Using Data To Solve Problems

Tyler Podcast Episode 13, Transcript

Our Tyler Technologies podcast explores a wide range of complex, timely, and important issues facing communities and the public sector. Expect approachable tech talk mixed with insights from subject matter experts and a bit of fun. Host and content marketing director Jeff Harrell – and other guest hosts – highlights the people, places, and technology making a difference. Give us listen today and subscribe.

Episode Summary

Data can be overwhelming. How can local government use data to solve important problems without getting lost in the sea of statistics? Oliver Wise, director of recovery solutions for Tyler's Data & Insights Division gives us some practical insights on how local government can use data effectively.

Transcript

Oliver Wise: Leveraging data in government to drive better results is about organizational leadership, and it takes the ability to cultivate trust from your stakeholders, both senior executives, the community, the city council, but really most importantly, the people on the ground themselves.

Jeff Harrell: From Tyler Technologies. It's the Tyler Tech Podcast where we talk about issues facing communities today and do so in a way that's both interesting and informational. I'm your host, Jeff Harrell, I'm the director of content marketing here at Tyler Technologies. The problem that we're looking at today is data. Data can be overwhelming, there's data everywhere it seems. How do you make sense of it? If you're not technical, how do you use data to solve really important problems? And today we turn to one of our internal subject matter experts, Oliver Wise, who's the director of recovery solutions for Tyler's data and insights division, and spent quite a bit of time in local government at the city of New Orleans. Oliver is a wealth of information. He breaks it down so well, you're going to love this episode. Here's my conversation with Oliver Wise. Now, Oliver, you've had a very interesting path that led you to local government. Tell us a little bit about your background and what first attracted you to serve in the city of New Orleans.

Oliver Wise: I started in local government, really, I've always been in local government. My mom ran for town board in the small town of Mabletown, New York, where I grew up, town of 5,000 and she was responsible for putting in our town's first sidewalk. So I was inspired by her of, would the power of local government to really make a difference, tangible difference in people's lives. After college and after some adventures abroad, including starting a bookshop in the Greek islands, I moved to New York and I started my first real job with a research organization called the Citizens Budget Commission, which was really a watchdog for fiscal policy for New York state and New York City government. There's no better way really to get an introduction to state and local government than through the lens of the budget, because that's really the driving force in how government operates. And when I was at the Citizen's Budget Commission, I was inspired by the work of my boss there, Chuck Brecher, who really came of age during the 1970s fiscal crisis, when New York City was brought to its knees. And he was part of a generation of policy makers that helped that city get back on its feet again, put it on in the path to be the vibrant place it is now. I went to graduate school at the same time at NYU Wagner, the School of Public Service. Katrina hit in 2005, New Orleans, a wonderful place, was brought to its knees much the way New York City was brought to its knees in the 1970s. And so I saw that as an opportunity to really have the similar type of experience there. So I moved with my wife to New Orleans after I finished graduate school a year or two after Katrina hit, started to get involved in the rebuilding effort.

Oliver Wise: And I was part of really a cohort of people, a community of people that came from New Orleans and then also moved to New Orleans to contribute their talents in policy analysis to help that city get back on its feet. Mitch Landrieu was elected in 2010 and I was lucky enough to be appointed as a policy advisor in his administration. One of Mitch Landrieu's main policy priorities, there are two really that I worked on initially. One was dealing with the city's massive blighted housing problem. New Orleans was a shrinking city before Katrina, but as you can imagine, the devastation of Katrina really decimated the housing stock in New Orleans. And when we took office, fully 25% of homes in New Orleans either were in some state of severe disrepair and were uninhabitable. And then the other big issue was the idea of using data to promote efficiencies and accountabilities and improvement in operation. That was a totally new idea to New Orleans at the time Mitch Landrieu was in inspired by the work of many others, including what mayor Martin O'Malley did in Baltimore and then when he became governor of what he did in Maryland, the idea of using data to really manage towards better results. So we put those two ideas together. We launched a blight strategy that aimed at reducing blighted homes by 10,000 by the end of 2014, which was his first term. And in order to track progress on that strategy, we created a program called blight stat and in blight stat, we used data to surface insights on a whole variety of metrics from the sticks of code enforcement to the carrots of redevelopment, to understand what was working, what was not and what we needed to do to improve. And through that feedback loop that we created with blight stat, we were able to accomplish that Mayor Landrieu's goal of reducing blighted homes by 10,000 within four years. And then really in the second term, we use that success to scale citywide.

Jeff Harrell: Oliver, that is fantastic. I know it's super meaningful when you can have such a dramatic impact on a city. What is your favorite part about working in city government?

