In this episode Professor Stephen Goldsmith talks with Amanda Daflos, leading expert in public innovation and executive director of the Bloomberg Center for Public Innovation at Johns Hopkins.
In this episode, Professor Stephen Goldsmith interviews Amanda Daflos, current executive director of the Bloomberg Center for Public Innovation at Johns Hopkins University and former chief innovation officer for the city of Los Angeles. They discuss Amanda's data-driven approach to police recruitment in LA, how her local government experience informs her role now, and why it's important for innovation teams to have a seat at the head table of government.
Music credit: Summer-Man by Ketsa
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Stephen Goldsmith:
This is Steven Goldsmith, professor of practice at Harvard's Kennedy School, Bloomberg Center on Cities, with another interesting guest in our podcast series, Amanda Daflos. We're particularly pleased to have Amanda because of her multiple roles in the world of making cities operate better. We met Amanda when she was the Chief Innovation Officer for Mayor Garcetti in Los Angeles, and now she is with Johns Hopkins. She's the executive director at the Bloomberg Center for Public Innovation at Hopkins, a sister program with the one we run at Harvard. And we're delighted to have you.
Amanda Daflos:
Good afternoon, Steven. It's lovely to see you and to be here with you today. Really appreciate the opportunity, and so excited to talk about all things government innovation and government data.
Stephen Goldsmith:
So I think what's really fascinating about you and particularly relevant to our audience is that you did it, and by did it, I mean you applied data to innovation to help address a really significant problem. And now, at the Center for Public Innovation, you're in a position to provide advice and support for cities who want to be innovative using data. So it's just great that you've lived it and now you're teaching it. So tell us a little bit about how you got to be Chief Innovation Officer for Eric Garcetti, what that job entailed, your relationship then with the Bloomberg Philanthropy Program, and then we'll go to the issue at hand.
Amanda Daflos:
Sure, yeah, happy to. My career has been spent mostly in government transformation, so I've had the opportunity now for close to 25 years to be working in the business of helping government really think about problems and address resident issues in a concrete way using data, using all the tools of innovation.
I went to the City of Los Angeles in 2015, and I went to the city because the city had received a grant from Bloomberg Philanthropies to start an innovation team, or I-team. And so I had the opportunity to be the first person there really doing that work and standing up what became one of the most, I think, exciting innovation teams in the world, but also one of the teams that really used the method that innovation teams put forth in a very robust way, and brought things like data scientists to the city of Los Angeles as well as roles like civic designer, behavioral science. There was a whole series of threads and practices and thinking styles that we brought to Los Angeles, all with the idea of public problems in mind.
Stephen Goldsmith:
I want to ask you about a couple of your projects there and then talk a little bit about your work now, but just a little bit more though, Amanda, about where you were structured in LA government, and how you accomplished so much. Did you have direct access to the mayor?
Amanda Daflos:
When I started in 2015, when the innovation team started, we were seated in what was called the Mayor's Office of Budget and Innovation. And in that office, the idea was that we were bringing innovation and budget together. And so I worked with the deputy mayor. We established the team there. And then within about four years, as the team really became strong and launched and was working with departments across the city, part of the benefit of the innovation team in Los Angeles was that we worked with department heads across the city government. So there were 38 departments. I think in my six years there, I worked with probably 20 of the 38 departments on intersectional issues, on a portfolio of problems. And so we were, for the first four years, really department focused, but seated within that Mayor's Office of Budget and Innovation.
And then in 2019, I moved into the chief innovation officer role and moved into Mayor Garcetti's executive team. And so that meant that in the second-largest city in America, innovation now had a seat in the most important office in city government, or the most influential in the sense that it's the executive office of the city.
And in all cases, we had access to the mayor. We worked very closely with the mayor and his team the entire way through, really looking at issues that were of most importance to the mayor, and things that he could see were good fits for the way of thinking that innovation teams have, which is looking at cross-cutting problems, looking at citywide problems, looking at border crossing problems, and really being able to take a longer view, both a short-term view but also a longer term view on those issues, and using data to research what is the problem anyway.
