Imagine your organization is a house. The tech stack is the pipes in the house, and the data is the water that flows through the house. When building a house, there are lots of things to take into account to build a successful plumbing system. The same goes for data – your tech stack needs to be correctly accepting the data from the business process that feeds it, you need to size the pipes adequately to allow the data to flow from point A to point B, and you need to have the right tools on the backend to get the water out of the pipes and make the water usable and palatable. 

Come listen in as Dr. Liz Crowe, the new director of Urban AI, talks through her insightful analogy between data flow in an organization and plumbing in a house, and what she’s doing to revamp Cleveland’s data analytics. She talks through barriers, lessons learned and the goals she hopes to achieve. 

You can learn more about Dr. Crowe and her work here:


What You Can Do

Think through your organization: what is the state of your data plumbing? Does it need to be upgraded? Is it the right size for the demands it needs to meet? Is data is flowing to the right people without “contamination”? Where might some improvements have the biggest impact?

Expand to read (auto-generated) episode transcript.

I am thrilled to be joined today by Liz Crow, who has an incredible story and journey she is embarking on. So, Liz, I’m going to hand it over to you to introduce yourself and tell us where you’re coming from.

Hello. It’s great to be here today. So I am here from Cleveland, Ohio, and I am the director of Urban Analytics and Innovation at the City of Cleveland. I have only been there about four months and I am charged with building, tackling, taking on a data department and data enterprise for the data division for the entire enterprise that is the city of Cleveland and coming out of a nonprofit world.

So definitely familiar with the exciting world of nonprofit data. But jumping back into, you know, government data and how we do data for municipal government. And I was so excited to bring, I should say, Dr. Kroes story. It’s always very important to include that her story here, because we’re going to get it from the beginning. We get to see what it’s like looking you up the mountain of where you’re headed.

And so that’s what I was so excited about this. So I was wondering if you could set the stage a little bit for us of what’s going on in Cleveland right now. What did you step into when you took on this incredibly audacious task work? So Cleveland’s in a really, really exciting site from a data perspective, but also just from a city perspective.

So we have a new mayor for the first time in 16 years, and our new mayor is the very first millennial mayor of Cleveland. So our previous mayor leaving office was in his early seventies. He’d been there for six years and I would say he ran an incredibly stable city. The city was in good shape. It was chugging along.

We are now very much in culture shock mode, where we have a 35 year old mayor who is incredibly dynamic and incredibly excited coming into the city and ready to sort of shake up some change and bring some modern management and modern leadership into the city. And a big part of that modern leadership is data. And so he has brought me in and tasked me with really how do we do data in municipal government?

And a lot of cities have been doing data incredibly well for a very long time. There are a lot of awesome leadership partners, but not a lot of cities just kind of didn’t touch it for the better part of two decades. And so we’re at the very beginning of climbing that data. Hill And we have an enterprise or a city wide institution where we have pockets of really, really good data.

We’ve got major systems, we’ve got software in place. It’s running, it’s been running for a while. We have other areas where that software has been running for a while and maybe it hasn’t been updated for five or ten years. We have other areas where we haven’t even taken on data in those spaces, and so we’ve got sort of the breadth of really good and really beginning sort of data users.

And so my challenge is not only how do we think about getting data into those divisions, getting them to have data, getting their data stable, getting their data systems stable, in some cases partnering with our technical integrity teams to put data in place where we may not have those systems, but also teaching our managers and our leaders. How do you use that data to make decisions?

Because just because you have it doesn’t mean you know what to do with it or know how to make a decision with it. Something that gets me so excited about this story is really that it represents every aspect of what’s required to be successful. And you are so aware of all of those things. And I love you mentioned that there’s infrastructure, right?

Just the actual piping to get data around the collection of it, figuring out, well, first we actually have to have something to put in the pipes. In some places we don’t even have anything to put in the pipes. And then even once we get it somewhere, you have to have that learning culture and a data savviness to be able to be comfortable receiving it and putting it to use.

Having data is not the same thing as using data. And so, you know, the fact that you so succinctly outlined all of those things as challenges of what you really need to be able to do to be successful. And I think the shift that you’re looking at is one that’s happening right now in the nonprofit sector. We see, as you said, places that have become quite savvy with data, places that have some data, but it hasn’t really been tended to for a while.

