Data Beyond The CDO’s Office

Tyler Podcast Episode 76, 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 Corporate Marketing Manager Beth Amann – and other guest hosts – highlights the people, places, and technology making a difference. Give us listen today and subscribe.

Episode Summary

Tyler Technologies’ vice president of data solutions, Saf Rabah, joins host Beth Amann to discuss how data shows up in government outside of the chief data officer’s work. We dive into examples of data being used across a variety of government verticals and identify opportunities for public sector leaders to expand data capabilities in their department.

During the conversation, we discussed how information is simply a manifestation of data and that data’s goal is to reduce barriers to access for the residents whom governments serve. We talk about the democratization of data and, of course, touch on forward-looking trends like artificial intelligence.

Transcript

Saf Rabah: The whole idea is how can we connect the systems that they use every day, ERP systems, CAD systems, RMS systems, court case management systems, how do we connect the data from those systems, put it in their hands, and surface it as domain specific insights so that they can see at a glance what their program performance is.

Beth Amann: From Tyler Technologies, it's the Tyler Tech Podcast, where we talk about issues facing communities today and highlight the people, places, and technology making a difference. I'm Beth Amann, the corporate marketing manager here at Tyler, and I appreciate you joining me for another episode of the Tyler Tech podcast. Today, we're joined by Saf Rabah, Tyler's vice president of data solutions, to discuss how data shows up in the government outside of the chief data officer's work. We'll dive into some examples of data being used across a variety of government verticals and identify opportunities for you, our public sector leaders, to expand data capabilities in your department. Saf, welcome to the podcast.

Saf Rabah: Thank you, Beth. Thank you for having me on the podcast. I'm so excited you're here.

Beth Amann: Our listeners will know that we were lucky enough to get to speak with Franklin Williams, the president of the Data & Insights Division, with whom you have worked closely for many years recently. And now we are doubly lucky to get to speak with you about data applications across a variety of industries in our government. For our audience's context, your role at Tyler is really about seeing opportunities for data-driven decisions outside of just what you think of as the chief data officer's work. And then equipping those teams accordingly with the right questions to ask and the tools to enable those solutions. So, I wonder if just to start, you can talk about where do you see data management, analysis, and sharing showing up outside of what we might traditionally think as the chief data officer's role or a data officer's specific work?

Saf Rabah: Yeah. Absolutely. So our group works with department leaders in various functions that cover pretty much the entire swath of government to help them use their data more effectively. So, yes, there are differences between these data-driven managers and people who lead with data to run a department and the CTO, but there's also commonality. So I'll start with that. The most basic need that all government employees have is still very much about basic access to the data, right? The same function that the CTO team would be focused on. It's that access to data is the ability to share it, it's the ability to protect it in case of sensitive data. So there are intersections between what you might traditionally think of as a CTO's role and all these business leaders across government functions. But there's also distinct differences. And the differences have everything to do with providing the shortest path from the data to the insights that these leaders need in order to manage the programs that they are leading. So what does that mean? It means the ability to translate data into domain-specific insights. Data needs to speak their language. Data needs to be presented in a way that is not technical as data, right? Data can be overwhelming. So the whole idea is how can we connect the systems that they use every day, ERP systems, CAD systems, RMS systems, court case management systems, how do we connect the data from those systems, put it in their hands, and surface it as domain-specific insights so that they can see at a glance what their program performance is. So, for example, think of a dashboard with metrics, right? Those metrics cannot be random. They have to be very specific to the needs of, for example, an assessor. They want to see property values. They want to see sales activity. They want to see differences between assessed values and market values for properties. So the whole idea that when it comes to these department-level leaders is to translate that data into automated flow and surface it as very actionable, very specific insights that they can then drill into and use to manage their program.

Beth Amann: I think that point about domain-specific is so important. It could be really easy just to say, oh, well, we are collecting data, we're going to measure the time it takes to complete a task. And that's going to be standard across every industry, and that's just the data we're going to collect. But what if you're missing how many resources you have to do that task or if you aren't measuring how many people have access to that information, then you miss the bigger part of the picture. I think it's a really cool point to say, like, there isn't one-fits-all data analysis that can be applied when you think of, oh, I'm going to work with a data scientist or someone who knows data very specifically you need to make sure that it makes sense to what you're trying to manage.

