 My name is Rachel Perry and I am the founder and owner of Strategic Data Analytics. I've been performing data analysis work for close to 20 years and seven of which are the most recent seven have been for legal aid organizations. I created the content for and will therefore be introducing you to the data analysis framework along with Scott Friday, who is our web designer. Just a little bit of background, the data analysis framework was an idea generated during a TIG project a couple years ago that was a partnership between the Legal Aid Society of Cleveland, Montana Legal Services Association, Strategic Data Analytics, the Northeast Ohio Data Collaborative, and Cleveland State University. During that project we worked on significant analyses of vulnerable populations in the greater Cleveland area and in the state of Montana for the two legal aids that serve those areas. Our analyses revealed patterns, some of which were expected and others which piqued interest, raised questions, and generated discussion regarding what the data revealed and how that new information could be used to improve client service effectiveness and efficiency. The analyses were fruitful for those two legal aids but we wanted to make data analysis more accessible to a broader audience. So we developed an outline of the data analysis framework. Then starting last year under a new TIG project, that's a partnership between NJP Strategic Data Analytics and Scott Friday Designs. We have actually created the data analysis framework site and all of its contents. Scott's going to talk to us a little bit about some of the technical details and then we'll get back to the content of the site. Thanks Rachel. So hi, my name is Scott Friday. I'm the developer on this project and I wanted to just quickly go over some technical details about the site. So for the back end we are using Drupal 8 and we're utilizing the new built-in REST API features that are available. And this is basically a way for external sources to interact and grab content from your Drupal site. So for example with this kind of setup you have the freedom to build the front end of your site with pretty much anything you'd like. As long as it can interact with a REST API which pretty much everything can. So for our front end we are using HTML5 with the AngularJS JavaScript framework. And with this kind of setup it's a little bit different than the usual Drupal site, but I think it has a lot of benefits. And if you are thinking of upgrading or moving to Drupal 8 soon, I would definitely recommend looking into the REST API features. So Drupal.org has some great documentation that can get you started, but I have some tips and advice that can save you probably a lot of time and headaches. So feel free to get in touch and I'd be happy to share those. And thanks and back over to Rachel. Alright, thanks Scott. So I'm going to go ahead and take us to the site. Alright, so this is the home page. And for anyone who wants to follow along actually on the site, I hope you can see the site up there. But I'll read it out. It's HTTPS forward slash forward slash daf.lsntap.org. And that will get you to the home page here. Alright, so just sort of some high level stuff about what the data analysis framework is. So it provides analysis instructions to help organizations answer five high level questions and or 118 more detailed data sub questions about eligible people, their legal needs and services provided. So hopefully when we when you go to the start site and start looking through, you'll see data questions that other legal aids have posed that might be similar to questions that you have. And it'll help walk you through how to gather data and then do the analysis to answer those questions. It's simple to use so organizations that are just beginning to look at their data will find it helpful. But it also offers a level of detail that will make it helpful to more data savvy organizations that are more interested in more sophisticated analysis. The instructions include guidance on what internal and or external data are required in order to be able to answer the data questions and undertake the analysis. And every example shown uses actual legal aid data. So scrolling down still here on the home page, there's a section called things to think about. And there are, I think, five sort of things that we think are important enough that we highlighted them here on the home page. And those are watching for data patterns, running every data, finding by staff, how to deal with difficult data, data integrity, and factors that can skew your data. So in each one of these categories, we have examples that are actual examples from legal aid organizations. So we'll just look at one to give you a sense of what we're talking about, the kinds of examples that might be helpful to you. So this is a dealing with difficult data section. And there's this first example here refers to uncollected data. So in this particular example, it was data about formerly incarcerated persons. Legal aid was curious about those individuals, but for policy reasons had decided not to ask clients about their previous incarceration. So that legal aid had to rely on external data sources with information about the reentry population. So that's just one of the types of examples that you can find in here. And I would encourage anyone who's starting out on data analysis project to scroll through these to read them to see if they provide any meaningful guidance for you as you get started. All right. So when you're ready to start actually beginning using the framework, you can get to the framework in a couple of different ways. But we're going to go ahead and start by clicking on this Begin Exploring button. And this takes you to sort of the heart of the site. It describes the four main sections, the first being the data questions up here at the top, the next being the analysis types. And there are data resources and partners. And then finally the data analysis framework itself. We'll quickly go through each of the sections so you know what's in there. Let me scroll back up. All right. So at the top are links to the five main data questions, which will take you to then those more detailed sub-questions and then ultimately to the analyses. So the questions are who is eligible. This refers to the poverty population in your service area. And generally you'll be looking at external data to get information about who is