They need some upgrading and then places that have never touched it before. And so you’ve got it all under one roof. We do. And the analogy I keep using as data is a lot like pipeline. Like this is what I keep telling everybody. If we think of data as plumbing, in plumbing, you’ve got your pipes and you’ve got your water flowing through the pipes.

I think of the pipes as our technical infrastructure. So that’s our systems, our software, our tools that we’re using. You know what people log into every day, Our power, we are tableau to get that information out. The water flowing through the pipes is the actual data that needs to flow through. And so you’ve got your business process that’s feeding data into the systems.

And part of my challenge and part of my team’s challenge is really taking a look at those business processes and making sure your technical infrastructure, your pipes, is accepting the water in the right way, that it’s really reflects the business process. And then you’ve got to get your technical teams involved to make sure that that water flows through.

And as we as we as data people know and data practitioners know, data never, rarely it rarely stays in one place, you know, go from system to a server to an analytics tool, and sometimes it hits two or three servers in the middle. So making sure you can move that water from A to B and it moves through cleanly and then making sure we’ve got the right tools and the right training on the back end to get that water out of the pipes, turn it into information, something that’s usable and drinkable and totally palatable for those out there on the ground and trying to take a holistic approach and meet departments where they’re at and critically rightsize their pipes. And so you can’t have pipes designed for a two storey house in a four story apartment complex and you really don’t want pipes for a four story apartment complex and a 30 storey sky rise. And so you’ve got to kind of look at each department, say, okay, what are your processes, what are the systems that you need?Are you good? And then how do we get it out and close that feedback loop so that those managers can take what’s going into the system and use it to drive better information in, make decisions, and kind of close that all around. And it’s a tough challenge to have it all under one enterprise because you have to meet every division where they’re at.

And just like in that nonprofit space, you have to meet every nonprofit where it’s at because each of them are different, just as you said. And that is certainly a challenge that we’re going to be facing and worth it very, very beginning of that. Hill Which is exciting but also daunting because you go, Oh my gosh, we’ve got to figure out what is where and figure out how we start climbing that super high.

Absolutely. Really, What have been some of the early challenges for getting all the way to the peak? You know, you’ve got a long journey ahead of yourself. What have been some of the big barriers or challenges or things you’ve had to solve right from the get go?


Yeah. Step number one was really starting to land a team of data experts that data Center for Excellence that can serve as sort of that slope back point where people can ask questions, they can go in and they can root out some of those wicked challenges.

A lot of people can do data. A lot of people can be really good at data. There are some really good data competencies in terms of just how you solve puzzles for data. So I step number one for me was get a team in place. And then step number two for me was a rebrand. So renaming my division, renaming the team that is working in that division specifically as a data team so that people were oriented to what we were doing and why we’re asking questions.

And part of that rebrand is explaining to people, okay, we’re not coming in to be difficult. We’re coming in because you can kind of hacking it for a while. We think there’s a better way to do it. And I work with incredibly smart people across the enterprise. They’re going to the conferences and they’re watching the super awesome presentation from the software vendor they’ve used for 20 years.

It’s like, Here’s all the cool stuff you can do. And they’re going, How the heck do I get what I have at home to that super cool thing? And we’re a team that can help you start to do that, but you got to bring along the hearts and minds and kind of get them to understand where we are first.

So we’ve been deep and just redesign and rebrand. The Mayor has renamed our office to Urban Analytics and innovation so heavily more data, heavily data focused. And now we’re starting to jump into what’s the data and where is it. So just taking an inventory, what are we got? What’s the state that it’s and you know, how many rooms do we have in this building that we have to run warming through?

And as we start going into each of these challenges, how do we start to our next gen, our future challenges, how do we start to address some of these challenges and weed them out and sort them out? Once we know what they are, we can start prioritizing, kind of chipping away at each of those challenges. I feel like any organization of any size, what you just described is if not, the only way to start is one of the best ways to start.

