agenda, if I'm not mistaken. Well, so maybe you could just talk about it a little bit.
Speaker: Sure, the Science of Data -Driven Storytelling was a series that kicked off in the summer in LA. It was funded in part with the Computing Community Consortium and we worked with a startup that has now probably outgrown its startup status, Data Science Inc.
Speaker: I'm so they have the lovely domain data science dot com which is great and sort of in retrospect i guess we should have realized that it was going to be this popular but we thought we were hosting the small get together. Having people share their lessons learned and best practices in terms of sharing how they communicate their work with a broader audience.
Speaker: It very quickly grew into this multi -location two -part event with the community. We had the Chief State Officer from the City of LA, folks from the LA Times, New York Times all coming.
Speaker: for this series and it really had this community of practice I guess sort of launched with this first event we had over 700 participants a lot of those were dialing in from you know more than 15 countries which we didn't anticipate
Speaker: We thought we were just having this West Hub event. We actually had 25 different states, so half of the states in the country joining in as well. Matt Daniels from polygraph .cool, who did a really interesting data visualization project on gender in film, spoke about his method, and we had folks speaking of police data. Chris Keller from KPCC was talking about his hashtag officer involved,
Speaker: story and interactive visualization to look into police shootings in LA. And we had folks from Project Jupiter. I don't know if all of your audience would be familiar with that, but it's an open source interactive data computation platform and they actually just announced their first Jupiter con. Hopefully it's as well received as Comic Con. It's in New York.
Speaker: The summer and i think it's a really exciting project and so bringing all of those different community members who are growing the the corpus of data driven stories was fantastic and being able to you know share common pain points and
Speaker: different methods of coming up with a compelling story was really the uniting factor. So let's talk about the story part. So how did people there view this phrase of data -driven storytelling? Do they view it as an actual story, the way we might think of a traditional story, a novel or a book or a play or an opera or whatever? Or was it more about, well, story is just sort of at the end, but I'm really interested in the data -driven part.
Speaker: I think there's a spectrum. Because we brought university researchers who were struggling to figure out how to get public engagement about their story, whether they needed crowd -sourced data from folks or they just wanted the public to be able to understand and support their work versus some folks who are much more
Speaker: at journalism basics and understanding that traditional story with the arc and all of that. So everyone had their own perspective, but I think we were much more on the end of the spectrum where it was looking at the call to action, the hook that pulls somebody in describing the headlines and the taglines and making it very engaging for the audience.
Speaker: So, I'm afraid there's not too much throwdown or arguing probably on the same page there. You know, I don't think that a bar chart is a story. Yeah, you know, we'll stop. Okay, so we are on the same page. All right. Well, we'll have to throw down about something else then.
Speaker: So when these different groups are sitting in the room together, do they have the opportunity to really sit down and try to hammer out some issues and problems? So if I'm running say a data portal in my city and I talk about it in front of a room of how many people.
Speaker: I often feel like a lot of these conferences are, I present my thing and then I go to the next session and there's not a ton of the sort of real collaboration that I might need because I'm – like you said, you're trying to solve a problem or there's a pressure point that I need help with. So do these events try to get people sitting down with pen and paper or computer and whatever and try to hack out or hash out some of the problems and the technical issues?
Speaker: Yeah, I think you bring up a really great point in terms of, you know, every sort of event or activity that we're pulling together, we try to be very intentional about the interaction. So there really isn't a lot of, you know, sitting in an audience and just listening to somebody and then, you know, going up afterwards for questions. We have much more of a roll up your sleeves, you know, get around the table.
Speaker: have a graphic facilitator or other ways to really get much more involved than just taking notes and raising your hand to ask a question. I think one good example of that was an event that we had
Speaker: just a couple months ago on building up data science capacity in organizations. So that was a joint with a whole bunch of teams, the Obama administration's White House data cabinet, the interagency policy working group.