eligible. The next is who requests assistance. This generally refers to the folks who come to you requesting assistance. So intakes is sort of our main source of data there, internal intake information. Then who do we help? That refers to the clients who you are actually able to serve. How do we help? Refers to the level of service you were able to provide. So did you provide brief service or were you able to give more extended service? And then what resources are required? Generally there are of course lots of resources that are required in the work that you all do. But generally the data that we're looking at here has to do with the amount of hours that are worked on particular types of cases and for particular types of clients. So just to get you a sense of where these buttons take you, I'm just going to click on the who is eligible question. And you'll see then that there is a list of sub-questions that we've come up with again with input from various legal aid organizations. I'm not going to go through these in detail because we'll look back at them again shortly. So I'm just going to click back on the data analysis framework and get us back to sort of our home base here. Then the next section goes through the types of analyses, the examples of which are included in the site. So for example, a snapshot measures counts or percentages for a given period, usually the most recently completed year. If any of the counts or percentages are unexpected, a comparison, trend, or spatial analysis might be necessary to better understand the reasons for those unexpected results. And the other types of analyses that we are incorporating here are comparisons, trends, geographic distribution, and geographic concentration. And I won't go through all of them in detail. I think in general most folks probably understand that a comparison is going to be comparing to or more variables. Trend is going to be looking at changes over time. And then just to clarify the difference between the geographic distribution and the geographic concentration, I will read over those. So the geographic distribution analyses show how people or problems or anything else of interest is distributed across service areas, which can be divided into smaller areas to reveal spatial patterns. So we're looking at sort of where do people with particular legal issues live? How are they distributed across our region? Then the geographic concentration compares the concentrations of multiple variables to determine how the variables and location impact each other. So in other words, if in a particular geographic area the poverty population is concentrated in one area, we would probably expect that most clients would come from that area. So then if you look at your data and see that the majority of clients don't come from that area, that will reveal a difference in those concentrations that might be worth investigating in more detail. And then scrolling down just a bit more to talk to you about some of the other resources that are here on this home base page, there's information about data resources. So we'll just click there. And there are two links here. One is related to internal data, and it provides a list of sort of the basic case data fields and client data fields that you might want to start pretty much any analyses with. And then if we look at the external data sources, there's a lot of information here, and it's a lot of detail, and that's purposeful. Because we know sometimes it can be intimidating to figure out where to go when you're looking at a site like the US Census American Community Survey. And so there's information, well, there's a link here to the site that will get you the data, recommendations about which tables in terms of the five-year, three-year, or one-year estimates. And then if you scroll down, there are vulnerable population examples and recommended tables from the ACS. So again, it can be intimidating to figure out which tables to look at. And so here's a list based on various populations. And then there's also in each section a link to a site within the Census that talks about that particular population and various issues that they've identified in the broader national data. So hopefully that helps with figuring out where to go in that site. And then there are, down here, these links I think are worth highlighting. There are some other entities that already aggregate and analyze data, and it might be worth looking at some of the national examples. And then also some state and local examples. We have just a few examples listed here, but there probably are similar sites around wherever you all are stationed. So let's go back. And the final, oh, sorry, the next resource is partnerships. And this is a table that was actually put together for us by a social scientist that worked with us on the original project. And so he put together a list of 628 potential university social science research partners from around the country, and it's sorted by state. It'll probably be a little bit difficult to view here, but you can download it and it's sorted by state. So you can scroll down and find a potential social service partner in your area. Okay, so finally let's scroll down to the actual framework itself. This is the visual depiction of the structure of the tool, and it can be a point of entry. You can click, if you're interested in a snapshot of who is eligible, you can click here and be taken right to the instructions and the analysis. So the way that it's set up is that the types of analyses are listed in the colorful boxes on the left, the snapshot, comparison, trend, geographic distribution, and geographic concentration. And then the high-level questions are listed across the top, who is eligible, who requests assistance, who do we help, how do we help, and what resources are required. So again, you can click on any example, and if you don't know exactly which type of analysis would be most effective, I would urge you to click on multiple examples to see if they shed some light on which analysis. This might be relevant for the questions that you have. So we're going to actually, though, scroll back up to the top and enter via these questions. My assumption is that once you've used the framework a few times, you would probably go straight down to this colorful matrix. But I think when you're first starting using this tool, it's helpful to start up here with the questions. All right, so let's start with who