You can’t solve problems if you don’t know what they are. So like you said, getting that lay of the land, understanding how many rooms do we have in the house? You can’t write, say something for something. You don’t know what the size is. But the idea that you started with identifying who your experts are in your case by adding to the team, but it was also identifying who already existed out there with expertise that you could bring more, you know, to be part of this team, identifying who those champions were, where those skill sets already existed before you even set out to augment them.

Absolutely. And they’re there like somebody has been keeping this thing afloat to the best of the resources that they have been given and the best of their skills. And I had so much respect for the person who’s like me, and we’re keeping this entire system afloat and in some cases have been given very few resources to do so.

And you go, Oh, yeah, okay, what if we throw some resources in a little bit more technical skill at this? Where can we get to? The possibilities are awesome and you have to respect that existing expertise. You can’t just dump a whole bunch of new people at it because each of these systems, each of these data systems got there through a process.

You know, we ran the pipes up and down in left and right instead of like down and up and right and left. So making sure that, you know, a little bit of that history, you understand because you want you don’t want to not invented your mentality. You want to learn from the lessons that they brought to the table.

And that’s huge as you’re doing that sort of organizational change and bringing along, you know, this stuff that exists in the cards and minds, getting them to buy into what you’re doing. And that’s what I loved so much about that second step. The fact that data at its core is always a people exercise, right? It’s yes, you need some technology.

There does have to be some infrastructure, but the infrastructure is pointless without people behind it and bought into it and maintaining it and using it. And so the fact that you’re starting there before you’re even worrying about what the pipes are going to look like, I’m saying, no, no, no, we’re going to get together. We’re going to all be on the same team here.

This isn’t about an alien invasion landing and taking over internally. We’re really driving this change together because we all want the same thing. You want our city to succeed and flourish. And data is a critical element of that. So we’re going to make sure we all have the resources that we need. And maybe there are some skills we had to bring in because they just weren’t present.

But we’re going to spread those out. We’re going to get everyone up, up and running in the same place. No one’s going to get left behind from this. So that idea of the rebrand, even if it’s you and one other person, is the internal team’s established within what you’re there to do and making sure everyone’s bought into that.

I think such an incredible and powerful way to begin. And I would say the other gifts that I have been given in this role is new leadership who’s coming in with an incredibly strong vision of, I want to use data, I want to do data. We need data to make decisions. How do we get there? And so enterprise wide, the entire institution is in a bit of culture shock mode.

And so making sure that, you know, as we’re come in, say, hey, we’re going to do data, there’s a team coming in, it’s like, yup, we can land this plane that they just told you you need to start building and flying at the same time. And so making sure that there’s the leadership support behind it is huge because there are sticking points where somebody goes, We’re doing fine.

And sometimes you do need leadership to come in and go, not as fine as I thought you were, and that’s okay. And those are hard conversations, but they are real and sometimes they need to happen. I have been surprised at how many people are just going like, Oh, thank God, thank goodness you guys are here to talk to us about this because we’ve been begging for somebody to help us with this for a long time.

Absolutely. And I do agree that if you are hoping to have the kind of full scale change that you guys are looking at, that it does require vision from the top. You have to have leadership that is 100% committed in terms of committing resources to it, but also committed to that vision and saying, yes, this is where we’re going to go and we’re going to stand behind us and tell the story about ourselves that is going to forward us along this path.

And you have to have buy in from all the levels down below. You need both halves of that equation. So I agree that that is an incredible gift and it is really the only way to get that kind of just wholesale change. Yeah. So as you sort of tackled some of these barriers of having to design your team and build this, what are some of the lessons that you’ve learned so far, some things that you have found that have helped you along the way?

Absolutely. So I would say, number one, find small wins. Everybody says this and everybody goes, oh, yes, we can find them. Picking small wins is really critical. And the challenge I have for my team is not to tackle what I call the pick list style problems. So you can go in and you can go from like 8 to 9 values on the pick list.

00;14;25;23 – 00;14;56;12
And yes, we can get those resolved. But if we want to tackle Wicked, systemic, enterprise wide challenges, we can’t spend all of our time on just fixing pick lists, which as data people will get, would pick less are. And so as we’re sort of jumping in, we’re looking for those really, really good sort of wins that we can take on that will start to buy that trust and buy that goodwill and bring us in where we can say, hey, yes, we can deliver a dashboard.