Oliver Wise: One of my favorite things about working in city government is just how close you are to the action. Rather than working in DC where you're really several degrees removed from policy to actually how that policy is implemented on the ground. If you're working in local government, when you're talking about dealing with housing policy, you're talking about how neighbors actually feel that on their block. It matters to them because that house next door, if that house is in disrepair, that's holding down that homeowner's property values, it's contributing to crime in that area, there might be squatters living in that blighted home, it's going to be more susceptible to fires. So you're not dealing with esoterica when you're working in local government, you're dealing with where the rubber hits the road in terms of how policy actually affects people's lives.

Valuable Experience

Jeff Harrell: What would you say is the most valuable thing that you learned from your experience in New Orleans that maybe still informs the work that you do today?

Oliver Wise: I think the most valuable thing that I learned from New Orleans about using data in government is how much it is a people and an organizational issue as much as it is a technical issue. When I started my work in government, I really thought that if you had the best practice framework that you could borrow from some other city or state who had done it well, you could just transpose that framework onto New Orleans and then start to execute and then everything would be gravy. But what I learned is it's not nearly that easy and really what leveraging data in government to drive better results is about, is it's about organizational leadership. And it takes the ability to cultivate trust from your stakeholders, both senior executives, the community, the city council, but really most importantly, the people on the ground themselves, that when you're in a data shop, that you're there not to make life difficult for the people who are actually administering programs, but you're there to help them. You're there to shine a flashlight rather than use a hammer so that the people with that data, the people working hard every day, can work smarter not just harder.

 

Jeff Harrell: Well, not all government leaders are technical or even comfortable with data. A lot of them don't have data analysts on their staff. How can a non-technical leader still rely success with data while they're also struggling to meet daily challenges and simply stay afloat?

Oliver Wise: When we started in New Orleans, New Orleans did not have a culture of using data to drive better results. And there was really very little capacity at the agency level for using data to drive better results. I mean, you're talking this- I joined city government 2010, that was five years after Katrina, fully half of the city's workforce was laid off after Katrina. So when we came in, departments were really operating on shoestrings budget with very little extra capacity to do sophisticated data analysis or even the basic stuff. I think what's really important is helping people understand just what the value of data is. We adopted a motto that what we're all about is setting goals, tracking performance, and getting results. And if you could put it as simple as that, that's six words, you take the focus away from the data itself to really the problem at hand that you want to address with your data. So I would say to governments just getting started on their data journey, start first with setting clear goals of what you want to accomplish. Is it bringing back your economy after COVID? Is it reducing inequities in your community? Is it addressing the homelessness issue? Be clear with what your goals are, then catalog all those programs and strategies and tactics that you're going to use to achieve your goals. And then, only at that point, start to surface the data that you're going to need to answer very specific questions on how you're going to track progress towards those goals. If you start in that very targeted way, rather than with a data catalog, for instance, if you start in a targeted way, you can focus your efforts to prioritize on surfacing that data that really, really matters, and that's going to drive better results in the here and now.

Oliver Wise: And if you surface that data and focus on surfacing some baseline measures on how you're tracking, and I would suggest you want to attract your workload, you want to attract your effectiveness, the quality of your service, the efficiency of your service. If you can start with some basic questions there, the most important thing is just having good goals, a few good measures, and then a forum where you can regularly iterate on that data so that you can learn from it, create a feedback loop and use that information to adjust policy so that you're driving for achieving your goals. Because at the end of the day, it's not about the data. It really isn't. What's it about is using that data in service of achieving the goals that you set out for yourself.

Truffle Pig

Jeff Harrell: Where does the truffle pig play into all of this?

Oliver Wise: So how does the truffle pig come into all this? Thank you. We started in New Orleans with this idea of performance measurement and management that is using data cycles, track performance, and get results. But as we moved on, we got a bit more sophisticated and we started to use data to help departments address some very specific operational issue. And so the first project that we started out, I'm going to tell you that story before I wind into the truffle pig story, we had an awful event where five people, a family of five, including three children, Jade, Jason and Jala Squire died in a house fire and Broadmoor, New Orleans. And it was an awful event, tragic on so many levels, especially because it was so preventable. That house did not have a smoke alarm. The fire department had an existing program where if you called the fire department, they would gladly come out to your home and install that smoke alarm free of charge.

Oliver Wise: But unfortunately, very few people took advantage of that program. Fire chief superintendent, Tim McConnell, resolved that he never wanted to have an awful tragedy like that happen again while he was in office. He wanted to actually start a program where the fire department would proactively go out in neighborhoods and knock on homes and install those smoke alarms that needed it. So he wanted to be proactive rather than reactively wait for residents to call him. But if you go out at random, you're going to waste a lot of time and effort because finding those homes that actually need a smoke alarm they're, to be honest, rare. We asked how we could help, and he said, "You know what would really help is if you could give us some indication of where we should go first. Where should we prioritize our efforts?" And I said, "I think we can use data to help do that."