Saf Rabah: Absolutely.

Beth Amann: When we were talking with Franklin, we spoke about the kind of evolution of data management and analysis in government, and we talked about going from getting access to the data at all out of spreadsheets, to being able to perform predictive analysis. And we talked a lot about this kind of difference between open data and internal data sharing and you and I were having a brief conversation before we pushed the record button about how maybe let's not say internal data sharing because that limits the scope of what's possible. Instead, it's internal applications of data. So I'm wondering if you can clarify for the audience because it was helpful for me, the difference between what was maybe really popular with open data and now different internal applications of data and the kinds of purposes all of those serve.

Saf Rabah: Yeah. So let's start with open data, and I think you and Franklin had a great conversation about that from the CTO's perspective. Right? When we think about open data, we think about the why of open data. The most fundamental driver of open data is still very much about transparency. It's a basic expectation, and constituents, residents, citizens, everyone that is impacted by the city where they live, the county where they work, the state that they live in, have this basic need for transparency, and whether it's driven by policy or by law or simply good governance, transparency, and open data, opening up the data that government has and putting it in the public domain in a usable, discoverable, accessible way. It's still very much part of good governance and basic expectations. But it's also about using this data to provide information to folks that need it. Right? So think of, you know, we think about it as a tool, an asset for engaging constituents, and helping them understand what their city is doing. Right? What is the budget for schools this year? What do investments in transportation look like? How is that going to impact their lives? So that's still very much the main driver of open data. And in some more advanced programs, there is this realization that data itself has economic value, right, meaning people can build applications, can build services, can connect the dots between all of this open data to create something new. Right? So we'll just use examples. For example, a lot of our customers, cities like Chicago, New York, San Francisco, put out permitting data out in the public, and that data updates super frequently. So that data is useful to many people among them contractors. So if I'm a roofing contractor, and I see that there is an increase in permits for a new roof in my neighborhood, then that's a lead for me. Right? So you can think of this data as also as fuel for economic activity and for innovation that translates it into something useful. Now your question was not so much about open data. It was more about these internal applications of data. And when it comes to these internal applications of data, we tend to think of them along, if you will, the customer journey or a maturity curve of some sort. So first of all, who is impacted by these internal applications of data? It is those data-driven managers, those who are leading programs or leading entire departments. And as we talked about earlier, the most basic need that they have is to have that operational visibility, right, call it situational awareness, which basically answers the question what's happening or what happened. So if I run a permitting department, things like, well, what is our backlog of permits that are sitting in a preapproval stage that require inspection? Very, very basic. It goes to their workflow. So you know, this data is not a curiosity. It triggers events and insights that help them direct resources and do the work that their department does. So, the most basic step or kind of like, you know, what we think of as fundamental data maturity is to turn the data from the various systems that they have into an operational picture that they can understand, relate to, interrogate, and then act on so that they're acting from a place of foresight and understanding of what's going on. The second kind of evolution of that customer journey has everything to do with understanding the why. So, for example, if I'm a court administrator, and I see a surge in court cases that are yet to be assigned to judges and the various resources that are involved. Now I see it. Great. Now I know. I can start the process of asking why. What's going on? So for example, that during the pandemic, there was a lot of cases that were driven by, you know, economic hardship. Think evictions, think people who are struggling with debt. And the idea there is to help the folks who run these programs who want to make, in this case, justice more accessible to people, to see what's happening and then drill down to understand where it's happening, when it's happening, and what's driving it. So, we call it diagnostic analytics. Right? It's being able to understand the why of things and really pinpoint, you know, specific problems or specific trends that they can then act on. In the third phase, when it comes to these internal applications of data, we think about something more proactive. So, if we can see what's happening, if we can understand why it's happening, can we aspire to predicting what's going to happen, right, forecasting things? And if we think about forecasting, maybe the most basic example is to forecast revenue so that when it comes time to budget season, we have a clear picture of what our revenues will be for a city, for a county, for a state, and be able to make good decisions about budget allocations based on that insight. So that's how we see these internal applications of data. It's very much about connecting these systems that any one department has, producing the insights that they need to run their business, and then kind of taking them on a journey from seeing what's happening, understanding why it's happening to being a little more proactive and predicting, forecasting, and, you know, things like even assessing risk, which I hope we can talk about later. So, there are many more advanced applications that have to do with data supporting these internal workflows, the internal mission of that department.