And in the process of delivering that dashboard, it gets our team to have a conversation with the business owners on the ground. It gets us access to data that we may not have had before. It gets us to have a data quality and process conversation that we may not have been able to have. And oh, by the way, we can turn it around in two weeks, which they got.

Ooh, that’s pretty good. Do it again. And all of a sudden we start operating at scale so we can start to hit some of those volume kind of modes of operation. And in an enterprise as large as the city of Cleveland, you have everything from public safety to public health to public works and operations to parks, to public utilities.

I mean, we have a breadth of types of data, types of services of groups. And so taking quick wins is not always easy because everything needs to be done and it needs to be done a little bit everywhere. I would say sort of the second success we have had is really slowing down to meet people where they’re at at the beginning of this whole and trying to understand where people are at and what they have.

And we have some who are coming in and they are so excited to get a dashboard. They’re like, Can I just have it by Friday? I’m like, With what? Like what’s included? Sure, but including one. And then we start churning through it and it’s slower than they thought it was, but it’s like you start pulling that card and it’s harder and it’s hard and it’s hard.

And then all of a sudden it’s just a little easier because you get to start to get some momentum behind you and as I have sort of brought in some analysts and some technical staff, they’re a little frustrated because they’re like, man, I know how to build a dashboard. Like, I should be able to do this in a day.

Like, I got this fax, I know what I need to do, and they’re having to slow down and go back through. And I would say one of the wins that we have, as crazy as this might sound, is we’re getting a little frustrated and you can’t solve wicked challenges until you get a little frustrated. But when we get a little frustrated, we kind of get into that meat of it.

00;17;02;02 – 00;17;32;09
Then we know we’re working on something real and we have a lot of real stuff to jump into where we’re going to be really, really frustrated. And I’m really just preparing my team to be horribly frustrated on different projects. Not all of them at all, hopefully, but enough of them that it matters. So yeah, I love that. Well, one of the things that really struck me as you describe those three lessons is that it’s clear you’re playing for the long game, but you’re not here to just do something flashy for six months and move on.

I like that you are building up a strategy that is going to get you all the way up that mountain. And the fact that you said it’s not just about finding small wins that, yeah, we can do a check, you know, like you said, of adjusting to pick list items, but they’re small wins that move you forward. They generate that value in key areas so that while you’re starting slow where you intentionally start slow, pick those small wins in such a way that they help you build momentum.

It’s not just about picking random things you can do quickly. It’s about picking those small things that get the cart moving. Just that little bit. Like you said, it gives it that little shove and then you pick the next bigger thing, which builds you a little bit more momentum and then the next thing and that’s what builds you that unstoppable avalanche of momentum.

I thought the avalanche is going up the hill in this case, so maybe I’m mixing metaphors a little too much. And the fact that you’re aware of that frustration that, yes, that means you’re really getting to the gnarly stuff when that frustration builds and that you’re prepping for it before hand so that when you end up there, you don’t stall out.

And I think that that’s very, very forward thinking. And to have that you mentioned scale, and I was curious if you could speak a little to those challenges of, you know, just facing the scale of what you’re trying to do. And I know because you talked a little bit when well before we hopped on of the idea that it’s not just scale in terms of the number of things you have to challenge, but also the fact that there are things that aren’t being done in a way that can be scaled. So I was curious if you could talk a little bit about that, too.

Yeah. So the city of Cleveland is an 8000 employee enterprise and we are legacy cities. So we are an old Midwest, you know, Great Lakes, Industrial City, and we have all the departments of a major city. And so part of our challenge is looking across the breadth of that enterprise.

And the data that you see in public safety is fundamentally different from the data you see at public health, which is fundamentally different from the data you see in building and housing or public utilities. However, if you look at some of those systemic challenges around data, and I’m looking at things like social determinants of health, social determinants of violence, you look at factors that cut the breadth of those data sources.

So social determinants of violence as an example, you’re not just looking at crime data, you’re also looking at weather data, you’re looking at health data, you’re looking at building and zoning codes. You’re looking at how many condemned properties are in that area. You’re looking at what other city services are being provided, and are we equitably clearing the streets and cleaning up trash in each of those neighborhoods?