Stephen Goldsmith:
I want to talk a little bit about a problem you addressed in that capacity then, which is actually even a worse problem nationally today, and that's the issue of recruiting officers into urban police departments. I've got a couple questions as I begin. As you know, I've been a mayor and a deputy mayor, and in both capacities, had a dotted or direct line to the police department. Police departments don't often accept innovation suggestions, even from a mayor, right? They're closed systems. They have their own procedures. They have their own standards of professionalism. Could you tell us a little bit about the exact problem you addressed and then how you managed to inject your office into the police recruiting process?
Amanda Daflos:
Yeah, absolutely. We were asked to take on the issue of police recruiting in Los Angeles somewhere around 2018. And Stephen, as you've mentioned, in 2018, there were some people that were looking at the field and saying there's just an issue, right? There's an issue now and there's going to be a bigger issue in terms of recruiting. And some of the issues were, are people interested, are the right people interested, recognizing that "right" is completely a subjective term. Are the police forces diverse? Are they reflecting the population of America today with the recognition that there's evidence to show that diverse police forces that actually reflect the community that they work in really can speak the languages that are spoken in a community. Those things really matter in policing.
And so we were asked to look at this issue of diversity in recruitment and just overall recruitment somewhere around 2018. And my approach to the work was to work closely with the police chief and work closely with his team, his immediate team, particularly the team that worked on this topic, and to work with our personnel department in Los Angeles. Both the personnel department and the police department have really important roles to play in recruiting, and it was equally important that both agencies be deeply involved in not only solving the problem or working to address the problem, but even defining what the problem actually was.
And so I spent a good amount of time very early on trying to work to get clear with both agencies on what it was that they were struggling with and grappling with in the short term, and then what they anticipated would happen in the future around recruitment. And we brought in... I think at one point I had three or four data scientists working to look at their data because police departments have tons of data, and personnel departments have tons of data, but to look at the data with the lens of what is happening in recruitment, where are people falling off in the police recruiting process, and then also, who is most likely... Who's leaving the police department? Why are they leaving? How quickly are they leaving? And also, within five years time, who will be retiring?
So we had a series of questions that really helped us, with the use of data, get clear about what the current state situation was, and then also look ahead to the conditions that might be true in the future. And that was really illuminating in a big way, which I think data has an unmatched truth about it, which is that data can be the thing that actually convinces people to act. And what became very clear through the data was where in the process people were falling off, what about the process was difficult for candidates, why people weren't applying, and really importantly, it turned out, what the five-year projection was in terms of who was going to be leaving the police department based on retirements. And there's a lot of historical information around that last part that we only learned with the police department because we were able to look at the data. And ultimately that let us then create a series of strategies from the innovation side of the house that responded to the data, what the data told us, in the end.
Stephen Goldsmith:
So this is interesting. Let me try to restate what you said just so you can correct me if I've got it wrong. So you gathered data to look at various choke points in the recruiting process where the city was losing candidates, how it could better gain candidates. What did you find that surprised the police department? Give us an example of something that was particularly interesting to them.
Amanda Daflos:
Gosh, there were so many things. I think there are things people felt in their bones but couldn't isolate. It was hard to isolate all the challenges, and then get the data about each challenge, and then decide what to go do about it.
What we started with was really talking to candidates. So the candidate set that might never have been spoken to without my team was the candidate set that fell out of the process, so people who didn't continue. We went and did interviews, and we asked them, "Why didn't you continue? What happened? Was it annoying? Were there better salaries in other places?" There was a whole lore about better salaries in other nearby places like Glendale or Pasadena. Some of those things are true. That's always going to be true. It doesn't matter if it's Los Angeles or Denver or any other city. But what was probably even more important was that the process was difficult at certain points. And so there were certain things in the process that we saw patterns. And those patterns are addressable once you can see those patterns.
