The Bottom-Up Revolution
What happens when a data analyst starts studying their hometown's finances? For Karl Urich, it meant seeing Albany, New York, in a whole new light. Karl shares how he makes intimidating financial statements accessible to everyone, why unbiased storytelling matters more than gotcha journalism, and practical tips for aspiring data storytellers.
Transcript (Lightly edited for readability)
Hello and welcome to Bottom-Up Shorts. This is Norm of Strong Towns, and I'm so glad that you're taking this moment to listen to this newest episode. What happens when someone who spent their career working with public data finally turns that lens on their own hometown? For Karl Urich, that happened in Albany, New York. After years in geospatial and analytics work, Karl realized he'd never actually explored his own city's data that closely. So he started AlbanyDataStories.com, using public data to tell stories about housing, crime, taxes, local development, and local government. Then someone said, "Hey, this looks really Strong Townsy." Karl was like, "Well, what is this?" So he looked up what Strong Towns was, and as he looked it up, he realized, "I'm all in." In many ways, as I often will say, Strong Towns was just a vehicle. But Karl was doing this already in many ways, and having these same instincts, these same insights. Strong Towns coming together with Karl was just the natural development of how this would occur. So now he's expanding that work of Albany Data Stories with a new site, HudsonFinance.com, helping communities see where their money's going using the Strong Towns Finance Decoder and what it means for their future. This is Bottom-Up Shorts, stories of people making their places stronger from the bottom up. Welcome, Karl.
Thanks so much for having me here. Really a pleasure to be here with you today, Norm.
Now I've sketched out just to open it up about the data stories and the work that you're doing there to help people feel they have a seat at the table and the insight that they need in order to be able to understand their community. Can you describe that in more detail for us?
Yeah, absolutely. We had a great experience. As you mentioned, we have a website, AlbanyDataStories.com, where we are taking public data and information on the city of Albany, New York's housing and population and demographics and spending. Along the way, we found the Finance Decoder tool. It's probably around April 2025 or so, we wanted to test it out. We were not CPAs. We had never looked at an audited financial statement. We really got a great comfort from kind of all of the trappings around the Finance Decoder tool saying, "Hey, here's a tool that you can use to take public data from audited financial statements, plug it into a Google Sheet and get results and information—great graphs that help you understand the health of your city or village or county." Importantly, having a set of information that you can share with people and really help give people an understanding of the financial health of your city, and if your city is able to meet its financial commitments, I think, is the verbiage that Strong Towns uses. What we wanted to do—we had such a great experience with creating the Strong Towns Finance Decoder for the city of Albany. We also put one together for the county that we live in as well. Our thought was, how do we scale this up? Is there a way that we can encourage and enable anyone anywhere to create a Finance Decoder for their community? We have this idea that it kind of meets the thought that someone sits in a room alone and foils or gathers open data and does analysis on their own and spits out some results. But we also like the idea that data analysis and understanding your community and sharing insight on your community is best done as a team sport. This is the Finance Decoder. It's a new tool. It seems a little daunting, but it's actually pretty easy, and it's made even easier when you have people—a group, a group chat, or people that you can lean on who are doing the same thing or have done it before. So that's kind of the origin story of the Hudson Finance Decoder project.
Yeah, and I love it, because the thing that my son is doing right now is a school project on his name, and because he loves his family, he is diving into some of the details of what our family story is—my side, my wife's side, all of those things. It's that same level of when we love our places, there's actually great value in understanding and even developing an affection for some of the quirks, some of the oddities, but also the present needs that we have. I'm just looking at some of the data stories on property taxes and land development and whether or not speed cameras are going to generate the revenue that they're projected to, kind of poking holes at times, but most of the time, just asking sincere questions to say, "Are we on the right track?" How has that process of doing so and alerting people—this is not a trap, we're not trying to lay out some gotcha, this is us using available info to understand our community better—how is that sort of developing that deeper love of place, but also understanding of some of its real needs?
Yeah, that's a great question, and something I could prattle on for hours about. But if I'll be succinct about it, I'd say we want to create data stories, whether it's analyzing building permit data, whether it's using the Strong Towns Finance Decoder, whether it's some other tool or some other data set. We're not trying, and I don't think Strong Towns wants people to write exposés. We're not trying to play gotcha with elected officials and cast doubt or cast blame. We're really trying to inform. We're trying to inform in sort of this passionately dispassionate way. We're passionate in the sense that we want to provide insight on how our community is doing and passionate about showing reality, giving our community a reality check. We also want to be dispassionate, in the sense that we want to present things in an unbiased fashion. We're not trying to lead the witness. We're trying not to again, write exposés or anything. So even just for Albany Data Stories or the work that we've done with Finance Decoders, we're spending a lot of time thinking, what does it mean to actually be unbiased in the presentation of information? What does it mean that we want to have all parties—elected officials, citizens, the government workers that come in every day and give their heart and blood and sweat and tears to their work—we want people to feel, "Hey, there's a reality check they want to give and to be aware of." Then, once we're aware of what the data says and what our current state is, let's all work together to create a new reality or a path to get us to a better place and not worry about this person's to blame, or that person 10 years ago is to blame.