is eligible, and we'll start looking at some examples. So we saw this a few minutes ago when I clicked over here to show the sub-questions, and I will just highlight a few of them. So some of the sub-questions that you might have, how many people are eligible? What are the demographics of the people who are eligible? Then let's see, how is the number of eligible people changed? How have the demographics of eligible people changed? Where do eligible people live? Those sorts of sub-questions. And so let's go ahead, and let me just highlight, though, that there are many sub-questions listed in each section, but of course this is really just the beginning and designed to prompt you to identify the detailed data questions that you might have. So of course there are many more questions that we could include, and the many more that you will have, I'm sure. So let's just go ahead and say we're just getting started, and we're interested in having a snapshot of who is eligible. So we would click here, and for every example you'll be taken to a page that looks like this. You will see the data questions across the top, so that allows you to switch easily between the different questions if you decide that your data question maybe is in one of these other categories. Likewise, you'll see the different types of analyses on the left, so you could easily switch between different types of analyses. And just to remind you, the sub-questions for that particular section appear over here as well. You'll see a description of the type of analysis up here at the top to remind you about what it is that you're looking at. And then every example is set up the same. It starts with an example data question. In this instance, the question is how many people with specific demographic characteristics in this example, seniors who are 65 or older, are eligible for our services. Then the next section is multiple analyses are possible, such as what proportion of our services, of our service areas poverty population includes seniors, what percentage of our areas seniors are in poverty. These numbers can be compared to internal data regarding the percentage of our clients who are seniors, and should that match the portion of the poverty population who are seniors. So these are just some ideas to get the juices flowing about what you might be able to ask and answer. Then you scroll down and there are data sources. And so in this particular example, we're just looking at external data and it identifies the specific tables that we would recommend using. But in some of the examples, you'll see both external and internal sources recommended. Then the next section is the instructions on the steps to do the analysis. This particular one shows some helpful screenshots for downloading data from the American Community Survey site. And it tells you in detailed instructions how to get at the data. Then after explaining how to get at that external data, there are tips about what to do once you download the data to Excel. Now we don't provide detailed instructions about how to use Excel. We hope that most folks know how to use Excel, but it didn't feel like the right venue for describing detailed steps about using Excel. But none of the examples here are terribly complicated. So this provides you just an example that was created with that external census data taken into Excel with a table and some pie charts created. And every example that we have on the site will show a sample visual like this. So in this particular example, the analysis looks at the share of the poverty population in Montana that is made up of seniors and also the percentage of seniors who are in poverty. So those are two different views of poverty for seniors. On the left, this shows that among everyone in poverty in Montana, 14% were seniors. Then on the right, this shows that amongst all of the seniors in Montana, 31% are below poverty. So thinking back about some of the questions that we looked at, a follow-up question might be that does the fact that one-third of seniors are in poverty make that particular population a high priority for this organization? That would be obviously something for them to answer internally. So now let's just say you're interested in understanding your eligible population, but you want to look at a spatial analysis. You can scroll back up to the top and click on this different type of analysis here, the geographic distribution. But I actually want, again, because we're just starting looking at the site, I'm going to actually go back here and enter it from the question again just to walk you through this. So if we started out saying we're interested in who is eligible and wanted to look at the geographic distribution, this is the route we would do, which would show us some of the sub-questions. But again, the other way is to get to the types of analysis is to click on these boxes on the left. Okay. So in this example, it's the same exact structure that we saw in the last one, but our example data question and analysis are different. So in this case, we're asking what is the distribution of our eligible population throughout our service area? And there are multiple analyses possible, such as where are the areas with high poverty and low poverty rates? Where are the areas with high and low numbers of people in poverty? Keeping in mind that looking at numbers and rates can result in very different patterns of poverty. You can look at broad trends by county or municipality or trends closer to the neighborhood level. Are the patterns of poverty as you expected? Or are there any surprises? So were there any pockets of poverty in your service area that popped up that you didn't expect? And then again, this shows a recommended American Community Survey site, I'm sorry, table. And it doesn't go into the screenshots of the last one did, but you can look back at that last example to look at those screenshots. So we have a couple different example analyses here. The first one, example one, does the distribution of eligible population vary by community? And in this example, one of our social service partners from a local university used GIS mapping to look at the data for Northeast Ohio. This is actually one of the analyses from that original TIG project. He used ArcGIS. It's a mapping software that most legal aids don't have, so the instructions here are very high level. The idea being that an example like this could be shown to a social