And so the patterns became things that we could do something about. The data shows us people are falling out. Candidates are telling us why they're dropping out. What can we do?
The other good example was Los Angeles is one of the cities that has a really incredible program around candidates that are training to be police officers because there's a very high bar, both physically, mentally... Every part of your life is really explored by the personnel in the police department as you're becoming a police officer, which we would expect and hope to see. But to be able to be physically fit, there's what's called the Candidate Advancement Program. And that is a boot camp, for lack of better words, that exists where you could go, Stephen, and you could train before you're even ready to take the test. You could train and get ready to jump over the wall, because it's a requirement to do that, run miles in a certain number of time. And so physically, it gets you ready to be ready to test.
I went, I got in uniform, I ran, I jumped over walls, I did all these things. And what we learned by talking to candidates was that actually, one of the things that was preventing a lot of the people who were in the process from going was that the time that this was being offered didn't fit because people are also working full-time jobs. They're working maybe at a diner or a restaurant or someplace, and they're trying to actually get this job. And so to be able to go to a boot camp at 8:00 AM is unlikely, 5:00 PM unlikely.
So the police department now started offering a program, that I think still exists, at 5:00 AM. Totally reasonable because if you have to get up and go to work at 8:00 or 9:00, you could absolutely go work out. You could run a mile, jump over a wall, do all these things at 5:00 AM, and still go to your day job, and get in that physical shape that you need to get into.
And then the last one, I would say, just as a good example, was we did a lot of analysis on retirements. And what we learned was that there were certain groups of people, truly groups of people, demographics of people, who, based on previous recruitment cycles in the Los Angeles Police Department, were more likely to be retiring in the coming five years at the time. And what that would mean was we would see real shift in the diversity of the police force. And so we needed to be really sharp and thoughtful about how we maintain that diversity in the police force, groups like women amongst groups like African Americans. And then at the same time, there were also groups of people who were overrepresented. That's fine, but if you're going to see retirements in big numbers in other groups, it means that over representation will be even more pronounced. So we were able to ask some really good questions about what to go do about that and look at how you actually recruit in a very thoughtful way to make sure that you're retaining or keeping diversity within the police force.
Stephen Goldsmith:
I think what's particularly interesting to me about your story is the multiple places you touch the process with data analysis that helps you put together a better system. I think we wrote about it a couple years ago for Governing Magazine and Data Smart. In this effort to improve recruitment for Los Angeles Police Department, can you talk us through how you accessed the data and worked across any silos, just so we can understand the scope of the issue?
Amanda Daflos:
In order to find the data or use the data that we needed to really get smart about the challenges in places where we had an idea about the kind of thing we wanted to learn about, we would make requests of the departments in the city. If we couldn't find that data or that data didn't exist, we would actually create that data.
So a perfect example is you're trying to understand what candidates thought about the police hiring process. That's not something that existed in the city's data. And so we were able to actually create surveys, run interviews, do a whole host of other things that really helped us get clear and create a data set around sentiment on the police hiring process.
In other places where we had things that are maybe more traditional, we were able to ask the agencies for those things. And there's a series of levels of what's accessible right city and what's public. We were able to use those things that were accessible to us, and as city employees, generally, the ability to use whatever it was that was provided to us. So we would make those data requests, and then we would work with people in the departments to make sure we were interpreting them in the right ways because data can be messy, it can be all sorts of things. So it was really important to have leaders in the departments we were working with, whether it was on police hiring or anything else, just to make sure that our interpretation as non-department experts was correct.
But we were able to, for everything we worked on, create data that didn't exist, and share it back with departments to answer questions that we had that were not traditional questions that a city might have been asking itself before. And then in other places, make data requests of departments, and really work closely with the departments to assess the nature of that data and how we might be able to use and analyze it.