Beth Amann: I love that it's getting to the idea of why and not just what. The what is certainly very helpful. The example you provided with permits and potentially contractors looking and seeing, okay, there's a ton of permits being offered in my neighborhood, and I can go get some good business. Like, that's an application I hadn't thought of at all, that that is a really great way to support your community, which would then increase potential tax revenue that you as the city would get. And it then goes back to this idea of what you said that operations and outcomes are being interrogated more; they're being investigated to understand why we got to that point to begin with and using it for future understanding of what the community that you're serving is going to have access to in terms of services or revenue or other things like that that you maybe weren't able to project out as far in advance before. You provided a couple of examples there, and that all sounds very fancy and wonderful. And there are perhaps some government folks who are listening to this and thinking, okay, I do not have those resources. I have a very small team, or I do not have the budget to devote to a data scientist on staff, and there's a lot of work that goes into building a robust data program. But one of the cool things about working here is that I've gotten to see success stories from teams that have a few people to those that have staff of seventy plus data scientists and from towns all the way up to states to the federal government. So can you speak a little bit about how governments can utilize data in their organization regardless of their maturity level, the resources they have, the number of personnel they have on staff? And maybe if you feel so inclined to share a specific example that comes to mind.

Saf Rabah: Sure. Happy to, Beth. I think this is a super important question. And, you know, when you said the word fancy, fancy applications of data, it really gets me thinking. The whole idea here is to lower the bar for using data on a day-to-day basis. Right? So let's look at the landscape of clients that we serve. Not anyone working in government service. I may be off with my stats here, but there are twenty thousand incorporated cities, towns, and townships in the US. There's more. Fifteen thousand of them have a population of under five thousand people.

Beth Amann: Whoa. That is unexpected.