In a lot of the emerging research in that area, specifically? Been around a little longer for social determinants of health is understanding that breath. And so part of the challenge as I view our department is organizing that data to prepare us to look across that breadth. And so when you realize it’s not ten data sources and ten processes, it’s a hundred data sources and 100 processes.

You breathe deeply sometimes, but you also step and you say, okay, my challenge is not to be the business owner of each of these sources. My challenge is to put in place the structure that allows the business owners of each of these sources to access that data. And look across the spectrum. I would also say that my team is never going to be big enough to be the analyst for every piece of that data.

It is not humanly possible and there are core professional competencies. The crime analysts have different training that the epidemiologists and they should. Those are their professional backgrounds and we have to respect those. But we also have to give them a structure by which they can start feeding in a lot of that data to tell some of those really, really awesome stories and start to manage against some of those systemic structural challenges that we may face or at its core, just manage their day to day work really, really well.

Because for some departments they’re making sure the grass is cut and the trash is picked up and they just need to make sure it’s done consistently and equitably across the city every week. And that’s enough of a challenge that we need to give them stable data to manage them. Yeah, Amen. Absolutely. And I think the what you’re describing with that need to bring in very disparate seeming data, but that actually are intrinsically connected in a way that if we ignore.

Right, we’re really limiting our ability to understand and respond to the truth on the ground. And I think no matter the size of your organization, if you’re involved in social good at all, you’re very likely touching something that is going to be a very complex issue, whether we’re talking about homelessness or food insecurity or climate or any of those things.

They all involve disparate pieces of data. And even just the operation of a tiny nonprofit does, because you’ve got fundraising and data, you have operations data, you have program data, you write, you even internally, even if it’s three of you are going to have different kinds of data that still need to be connected. Absolutely. And oftentimes it’s not worth it to build a mega system that takes on everything.

You know, there’s other things that you’ve got to tackle, you know, that specific things to manage that work within that zone. The other thing I will say, because I know you have users or listeners all over the place, is we many of our listeners might be in cities that already have open data and public facing data. The city of Cleveland does not.

And that wave really came in place ten, 12 years ago in a big way. And our previous city leadership was nervous about taking that on. And so they they just held steady. They were stable and what they were doing and chose not to innovate in that way. And so part of our challenge is jumping on that wave and so starting to release some of the data, starting to get it organized in a way that it can be released.

And we’re at where another city may have been there ten years ago were there today. And one of the things I’ve said to our leadership is that’s okay. Like it is okay. It is the journey of our city. And just as you’re you may be in a nonprofit that’s like me, and we got to start doing this data stuff and the Office next Door has been doing it well for ten years.

We’re in the spot where we’re starting and we have to acknowledge our history and our journey and where we are and the challenges our city has faced and why some of those decisions were made doesn’t mean we don’t need to change. But what it does mean is that we can’t sit here and pity ourselves for not having done that for the last ten years.

And the anxieties about moving in that direction are real. And I’ve heard them from our staff and they get nervous about it. They’re not telling me now, but they are nervous and I respect that their cautions should be listened to. Change is hard. It just is hard and there’s no getting around it now. And I am currently in an enterprise that was really change adverse and change wary and part of the culture shock in the city of Cleveland is we have new leadership who is really not change averse anymore.

They’re coming in and they’re going, Yeah, we had to do this differently and that message and we have an 8000 person enterprise that’s in a little bit of a culture shock right now, or at least they have been. I think we’re kind of coming out of that and it’s settling a little bit in a good way, really starting to operate in that change mode and address a lot of those wicked, systemic, long standing challenges that can plague cities, that plagues so many cities, not the least of which is Cleveland.

And that’s why that vision, that leadership, is really important, that it can’t be so visionary. It leaves everyone behind. It has to be tied to the organization. So that they can they come along with you and that even if it’s uncomfortable that you can do that change. And I love that to just say it’s okay that we haven’t done those things, we don’t need to beat ourselves up over it.

We also don’t use it as an excuse for continuing to not do it. But this is where we are and we will move forward from here using the examples of those who’ve gone before us. And we’re going to take our own journey and we’re going to get there. I would say one of the things that I think we’re doing really well is we are very purposeful as an enterprise, as a leadership team in saying that not invented mentality, not invented here mentality is not us.