service partner in your area with ArcGIS capabilities and that person would know how to replicate this type of analysis. There are other examples that we show where you don't have to have that kind of software and you can still do some spatial analysis. So we'll get to those. The map that you see here shows where poverty rates are highest. So the dark brown is where poverty rates are the highest and lowest. The more yellow areas are the areas of lowest poverty. The dark area in the center, go right around here, is the city of Cleveland. And then the dark areas to the far east are more rural. So those were pretty expected concentrations of poverty. So then the second example does the distribution of eligible population vary by community. And then this one is using the built-in mapping feature that's in the American Fact Finder site. So again, this is an example where you don't have to have fancy spatial software. You can just do this right from their site and their instructions here on how to create a map like the one that you see here, which, similar to what we just saw, but with obviously slightly less detail, this is a map showing the poverty rates by county in the state of Wyoming. So it's clearly not nearly as powerful and doesn't have the same kind of flexibility as GIS mapping, but this is something that's accessible to everyone. All right, so now let's say you're satisfied with what you know about the eligible population. And I will just remind you, though, that there are multiple types of analyses you can look at for who is eligible. But let's say now you're interested in knowing more about who requests assistance. So obviously, compared to the who is eligible section, we have many more sub-questions here for each of the different types of analyses. And they ask many of the same kinds of things in terms of the volume of people and their demographics. But in this instance, we're honing in not on a different population. We're honing in on the folks who actually request assistance from your legal aid. Whereas the who is eligible, we were looking at who in your community is eligible for your services. We're going to hone in on the trend section here. And so we'll just look at some of the sub-questions. How has the number of people requesting assistance changed? How the demographics of people requesting assistance changed? How have the number of people requesting assistance from your defined groups? So that might be a vulnerable population that you've identified as a high priority for the organization. How has the number of people requesting assistance within certain categories of legal problems changed? So that might be housing cases or family cases, for example. So let's look at an example trend analysis set up the same way. And a reminder that the description of the type of analysis is right here at the top of the screen. In this instance, the example data question is how has the number of people requesting assistance from our organization changed over time? Multiple analyses are possible. How do the trends in intake numbers compare to the trends in eligible people over time? What have been the annual changes in intakes compared to the annual changes in the eligible population over time? What percentage of all eligible people have requested assistance in each of the last five years? So in this instance, this is one where we talk about using internal data and external data. This S-17-01 poverty table is one that we use in a lot of analyses. But then it also spells out for you what type of intake data from your case management system would be helpful. And so it's some pretty basic stuff, but the demographics about what you are curious, the open and close date, other case information that might be useful, including problem code, other demographics, et cetera. And a note to exclude cases that were identified as errors or duplicates, but to make sure to keep cases that ended up not being served. And that's so that you can do a comparison of those who you're able to serve and those who you're not able to serve. So for this example, we'll go down to the example analysis steps, which again are the instructions. And so there are some basic Excel instructions and the instructions regarding downloading the ACS data. And then there is a specific recommendation here to create a combination chart, which is a type of chart in Excel. And for this particular image, we're looking at the changes in intakes and changes in the eligible population over the course of five years. The intakes being the green bars and the eligible population being the blue line. And so you can see that there was an increase in intakes and a bit of a dip. This particular legal aid knew that that had to do with some resource and staffing issues. And then as is the case in most areas, the eligible population was on the rise. At the bottom of the page is another example of an intakes dashboard and again intakes being who requests assistance. The example uses an open source program called Microsoft Power BI that can easily use data from Excel, among other sources, to create really nice visuals, including multiple types of analyses. I actually presented a tutorial on this program at the, at last year's, the January 2017 TIG conference. And would be happy to send that to anyone if they're interested. I really recommend that as a, this tool as a resource for conducting analyses. It's very easy to use and create some nice visuals. So let's scroll back up and take a look at another question. And that's who do we help? Again, multiple sub questions. And for this example, we're going to scroll down to the geographic concentration area. And that's the example we're going to look at. And so the questions, the sub questions in that particular area are things like, where do the people that we helped or were able to serve, where do they live? Where do the people serve with different demographics live? Where do the people served from your different defined groups live or different legal problems? So let's go ahead and click, I'm sorry, I'm going to scroll back. The, actually the example that I, but I really wanted to show you was the geographic concentration. And so let's actually look at those sub questions. Does the geographic concentration of intakes match the concentration of serve people? Does the geographic concentration of intakes match the concentration of serve people with specific demographics? Does the geographic