Oliver Wise: What we did is we worked with the fire department, develop a model to predict those areas of the city that were most in need of smoke alarms. And most in need was a result of two different analytic insights. One, we predicted those areas of the city that had homes that were least likely to have smoke alarms. And then we also predicted those areas of the city that were most vulnerable to fire deaths. And research shows that it's the very young and the elderly who are most at risk of a death if there's a fire and a house. We put that data together and we created a map for the fire department to go out in a surgical way, rather than out at random.

Oliver Wise: And low and behold, the first neighborhood they went out to Central City in New Orleans, there was a fire seven months later in a house in that neighborhood. And 11 people, including an infant were able to escape with their lives intact, all because of that very cheap, but strategically installed smoke alarm. With the data, the fire department was able to prioritize their efforts to really save lives. As we shifted to that type of analytics work where we're using data to solve really specific operational problems like that, we started with kind of ad hoc projects like that fire alarms project. But what we needed to do in order to scale that work is we needed to provide a framework for people in government who are not data people, but really the program people, the subject matter experts for them to be able to see for themselves where are those opportunities where data analytics can provide value and really make people work smarter, not harder. And that's where the idea of the use case truffle pigs came up. My team, the data team, can't be in all those departments. We just don't have enough capacity to find those opportunities. So what we did is we created this framework, the six recipes for finding good analytics projects so that the program people could sniff out those opportunities, could sniff out those truffles and surface those use cases for us. And what we did was we either executed those projects with our own resources, or we partnered with outside groups and universities to get the analytic resources that we needed to execute on those projects.

Problem-Solving Using Data

Jeff Harrell: I'll be back with my interview with Oliver Wise in just a moment. Hey, one thing that you could do if you enjoy this podcast is to give us a rating and review. Actually, written reviews are one of the best ways that people can find new podcasts just like this one. So just go to your Apple Podcast or wherever you listen to the podcast, give us a review, five star review would be great. We'd really, really appreciate it. And now back to my conversation with Oliver Wise. You talk in your work about the importance of government leaders in identifying solvable problems. What are some types of problems that can easily be solved using data?

Oliver Wise: Yeah, I think it's super important for leaders to identify solvable problems. Make no mistake, when you're in government, you've got very limited time, especially if you're on a political calendar. And if you want your data program to be successful, you simply can't afford a three to four year ramp up period. You need to show results within the first three to six months so you can use that momentum to further your investments in your data capacity. I'm a big believer in that. Where should you start? I would say where you start is, that idea in New Orleans is set goals, track performance, get results. In New Orleans, it was the blighted housing problem. That was an urgent issue that the mayor had put political capital behind. There was, through the electoral process, a clear mandate from the public to be aggressive on blight. So we had a clear problem to solve, and we used data really for the first time ever in the city's history, to use that data, to pinpoint bottlenecks, reprioritize, and resort our resources, and to come up with new policies to make a significant dent in that problem and we were able to do that. And with that success, we were able to get investment from the city council from our budget office to really build out a team of five people, not a huge team, but a big enough team that we could really take that work citywide. So I would recommend to anyone doing this work now is don't go big, don't scale initially. Really start with a very specific policy issue that's important to your executive, that's important to the public, that's important to your legislative body and do everything you can to surface data that can be used to drive towards better results on that particular challenge.

Identifying Data Analytic Opportunities

Jeff Harrell: What are some things other leaders can do maybe to identify practical data analytics opportunities?

Oliver Wise: We found in our work when we were starting out, we really didn't have any framework for finding these opportunities. The best suggestion I had was from Mike Flowers, who was the chief analytics officer for New York City. He had great advice. His advice was go find the backlogs. If you can find the operational backlogs, nine times out of 10, there is some legacy way that the department is going through that backlog. Probably some arbitrary way like first in, first out, right? So if you need a service, you take a ticket and you get in the back of the line. One big opportunity for data is to help assess that backlog and triage that backlog so that you are using your finite amount of resources to address the most important problem first. The work that we did in New Orleans around smoke alarms was kind of like that.

Oliver Wise: We also did a project new in New Orleans, where we had a huge backlog of traffic sign installation requests that came in 311 and there is no way our Public Works Department could ever make through that backlog. So what we did is we used data to identify hotspots in terms of traffic collisions to prioritize how our Public Works Department would assess whether a 311 request for traffic signs was actually warranted. So rather than go through that backlog first in, first out, which would take forever. We resorted that backlog and prioritized our efforts on those intersections that are actually the most dangerous and where traffic signs can make the most impact. So I'd say first look for your backlogs and find opportunities there to triage that backlog for better results. Other opportunities are try to find problems where you can find the needle in the haystack. That's kind of like what we do with the smoke alarms, right? Data can help you identify where those needles are most likely to be. And instead of going out at random finding those needles, or in the case of the smoke alarms, those homes that actually needed smoke alarms, use data to prioritize your efforts in those areas where those needles are most likely to be.