Saf Rabah: Yeah. So, the landscape... We work a lot with big cities, like New York, Chicago, and these are, you know, big — it's a metropolis. With all the sophistication and the complexity that comes with running a large city. But for thousands of clients — the reality is once you get, you know, if you were to rank cities from largest by population and budget, right? And you get to, like, city number one hundred and then down one hundred and one and so forth — you're dealing with cities that have about two hundred thousand in population, right, and below. And about two hundred million dollars in budget and below. That's kind of like where, you know, the scale of what happens once you get past the largest, the hundred largest cities in the US. The same picture applies for counties, states. So, the question is, well, what can you do when it comes to data if you are a city with a limited budget? And limited budget really means limited resources. What kind of resources are we talking about? Certainly, IT resources that would traditionally be the ones who work on these data initiatives to create the types of innovations and, you know, the things we just talked about, right? But it's also these cities, these counties don't have enough inspectors, don't have enough firefighters, don't have enough assessors, don't have enough — you know, staff to perform the very core functions that they need to perform. Meanwhile, we all know that expectations are high. And even if you're a small city, you still have to do all the things that large cities do. So the question of resources, and what is the role of data in a world where resources are constrained, becomes really, really important if these innovations have a chance of being mainstream, of being accessible to the broadest swath of jurisdictions out there. So I go back to this key point that at least my team focuses on, which is how can we lower the bar? Right? How can we lower the bar so that you don't need the team of data scientists on staff? You don't need a lot of complex data analysis and infrastructure that would be even hard to stand up and manage, never mind use. So here, Beth, we think about practical approaches that will provide these insights to these clients in a way that doesn't require them to have this high overhead. And directing the insights and the product of these internal applications to helping them solve the core problem of “we don't have enough assessors. We don't have enough inspectors. We don't have enough firefighters.” So let me illustrate this with an example. We’re working with the state of Kansas, specifically the Department of Revenue in the state of Kansas. And it's a really big, ambitious program that we are supporting, and it has to do with collecting data from assessor's offices who are located in the hundred and five counties in the state. So just for our audience, if you're not familiar with this process, every county has a county assessor. They have assessors or appraisers working in their departments. And the whole idea is to provide accurate, equitable assessments of properties in that county. Now the process of assessing and assigning valuations to these properties is governed by state laws. There are laws in place or statutes that govern how this really important process should happen and provides guidelines for how to do it in a consistent way across the state, for example. So the Department of Revenue, one of their core functions, is to make sure that property assessments across the state are happening in a consistent way and in a way that complies with state rules. How does that happen? Well, it happens by collecting data from the assessors in each county. And in the state of Kansas, just like in every other state, there are large counties. There are small counties. Small rural counties with a low population and also limited resources. So, this process used to be super manual. Right? Uploading a spreadsheet, consolidating spreadsheets into some sort of database, and then standing up a layer of analytics that does all the heavy lifting of analyzing property values and making sure that, you know, they meet these criteria. So, the project that we are that's currently in flight with the state of Kansas DOR and all county assessors in the state is to automate this whole thing. Why send a spreadsheet when we can automate the flow of data from each county assessor system right into DOR, consolidate that data into one place, and make sure that the data is constantly flowing as opposed to, you know, every year, we have to do a whole manual process to collect it. Then the scope of the analytics that are performed on the data are well-known, well-described, well-documented, in fact. And they are very common in the world of assessment. So we're standing up an entire layer of domain-specific — back to that domain specificity — domain-specific analytics for those assessors. And that takes the bite out of the process that an IT organization would go through to not only wrangle all of this data but perform these analytics. And then these analytics are available to all the counties. And what does that mean? That means that if you're a small county that doesn't have all the resources that your neighboring county has, you still have access to the same insights. You still have that operational visibility that we talked about. And even for processes that were super manual. So, for example, an appraiser, when they're looking at a property that is a bit unique or a commercial property, they often need to find comparables to that property so that they can check their own assessed values for that property. Well, how does that happen? Well, it happens by calling colleagues in neighboring counties and saying, “Hey, I've got this warehouse, or I've got this lakeside property, and I don't have many of these properties in my county. Perhaps you do, and it would be nice to understand how the valuation process is working across the state so that when we come up with an assessment, it's defensible, it's accurate, it's informed by data.” Now that process, instead of happening by phone and by collecting data, you know, through relationships, which is great. We can automate this so that appraisers in these counties that don't have a lot of properties still have access to a statewide database of properties from which they can search, derive comparables, and do the work that they need to do in a faster, much more efficient way. That's a good example, at least in my view, of how do we bring these innovations — in this case, to an entire state — and allow all counties in addition to the state Department of Revenue to participate in this ecosystem of data and insights and analytics in a way that is not cost-prohibitive for those jurisdictions that just don't have the resources that we talked about.

Beth Amann: We'll be right back to our conversation.

Jade Champion: I hope you're enjoying listening to this episode of the Tyler Tech Podcast. I'm Jade Champion, and I'm here with Dani McArthur to see what's happening this week across government associations. This week, we're talking about NASACT, the National Association of State Auditors, Comptrollers, and Treasurers. Dani, you just wrapped up the NASACT annual conference in Portland. So how was it, and what training topics did you notice at the conference?

Dani McArthur: Hey, Jade. NASACT was great. Sessions and topics were geared towards effective and efficient financial management practices and how data can drive informed decision-making. Tyler Technologies has been an affiliate partner with NASACT for more than six years, and we were able to present alongside the state of New Jersey to showcase their work with open data. The presenters talked about how organizations can connect data across departments and leverage that intel to inform decision-makers and support residents' everyday engagements. We explored New Jersey's open data portal, which has become one of the nation's premier state-level portals that continues to establish standards and best practices for data.

Jade Champion: It sounds like the state of New Jersey serves as a prime example of leveraging data for operational use and public access.

Dani McArthur: They do. Through collaborative efforts and the use of compelling visual dashboards, New Jersey has been able to enhance transparency and accountability with constituents on efforts such as COVID-19, Hurricane Sandy, small business grant support, ground-level neighborhood programs, and much more.