Do you have an example of a story that emerged from data that maybe was surprising or counterintuitive?
Yeah, I think the city of Albany is an interesting city. It's the state capital, state capital of the state of New York. It's the hub of state government. A lot of the land is owned by the organs of state government and also nonprofits. You'd always hear this story, "Hey, we have this property tax revenue problem, because such a percentage of our land is tax exempt, owned by the state, owned by nonprofits." You'd hear this thrown around like 60%, 64%, 62%. So we thought that number can't be right. So we got our property data, we got our parcel data, we analyzed it, and by gosh, it's 64%. But we went a little level deeper and said, "Hey, it's 64% because X percent belongs to the state, Y percent belongs to nonprofits." Here are some challenges that we have. So we really provided that kind of validation and just dug a little deeper into the issue than sort of these short statements you'd often hear from politicians about our challenges. That was very—all we did was just confirm and dig a little deeper on what people have been expressing. But we'd also have people come up to us, figuratively or literally, and say, "Hey, thanks. I understand what's going on here. I understand what one of our foundational challenges is."
Well, it means that if 36% of the land base is responsible to generate the amount of productivity needed in order to be able to fund all of the core services, as well as any extras that the community might want, that actually puts an onus and an emphasis on the importance of productive land use, which is very in keeping with Strong Towns language on there.
Absolutely, absolutely. Then you can start to think about bringing in other tools, like the Strong Towns tools, and Kansas City has been the exemplar of showing kind of that property tax value per acre, and how often you get very counterintuitive views into what pieces of your city are making and generating the most revenue on a per acre basis. It's really neat. We can start to bring in these tools and give a reality check of where our revenue is being generated. Then we can bring in tools that talk about infill development. I'm really excited to see a lot of the new toolkits that are coming out to support infill and that mid-size housing—very exciting add-ons.
Can you share, just for our audience, for folks that maybe feel intimidated by all of this, what was it in your story and your path that made you the person that's not only publishing data stories, but also now rolling out websites about the Finance Decoder and other tools like that to help your community?
Yeah, so this journey has been interesting and kind of an extension of what my career is. I've spent 30 years in the kind of the data business. I've been in geospatial, GIS, geodemographics, location intelligence, and spent a lot of time building products and services nationally—Canada, US, globally—that help businesses solve location-based business problems. A lot of those data sets that we would use to build products off of came from public sources: Statistics Canada, US Geological Survey, individual county and town parcel and property data. So yes, I had a familiarity with a lot of these data sets, but certainly didn't have a huge familiarity with what it means to FOIL data, what it means to file Public Information Requests. So we just learn things as we go along about how to get data and how to be polite and respectful but also forceful enough to get the data that we need. The neat thing is, we're not—a lot of the analysis that we do, people come up to us at Albany Data Stories. They're like, "You guys, you must do all this data sciencey stuff." It's like, you know what? It's all Excel, Google Sheets. 95% of what we've done is Excel, Google Sheets, a little bit of stuff in open source GIS tools like QGIS. We're not creating machine learning models. We could do that stuff, but some of the most easy stuff to analyze and process and come up with results is just Excel and a pivot table. So very accessible, even the Strong Towns Finance Decoder. Very intimidating. You get 150-page audited financial statement, but people like Michael Duranwood and the larger Strong Towns community have done a great job of kind of creating instructions, creating a great Google Sheet. You just sort of poke around and, "Hey, I'm not an accountant, but you can figure things out. You can figure out, oh, this is what a deferred outflow is. Okay, let me find deferred outflow in a table. Okay, yeah, that looks right. Okay, let me plug that in." So some of this is really just half the battle is just starting and feeling confident enough that—I don't know, I suppose it's kind of like Ratatouille: anyone can cook. It's like Ratatouille: anyone can do a Strong Towns Finance Decoder. I strongly believe that.
Yeah, totally. I mean, I could say if somebody with a philosophy degree is assumed to be kind of useless at this stuff—I've got a philosophy degree, and I've figured out how to use it, and so it's totally doable and it's within reach. I would love to ask also, I mean, you mentioned a couple of great suggestions of just getting started and working with available data. What are some other tips or suggestions that you have for people that want to tell their own data stories?