So we want every one of your lessons learned. We want to beg, borrow and steal all the stuff you screwed up. We want to know because we want to learn from it. And that’s a really it’s also a hard spot to be in because you’re like, Well, I can figure it out right hand, but it’s going to take you twice as long.

It’s going be four times as expensive as if you just start making phone calls and saying, How do we do this? Just 25% better. So that’s a great point as well. And when you aren’t the first to try to do something, you then have that advantage of saying, well, how did people who’ve gone here already get there? And to not be afraid of picking up the phone and making that call or meeting people?

I actually I’m working with a client where they’ve had a similar experience where they’re like everything we’re doing, it’s brand new. This is this has never been done before. I was like, It’s true. It’s not been done exactly like you’re doing it. But related groups have done similar things and so you could even if you learn only 25% from them and you still have to invent 75, hey, that 25% you don’t have to figure out is getting you 25% ahead of the game.

So let’s see what other lessons we can learn from those around us. And the super cool part about the nonprofit sector in the public sector is there’s no trade secrets, right? We’ll just talk to you about it. Like we’ll tell you how we got there. At least most people will. And that’s awesome. Not every industry will do that.

And that’s really cool in this space specifically. Well, and again, there’s nothing to stop a nonprofit who works in social services from calling the city, who also works as a social services and say like, Hey, how are you guys doing case management or vice versa? So we’re going to train and to look at peer cities, peer regions, peer organizations who look across industry groups and say, Hey, how did you figure this out?

Can I get to your data person? Because it’s great to go CEO or CFO, you know, and connect those leaders that talk data person to data person. I know sometimes in my world, like I don’t want to talk to your most senior executive, I want to talk to your data person. Or when I say like, give me an object model, they go, Oh yeah, of course that somebody else in the enterprise may or may not understand to get ourselves in there is real.

So yeah, that expert frontline expertise totally in need.

Awesome. Well, thank you so much for sharing this story with us. There is so much fun and I really hope that this is just our first check in and we’re going to get to see your journey over the coming years and watch the incredible things that are going to happen out of Cleveland.

If people wanted to connect with you or learn more about what Cleveland is up to, what would be a great place to send them to.

Sure you can check out Cleveland, Ohio dot gov. But I will say today as a disclaimer, we’re working on a new website, so go check it out today and then check it out again in a number of months. And I think you’ll see a very diverse website. That’s definitely part of the digital transformation that’s happening. Yeah, I would start with Cleveland, Ohio. Also on our Instagram and our Twitter are pretty much on point. So highly recommend all part of that millennial transition. Totally. It’s great. Well I love that, as you said, that this transition is not happening in a siloed data transition, a full digital transition.

Having it all happen together is one of the reasons why, you know, you are so set up for success here. Yeah. Thank you so much for your time today.

Liz Crowe joined in the City in August 2022 as the Director, Urban Analytics & Innovation (Urban AI). She is responsible for championing the use of data and modern data analysis, visualizations, and storytelling to provide a much more engaged approach to performance improvement and change management. Liz is an economist and researcher, who is passionate about civic tech, data and the possibilities they hold for Cleveland. An experienced data manager, she has years of experience in economic development, nonprofit management, venture capital, and the private equity industry.

Most recently, Liz served as Principal of Data Governance & Analytics at JumpStart, Inc. Prior to joining JumpStart, Liz worked as a Research Associate at the Urban Institute in Washington, D.C. where she conducted primary research on the role of government programs and private organizations in supporting low-income families in the U.S. Topics included, cash and in-kind assistance programs, the U.S. social safety net, poverty, inequality and social policy. Liz also worked at the Congressional Research Service at the U.S. Library of Congress, where she provided nonpartisan research assistance to members of Congress and their staff.

Liz has a Ph.D. in Public Administration and Policy from the School of Public Affairs at American University, as well as Master of Arts from the Department of Economics at American University.

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Brainstorm a few small wins that would generate momentum for your team or your data efforts. Is there a data point that could help at your next board meeting? Something that would bolster a grant application?

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