Jade Champion: That's so awesome. Thank you so much for the update, Dani. I'm going to link a few stories about how Tyler can help enhance the digital infrastructure for your state and New Jersey's data-driven financial practices in the show notes. Now let's get back to the Tyler Tech podcast.

Beth Amann: There are so many good things out of what you just said. And the concept of, like, we want to lower the bar so that it isn't this idea of “we don't have enough resources to have a data program, we can't commit budget to that.” Instead, this would increase efficiencies that would allow governments to do more important work than just paper-pushing, collecting data, things that have to get done, but don't necessarily move the needle. It reminds me, we used to use this phrase a lot in the Socrata days, which you were a part of as well, about democratizing data. And this is a different way of doing it. We talked about that in the times with Socrata and open data being the main focus, it would be about democratizing data for the residents or the citizens, but this example of a state agency supporting the counties, it offers a different level of access for counties or jurisdictions that might have smaller budgets. It allows them to operate at a high level that they're capable of. They just might not be resourced for it.

Saf Rabah: It's democratizing analytics; that would be a good way to describe it.

Beth Amann: Yeah. I think it's great. And I do love that part about government that you can share so many of your resources that you can see, okay, the state of Kansas did that. So maybe we can now do that in the state of Montana or the commonwealth of Virginia. There are options to take that and have that same experience for our government workers and for our residents transferred from state to state and location to location. So I want to kind of switch us to talking about residents because that conversation we just had was very much about “I am a government worker and I am being equipped by my state government or a neighboring jurisdiction to understand comparables, to understand more about the data landscape.” But residents are probably also expecting a lot of their government, especially when it comes to data management and sharing for many reasons. The increase of technology accessibility has really made everyone think, “oh, I can just type in the same thing and get exactly what I want, or I can click a button and my Uber is here.” And therefore, government should do the same thing. So, what are residents, like, truly expecting of their governments when it comes to data management and sharing?

Saf Rabah: Well, we're all residents wherever we live. So we can relate to this, right, at a personal level. You know, our observations are that residents expect a lot from their government, and they understand the value of data and information. And, you know, we talked about transparency, right, basic expectation. So, to be able to operate in the open, understand what's happening, understand where crimes are happening, what trends exist, how money is being spent, what projects are currently in flight — just very, very basic expectations. But the part that we didn't talk about has to do with, I'm gonna call it convenience. Maybe there's a better word, but convenience, at least, begins to describe what I believe is an important aspect of data and how it intersects with the daily lives of residents. And, you know, what do we mean by convenience? So Beth, let's say I own a small business in my city, right? As a small business owner, there's a whole bunch of things that I need to do all the time. Right? I need to maintain a permit, I need to maintain a license, I need to pay taxes, I need to have — if I'm a restaurant, for example — I need to have health inspections from the county, I need to pay my bills, I need to do all of those things. And a lot of those things are direct government services that are provided by the city, the county, and the state. Right? So, I have to deal with all three levels of government, if not the federal government, for example, I might have a small business loan, right, from the Small Business Administration. So, you know, if you think about it from the perspective of that small business owner, not only do I have to do these things, but I need information about these things. So, you know, what do I mean by convenience? I mean, today, that information — which is information? It's the manifestation of data. Right? — that is in a whole bunch of websites. If I want to check my permit, I need to go to the specific permitting department. If I need to pay my bills, I go to the utilities website. Right? So, there is this expectation out there, and it's really, you know, we can call it an aspiration because it's actually really hard to do. Can we provide an experience where information, transactions, interactions with government are more centric to the persona of a small business owner, a homeowner? Can we bring this information together? Can we make it more convenient to do business, right, to own a home, to send kids to school? So, the idea is can we take all this data that's sitting in all these silos and not only connect it all together, but translate it into consumable information that everyday folks can relate to, interact with, make sense of. And can we do that in a modern experience? And the reason why a modern experience matters is that, you know, we live our digital lives. So, and I say this recognizing that too high a portion of the population don't have access to digital lives. So that's an entirely different set of problems, which is, yes, there might be information on the website, but I can't access that website. The digital divide is still very real, and there are populations that are at a huge disadvantage there. But if we focus on those that are lucky enough and fortunate enough to be able to live their lives in a digitally savvy way, there is an expectation that builds up every day that you know, it should be as easy to do business with my city as it is to do business with my bank, or booking an airline flight, or you know, those types of things. So, the experience, the access to information, the ability to design citizen-resident-business-centric experiences that allow them to get the information they need, transact, interact with government — that's kind of the big picture where it's not just data. Data is a big component, but it's all kinds of things. And they come together to, you know, give hope to this aspiration that we all have of modernizing that experience. And it's important to note, then maybe as a final piece to this, Beth, that, you know, these things take time. Right? If you think about the arc of evolution of government, at least when it comes to technology — at least the parts that I remember — right? It started with digitizing paper records, digitizing paper-based processes. You had to go to a counter and fill out a form. Now you can do that online. You know, the internet, mobile. You know, we've seen a lot of investments in IoT, Internet of Things, sensors for parking, for traffic violations. So, there is this evolution, and there's always pressure and a challenge to keep pace with this evolution. And now with AI, there's a whole new world of expectations, hopes, dreams, fears, right, all of those things. And, you know, if you think about over the last twenty years, government has done a really nice job of connecting what they do to the current state of the art, and it just takes time. It may lag by a few years, some things are harder than others, but that's kind of the general shape of what I believe the citizen experience, the resident experience, is evolving towards and the role of data in facilitating this kind of modern resident-centric experience that we just talked about.