Stephen Goldsmith:
So Amanda, one of the things I hear in your story is that you can look at the data and see what's missing, not just look at the data and see what's there. And then you go through this exploration process to fill that out, which may even mean jumping over the walls, to use your words. So talk to us a little bit about how you use your data to identify what you don't know, not just what you do know.
Amanda Daflos:
One of the most impactful things, I think, an innovation team can do and be, and people on innovation teams, or in innovation capacity, or frankly, any capacity in city government, is really be curious. And that curiosity, I think, about city services, about how city services can be better, about what residents want from their city services, and also what users get, need, don't get, these kinds of things, I think, are the kinds of things that can help a group really understand what is in its data and then what actually they wish they could see in data. And not always can you create a data set to answer a question, but it turns out that you can get smarter about things you're curious about if in fact you are just curious and think about the users of city services as the paramount place to actually get those answers.
Because it's the case that anyone using a city service probably doesn't think about it day to day like someone like I think about it. I think about it all the time. I turn on my water. I'm like, "Oh, I wonder what happened. How many people had to touch this to make it happen?" But not everybody thinks in that way. But I think groups that are really focused on innovation, the pure curiosity about the way city services work, how people experience them, how they should be experienced is a way to get to this question of what are we missing. And if you know what you're missing, you can also figure out the chances of getting at least enough information to know something, if not a lot of information, to be declarative about why something is true.
So there's lots of different ranges of what you can get, but it's always a great idea, I think, to both be curious and think about the people who use a thing, and how you get more information about that thing from them.
Stephen Goldsmith:
Excellent. Let's jump forward now to your job at Johns Hopkins for Bloomberg. What did you learn in LA that is particularly important in your current work for cities at Bloomberg?
Amanda Daflos:
My gosh. I mean, I learned a lot of things, obviously, Stephen. I think one of the things I've learned about innovation work, innovation in cities, is that it is really important to be invested in the people who work in a city and, at the end of the day, actually know the problem better than anyone else, and may never have been asked to give their ideas, may never have been asked for their data, and probably never had the sort of level of analysis that can be done on existing city data that actually creates a pathway to solutions and policy change. It is the case that most agencies and cities don't have data scientists, don't have behavioral scientists, don't have project managers. There's a skillset that innovation groups or innovation people bring that, paired with pure city policy expertise, is incredibly powerful.
But I think for me, it was the idea of putting people first, and analysis as a really close second, and together, the two, I think, just create runways for a lot of innovation. It is an important balance that I think people with a lot of intentionality can do in a really remarkable way.
Stephen Goldsmith:
I like your story. I was mayor so long ago that data analytics as a field didn't exist. When I wanted to gather data about better ways to pick up trash, I went out and picked up trash.
Amanda Daflos:
Exactly, yeah.
Stephen Goldsmith:
You're jumping over the wall. It's a way to understand things from the perspective of the people who are involved. So I appreciate your point. Let's close with a plug about the Bloomberg Center for Public Innovation. What is it about your current work that should be particularly important to our listeners, and what resources do you offer?
Amanda Daflos:
The Bloomberg Center for Public Innovation is more and more every day the place to go for support on a method that has to do with how you really transform the way that government works when it comes to innovation. And so we are really excited to be offering a whole host of grant programs, a whole host of training programs, and day-to-day support from a research and academic perspective to government officials globally. Today, we're working in about 50 different cities globally, and we have the opportunity to do that through a wide variety of programs that are all accessible, of course, on our website. But we believe fundamentally in the opportunity for government to really meet people where they're at and lift up the voices of government officials in that exercise of transformation.
Stephen Goldsmith:
Well, you have a remarkable story, and you're in a position to help others emulate it, so we're delighted to have Amanda Daflos, former chief innovation officer in the City of Los Angeles, and now executive director at the Bloomberg Center for Public Innovation at Johns Hopkins as our guest today. We'll continue to watch your wonderful work. Thanks, Amanda.
Amanda Daflos:
Thanks, Stephen.