Speaker: I think we had Department of Commerce, DHS, NSF, and IDEO, the consulting firm. So I think, you know, having about eight co -hosts is very indicative of how collaborative of a business data -driven, you know, organizations is. And that particular workshop, we actually use this IDEO framework of creative tensions. Have you heard of creative tensions at all?
Speaker: No. It's really cool. I know. And you know, this is something that you can bring out at your dinner parties. You essentially ask, you know, are you stripes or solid? It was near the holidays. So it's like Grinch or Scrooge, big data, data science, etc. So you have different ends of the spectrum. And then people get up and vote with their feet where they are along that line.
Speaker: So you're actually doing this, you know, moving with your body, physical, you know, data visualization. And it gives everybody, you know, there may be quieter folks in the room, some way to contribute to the conversation. And then, you know, some folks will raise their hands and say, Oh, I'm standing here because you know,
Speaker: I have this, you know, perspective for our organization. So it worked really well in terms of figuring out where people stood and also sparking conversation about should things be top down or bottom up, like how might we empower small data science teams within a bigger organization.
Speaker: We try to design our events a little non -traditionally and have those different engagement options so that we can help foster those relationships. At the end of the day, it's all about the relationship. I'm glad I now have a name for this because I do do these activities in class.
Speaker: Oh, cool. And did a really interesting one, must have been a year or two ago in New York, where it was an event on data ethics. And we asked different questions and the group had a stand -along spectrum of where they stood on these different data ethical questions. So now I have a name for it though, which is nice. And I also like that Tensions is right there in the title, so it's great.
Speaker: Before we sign off, I do want to talk about one project that you had emailed me about because it has a great acronym. The Water Task Force, can you talk a little bit about that and the different groups that you've pulled together and the goals of the group?
Speaker: Yes, absolutely. I've got to love the acronym on that one too. I mean, it really just sort of captures the need for a call to action and bringing all the folks together. If you look at the West, the 13 states, our bodies of water actually physically connect across state lines. So that's a huge opportunity to have collaboration regionally.
Speaker: For example, the Colorado River, you know, produces $26 billion of economic output and has like a quarter million jobs associated with it. And that whole river watershed, you know, spans seven out of our 13 West states. And there's 30 plus million people in those seven states and Mexico who depend on the Colorado River for their water supply.
Speaker: So we've been making the rounds in all of our different states and seeing that there really is this shared desire to collaborate more on some of our water resource management projects. California has 2 .7 billion dollars earmarked to improvements and a big chunk of that is looking at how we can
Speaker: update some of our state's very in need of updating systems in terms of data sharing and coordination across all of our different regions. So yeah, I'm getting to a lot of meetings with folks very interested in talking about groundwater and better data about how we as a community are using and saving water.
Speaker: So, a lot of interesting things to watch for in that space. And we're always taking offers for help and leadership in that water task force. Yeah. I mean, it's fascinating not just only because you get to work with all these different groups, but you're working in all these different social policy, public policy areas that I'm sure like, I don't know, maybe you've never thought about water task force, water usage. I know I haven't, but it would be fascinating to just sit on these meetings and talk with these experts.
Speaker: Meredith, it's been great having you on the show. It's really interesting. I will put, of course, links to all of your work on the site, and if folks out there have expertise or thoughts that they want to lend to the work, I'm sure they can get in touch with you. Again, thanks for coming on the show. It's been great. Thank you so much. Take care, John. Take care, Meredith, and thanks to everybody for tuning in to this week's episode. It's been fun as always. So until next time, this has been the Policy Viz Podcast. Thanks for listening.
Speaker: This episode of the PolicyViz podcast is brought to you by JMP, Statistical Discovery Software from SAS. JMP, spelled J -M -P, is an easy to use tool that connects powerful analytics with interactive graphics. The drag and drop interface of JMP enables quick exploration of data to identify patterns, interactions, and outliers.
Speaker: JUMP has a scripting language for reproducibility and interfacing with R. Click on this episode's sponsored link to receive a free info kit that includes an interview with DataVis experts Kaiser Fung and Alberto Cairo. In the interview, they discuss information gathering, analysis, and communicating results.