Yeah, I think there's definitely the analysis part, which is you have to figure out how to get the data. If you're lucky, your city, your province, your county has an open data site. You may need to do a Freedom of Information or Public Information Request. Even that, there are various resources that you can use on the internet to make sure that you're able to kind of unlock that public data. There's an analysis process. Oftentimes it's just, "Hey, pull the data into a spreadsheet and start playing around, sorting it, create a pivot table, something like that." The storytelling, to me, is interesting, because at Albany Data Stories, we tend to think that—and we use the terms supply side and demand side. Sometimes supply side is, "Hey, we have a data set. We have our data supply. Let's figure out what questions we can answer." You just play around till you're like, "Oh my gosh, here's a salient point or two that really is interesting." The demand side is, "Hey, I have a very specific question I want to answer," like how much housing have we built in the city of Albany in the last three years, and what does the trajectory look like? So you start with a question, and then you have to say, "Okay, let me go find the data." So either of those approaches are great and something to think about. Is this a supply side—hey, we start with the data—or do we start with a question? Then lastly, the storytelling itself. Once you've analyzed a data set, you've come to some conclusions, and it can even just be one conclusion. We're not trying to write Shakespeare here. If you come up with one conclusion, one insight from a public data set, awesome. Yay, you. What we do at Albany Data Stories, sometimes I write something down in a Google Doc, I pass it to a couple collaborators, and you just let people pick it apart. Primarily the reason the way we have people pick it apart is kind of three questions that we ask. One is, "Hey, does this actually make sense?" The second is, "Have we really let our biases show through?" The third is, "Words are great, but pictures are awesome." Pictures help really elucidate that story. So what graphs, what charts, what maps? Do they make sense? They have the right colors. Even one or two charts in the middle of a story just really helps make things impactful and understandable. So we definitely like the act of writing the story. We also like the act of field testing it with different people to see, does this make sense, and is it readable, usable?
It's so powerful because it also touches on ways that you're picking up and sharing in Albany Data Stories, just things about the regular run of the mill activities of the city, regular things that are either going to have a measurable impact on improving quality of life or potentially serve as a partial drain on that ability to meet those local needs. You're just laying it out. I know if I spent another 20 minutes, I would understand Albany better. If I spent 40 minutes, I would understand it even more fully. I love that way of putting that together. So as we wrap up, what are the things that, as you look at your community, give you hope?
Yeah, I think the things that give me hope is really the resonance that as we use data to tell stories with a Finance Decoder, whether we've written a story about how much rooftop solar is being added in the city of Albany, or what percentage of the city is tax-exempt land, or writing about infill—the thing that gives me hope is these stories resonate. We take a story, we throw it out on Reddit in r/Albany, and we get good responses. We get people who are—I think there are probably three things that we're looking for. The first is people who say, "Hey, this is great. I see this data. I see this data story. It really gives me data and a deeper appreciation to validate what I already knew." Awesome. The second thing we get is when someone says, "Hey, your data and your data story, whether again, a Finance Decoder or something else, kind of challenged my preexisting opinion. I thought it was A, but it actually is B. I thought we're in a good place, but now we're actually in a bad place, or vice versa." Then the third thing we like is when we can tell a story and someone's like, "You know what? I had never thought at all about that. Now I feel more informed about a subject that didn't even cross my mind." So when we get people who they're not exactly saying those things, but kind of saying things along those themes—if we had any of those three kind of outcomes when we're sharing a story with someone, it's like, "Hey, success, field goal." We accomplished something positive. We see that a lot in the city of Albany.
I love that sense of positive accomplishment, because I think that really resonates with what a lot of us are seeking. We want to feel we have made a contribution. I liken it that you can go on foot and do a walk audit of your community. I definitely encourage people to do that. We feature people that make a regular habit of taking people out for walking tours and doing walking audits, but this is a different type of—not necessarily audit in the financial context, but just taking a closer look, observing and then saying, what is the next smallest thing that we can do to address some of the struggles that we see evident here? So thank you, Karl, for being a guest here today on Bottom-Up Shorts.
Thank you very much for having me, Norm. Really appreciate it.
Really appreciate you joining us as well, and for your time. To everybody that's listening, take note. Definitely go check it out. AlbanyDataStories.com, or Hudson—let me make sure I've got this right here. We've got the new one, HudsonFinanceDecoder.com. So HudsonFinanceDecoder.com will be live when this episode airs. I'm really glad for the time. Take care and take care of your places.
This episode was produced by Strong Towns, a nonprofit movement for building financially resilient communities. If what you heard today matters to you, deepen your connection by becoming a Strong Towns member at strongtowns.org/membership.