Jeff Harrell: I know you're also involved in leading a virtual COVID-19 peer working group in which local leaders come together, they share ideas and successes. Tell us a little bit about this group and the value that you've seen and of peer communities of practice in general.

Oliver Wise: I'm really blessed to be co-chairing with my colleague, Justin Bruce. Who's great. He was the data analytics director in Jackson, Mississippi, while I was also in New Orleans. We're co-chairing the Tyler Technologies COVID-19 peer working group. What that is basically a monthly meeting where data principles in local county and state governments in our customer base are getting together to share ideas, experiences, and really have some group therapy of how data can be used to address all the multifaceted issues that come with COVID-19 response and recovery. And it's really been a fascinating, very productive group who are in real time, sharing their experiences on how they're using data to help their governments address this enormous, enormous challenge. And we've had presentations that really have run the gamut. We've had presentations from Pierce county, from Julie Demuth in Pierce County, Washington on how they're using data to help policy makers decide to reopen various sectors of their economy. We also had a great presentation recently on how governments, especially the state of Iowa and Ramsey County are using data to drive accountability and transparency on how federal CARES Act funds are being spent to help that local government respond to the COVID-19 crisis. Equity is a huge issue in COVID-19. There's definitely disparate impacts on how the pandemic has affected populations, both in terms of the public health implications and also the economic implications of the pandemic. Chattanooga had a great presentation on there, how they're using data to drive equity in how they address the COVID-19 pandemic. So it's really been a fascinating discussion and people are really learning from each other in real time. Going back to that CARES Act work that we talked about, Iowa had really pioneered a great application of our Secrata Solutions surface data and make that data shareable on how CARES Act monies were being spent, had a really great way of sharing that data and those insights with the public and with the legislature.

Oliver Wise: Ramsey County saw that work, a county government that's not in Iowa, obviously it's in Minnesota. They saw that work and then said, Hey, this framework that Iowa developed is wholly replicable for our context, Kristine Grill who's the open data lead in Ramsey County used that framework that Iowa had developed and was able to quickly deploy that solution for her community and really show value in a short period of time. And that's what we hope to accomplish through this group. It was great that we were able to do it through CARES Act reporting, but I think the more people get together to talk about how they're using data to solve problems, a lot of these problems are not unique to a specific jurisdiction. So if one community, one state or local government figures out, we want those insights to be scaled.

Jeff Harrell: When government leaders share ideas, what are the key ingredients that help them scale, that help them really see success and are able to maybe replicate ideas from one jurisdiction to another?

Oliver Wise: Yeah. What are the key ingredients of scalability? One is, the problem is common to a bunch of jurisdictions. When we were in New Orleans, actually the problem scalability was real, because there aren't a whole lot of cities out there who have been decimated by a flood and had 80% of their housing ruined. At that time, we were looking to other Rust Belt cities who had blight problems, but our blight problem was really quite unique. But here we are in this COVID-19 pandemic and it is truly hitting every community. There is not a community, not a state, or a county, or city who is being spared from the effects of this really unprecedented pandemic. Whether it's addressing the public health issues that come with this pandemic, whether it is under using data to manage fiscal recovery, economic recovery, dealing with the really important equity implications of this issue. I think this pandemic, it's common enough that it's hitting pretty much everyone and communities can learn from each other in real time on how data can be used to address those issues.

Jeff Harrell: Well, Oliver, this has been super helpful. Thank you so much for your expertise and for your willingness to share your experiences and how data can really solve real important problems. Really appreciate you being here. How can people get in touch with you?

Oliver Wise: Thanks, Jeff, enjoyed being here. If your listeners want to get in touch with me, please do either on Twitter through @OJwise or feel free to email me anytime. And my email is oliver.wise@tylertech.com. Thanks so much.

Jeff Harrell: Well, Oliver is such an interesting guy, had a bookstore in the Greek island of Santorini earlier in his career, spent a lot of time in local government with the city of New Orleans, and now helps us understand data and the problems that it can solve. I hope you found this episode really interesting. Well, thanks for listening to the podcast. I really appreciate it. One thing you can do to really help us is leave us a review. We have lots of great episodes planned. So until next time, this is Jeff Harrell, director of content marketing for Tyler Technologies. We'll see you soon.