concentration of intakes match the concentration of serve people from your defined groups with specific legal problems? So again, we're sort of always looking for what do we expect to be true? And then what does the data actually tell us is true? So now we'll click on the geographic concentration. And so we're going to, again, compare the concentrations of different variables to determine how, you know, determine if there are things that are, that require some further understanding so that we can focus in on where potentially things such as unmet, there are peaks of unmet need geographically and those sorts of things. There is a link that provides more information about location quotients. And so if you wanted to get more details about that, you could link here. But in a nutshell, what we want to do is to compare the share of the poverty population by county to the share of the clients we serve. So for example, what would it tell us if in one county, if one county had 10% of our service area's poverty population but only 5% of the clients we serve? So the location quotients which are calculated in this example compare those percentages and determine whether the service provided is lower than expected, close to expected, or higher than expected. So for this particular example, the question that we're looking at is how does the proportion of people we serve from different parts of our service area compared to the proportion of eligible people in those same areas? And this lays out that we're looking at closed case data and provides some recommendations of the kinds of fields that you would want to include. And then the recommendation about the external ACS table to use. So this example actually uses, there's lots of steps here, but we will end up with this map. And this example actually uses Microsoft Power BI. And there are lots of detailed steps here because I know most folks are not familiar with that tool compared to Excel. And so, and then likewise because location quotients are new to folks, there are pretty detailed descriptions here. So even though it looks like a lot of steps, it's actually pretty easy to do. I'm not going to walk you through word for word, but basically you gather external data about the share of the total poverty population that resides in each county. And then you gather data about the share of all the closed and served cases by county. You divide the percentage of served cases by the percentage share of the poverty population for each county. And you end up with ranges of numbers that range from, in this instance, 0.1 to 3. And let me just scroll down so you can see the image. That's hard to get on the whole screen. So this is again comparing the concentration of served cases in the state of New Mexico to the concentration of the poverty population. So the green areas are the ones that ended up with numbers of 1.26 to 3. That number itself is not as important again. It's dividing those proportions. But because it's above 1, it's telling us that we are serving more people in the green areas than would be expected if the only thing we were looking at is the share of those counties share of the poverty population. So for example, if in this county over here, if this county over here has 5% of the poverty population but 10% of the cases that we serve, that's the reason for it being green. We're serving at a higher rate than would be expected. Of course there are going to be things that come into play, policy decisions and priorities that may skew these things. And so when you see something that's more than expected or less than expected where you'll really want to dig in and try to understand is if you don't have an explanation already. There isn't a priority for the organization that leads to that more than expected or less than expected category. So again the yellow is where the level of service or the volume of service is almost equal to the rate of poverty in that area. And the red is where the poverty population is a higher share than the share of the folks that we're serving. And so sometimes that might indicate, you know, you can do this at all different geographic levels. You could do this at smaller geographic levels as well. And some folks have used this kind of understanding to say, all right, well in those red areas we might need to do a little bit of additional outreach or something along those lines. And then I just to highlight the areas that are white had fewer than 20 cases. And so they were omitted from the analysis because those numbers were too low to it might be skewed data. All right, so let's look and I should highlight again that this is again a spatial analysis that does not require fancy geographic software like ArcGIS. It's that free open source Microsoft Power BI that was used. So let's scroll back up and go to look at another question, how do we help? So how do we help is really getting at again the levels of service? Are we providing brief or limited service? Or are we able to provide more extended service? And this time we'll look at a type of analysis we haven't looked at yet a comparison and look at the sub questions that are things like how does the level of service differ by legal problem among different demographics. So there are some legal problems that we know are that require more extensive service and some that can be resolved with with more brief service. But there might be some fluctuations in that and some patterns that weren't expected that are worth looking at. And so basically that's the focus of this set of questions and this example is looking at comparing the levels of service to see if expected results arise. And so the comparison analysis review linkages between two or more variables and then cover information about client conditions and data relationships. When unexpected data relationships are discovered investigation is warranted to better understand those linkages and determine whether they indicate the need for client service or advocacy work that simultaneously targets multiple conditions at once. And just a reminder again about the structure of the site we've got our sub questions here and our links to the other types of analyses and links to the other types of data questions across the top. The example data question here is how do levels of service differ by legal problem among different racial categories? And there's just a note there that because many legal aids count Hispanic as a race rather than