Beth Amann: There's a realistic grounding aspect you just shared with the idea that the digital divide is definitely more prevalent than I think those of us who maybe live in big cities realize. But along with that comes this hope that there is an evolution. There is an opportunity to continue expanding and improving. And when you think about how far government has come in the last fifteen years, to now, there's a huge expansion of services offered and dots connected and improved offerings from government. That's only going to continue with the rapid spread of technology access across all of our communities. And especially, like you mentioned earlier, those fifteen thousand counties that have many fewer residents than I was expecting, I find it exciting that there's still improvement and opportunity. And so I want us to leave off with a look towards the future and another exciting thing. And you mentioned AI already, so I have a feeling this will be a part of your answer. But Saf, what's the most exciting thing you're seeing in government's use of data?

Saf Rabah: There are a lot of exciting things, and we're definitely going to talk about AI because how can we talk about exciting things and not mention AI? But before we get to AI, Beth, I want to highlight something that is, you know, perhaps invisible to the outside world but is very real. And for me, it's the most exciting thing. It's the thing that gives me, you know, motivation and hope that we are very much on the right track as a community, Tyler, other vendors, civic innovators, and our government clients. Right? And that is the growing realization that data holds a ton of promise to help government perform better, meet the expectations of its constituents, run operations more efficiently, and, you know, take steps towards that future that we talked about. I think of, you know, ten years ago, data was a curiosity. Right? Perhaps if we think about innovators, early adopters of technology, were very much at the forefront of all of these ideas that came from, hey, we've got data. What are we going to do with the open data program started fifteen years ago. They produced a ton of innovation. It changed kind of perceptions about data. But what excites me the most is that excitement about data is now pervasive mainstream and, like, you know, when we go to Tyler Connect every year, we have thousands of our clients there. And they're all talking about data in real-world terms. They're saying, “I've got this data. I want to do x. I want to be able to understand, you know, revenue as a result of my permitting activity.” Right? It's no longer this abstract, this kind of conceptual thing. It is very real. It is very practical. And folks out there are seeking ways to use this resource that, when we talked about resources earlier, it's pretty much the only resource that government has in abundance is data. So that's, you know, by far what I'm most excited about because it just opens the field of opportunity for our clients to take stock of this asset that they have. And think and implement and experiment with practical ways to use the data every day to run their business. So that's, you know, if we end there, I think you'll be a success. But we should talk about AI just a little bit because, you know, there's a lot of hype. We can look at AI. I mean, everywhere you go, you see articles about AI on social media. It's, you know, AI, this AI that — massive developments. So, there is definitely hype. There is, you know, kind of the shiny new object syndrome happening, but there's also a lot of substance behind it. Right? And we've seen examples that really ought to give us an opportunity to think and perhaps rethink how we do things. And I'm going to stay away from all the buzzwords, Beth, and all the hype, and really focus on the kind of core goal of helping government leaders. These data-driven managers use data more effectively to improve the operational profile of their program. So run them more efficiently and also improve the outcomes that are provided to the various constituents that these programs serve. So, when I think about AI, I think about data because without data, there's no AI, it's not just the generative AI that has filled the room with excitement and noise and fear and all of that stuff, you know, ChatGPT being a prime example. It's actually more simple than generative AI. I'll give you some examples, but we talked about resources earlier, the lack of resources. So, for example, inspections. If I'm a county, I have to literally inspect all the restaurants in the county to make sure that the food they serve is safe for my resident. Very, very basic. The reality is the number of inspectors is far too small to inspect all the restaurants that need to be inspected on a frequent enough cycle. So, what's the answer? Right? What is the answer? The answer lies in the data and an application of data that you could call AI because what's the point of inspecting all restaurants equally and never being able to do that because of a lack of resources? When methods, technology exist to take these restaurants, build a risk-adjusted profile. If we take data such as 311 data, we look at prior inspection data, we look at infection data, we look at certain variables that exist in all of these systems, bring them together, build a risk model that would literally stack rank restaurants from the highest risk of having health-type violations to the lowest risk possible. Will it be foolproof? No. But what it will do is basically produce a smaller number of restaurants that need to be inspected on a priority basis. And then we can balance the need to inspect with the resources that are available for inspection. And to me, this is an example of, you know, what I'm most excited about when it comes to all of this AI talk, which is how can we apply advanced analytics? And how can we again lower the bar for using these advanced analytics to plug this intelligence into every government process and every government program to make it more efficient — back to balancing the need to inspect with the number of inspectors available, but also to produce better outcomes, which, in this case, is safer restaurants and less likelihood of foodborne disease spreading in the community. And that example, Beth, applies everywhere. If you think about every government function, whether it's assessing properties, whether it's doing fire inspections on buildings. All those things can benefit from this type of thinking of applying domain-specific analytics, data science, AI. I don't really care what we call it, but it's advanced applications of data, to achieve that need for more efficiency, more throughput, and better outcomes for constituents. So that is I think the promise of AI, and it's up to all of us to figure out how to make that promise real, accessible, safe. And, that's what we're working on. So, hopefully, we can get together a few podcasts from now and do a little update on all of this work.

Beth Amann: I think we certainly can. And I'm going to be very honest, Saf, when you started sharing that example about AI and restaurant inspections, I started going very different places in my brain of how AI could be used in restaurant inspections, and it did involve holograms. But it goes back to the point you made earlier about the data's goal is to lower the bar of access. It is to increase efficiencies and find different ways to prioritize and having an AI-informed, risk-adjusted model would help you think, okay, I probably don't need to worry about Joe's Cafe, but I might need to be worried about Bob's Cafe. Like, there could be different health violations that have happened in the past or outside circumstances that we're not aware of. And I think you really hit the point in this conversation that there are so many different ways that data shows up in our government services and can drive efficiencies than we're thinking about. It is not just the chief data officer. It is every government official's job and opportunity to harness the data that they have and really put it at the heart of their decision-making. So, Saf, I want to thank you so much for joining us today. I really enjoyed talking with you about all of this.

Saf Rabah: Yeah, so did I. I thank you so much for having me. Can't wait to do this again, Beth. Thank you.

Beth Amann: There's a unique opportunity for governments to harness the data that they have, explore its applications, and use it to make better decisions for both the internal teams of government and the constituents they serve, regardless of your vertical or area of expertise or size of government or size of budget. I hope you found today's conversation enjoyable. For Tyler Technologies, I'm Beth Amann. Thanks for joining the Tyler Tech podcast. We're looking to learn more about you and what you want to hear more of on the Tyler Tech podcast. Fill out our audience survey in the show notes today to let us know how you heard of the show and what you want more of. And don't forget to rate and review the show wherever you listen to your podcasts.