ethnicity it is considered a racial category in this example analysis. There are multiple analyses possible and some examples listed there. The first one which legal problem codes have the highest percentage of extended service cases. And the example here is do you know why and this is based on actual legal data? Do you know why 55% of clients with problem code 73 food stamps receive extended service while just 6% of clients with 63 private landlord tenant cases receive extended service? So that was a question for that legal aid to answer. And then another type of analysis and examples within problem code groupings to clients with different races have similar percentages of extended service. So for example, do you know why 60% of Hispanic clients with food stamp cases received extended service while 38% of white non-Hispanic clients with food stamp cases received extended service? These analyses are based on intake and close case data and in this case don't include external data. So again there are these example analysis steps which are like the instructions to conduct an analysis like this. And this table shows how to review the levels of service. So this column here is brief and this column is extended. These are the problem codes and then these are the racial categories. So the results that we felt warranted additional review are highlighted in either yellow or red circles. And so similar to the examples I mentioned above, in this instance when we're looking at the food stamps, the food stamps cases here in the sort of the second category, we can see that, and I called attention to this earlier, 60% of Hispanic clients received extended service and the 38% of the white clients. And so again we would want to understand the difference, you know, were there language issues or something else involved that meant that some folks needed additional time or what was the issue there. And then if we look at the red circles, we can see that 55% of all food stamp clients received extended service and again this example I called out before, the 6% of the landlord tenant cases. So even just a simple table like this can, when you start comparing the results across problem codes or across racial categories or across levels of service, you can uncover some interesting relationships that might warrant further investigation. So we'll look at one last example with the last question, which are what resources are required. And so I think I mentioned at the beginning that there are of course lots of resources required to serve clients, but we focus here on data around hours spent. And we'll look at the one type of analysis we haven't looked at yet, which is the geographic distribution and the sub questions there are for cases in which geographic, for cases in which, that isn't worded correctly. We'll have to edit that wording. So we want to know where hours are spent and another example of questions and what areas are the most staff and pro bono hours worked. All right, so the data question here is, do the average hours per case that we spend on cases vary by client zip codes? We consider multiple analyses are certain types of cases that require more time or less time coming from specific geographic areas. Do we tend to spend more time on cases for clients who live near our offices? Those sorts of things. And we used internal closed case data. Now this example, which also has lots of detailed steps, is using another free open source software called Cardo. And again, because it's one that I'm assuming most folks aren't familiar with, that's the reason for the length of the instructions. But just like with the Microsoft Power BI tool, I think it's pretty easy to use and this provides you with real step-by-step instructions on how to use it. So let's look at the visual. So what we're showing here is the average hours per closed case in 2016 by zip code. And that's for zip codes with 10 or more cases. So the darker the blue, the more hours spent per case. And then all the way over to the yellow, those are the fewer number of hours spent. So this organization would want to dig in further to understand how other things might be impacting these results, such as the distribution of particular types of legal problems. So our legal problems that require more time are they focused also down there where we see more hours, down in the southeast corner of the state. And then likewise, our legal problem codes that we would expect to require fewer hours are they skewed up in the northwest section. And if not, then what might be causing that difference? So we have reached the end of the examples. I want to just pop this up. So we've walked through today just six example analyses from the data analysis framework, but there are actually 25 different analyses combining various data questions and various types of analyses. Every example is different and provides unique instructions and unique visuals. And as you saw, there are different types of tools used from Excel, Microsoft Power BI, Carto, and then ArcGIS. It is our hope that the guide will help you figure out ways to analyze your data and data about your community and in ways that will allow you to serve your clients as effectively as possible. Thank you, and I'm happy to answer any questions. And of course, our contact information is there if you want to reach out to us individually. Yeah, definitely. If you have questions over that, please feel free to reach out to us or to ask questions generally on the LSM TAP listserv. I know there was a lot of information that was covered here today. Rachel, out of each of the tools that you talked about and the examples that you gave, if someone is doing this for the first time, should they dive in with one of the more complicated tools or how would you recommend someone get started? We've got a few people here that this is going to be their first project. Yeah, and that's a great question because the intention here is to try to make this kind of analysis less daunting for folks who have not done it before, but then obviously we saw some examples that did include some heavy lifting in terms of analyses. And so for folks who have more experience, they can find what they need here too. But I would say if you're just starting out, and I know a lot of folks are, and it can be overwhelming to figure out where to start, I would start noodling around just clicking on these different types of data questions and looking at those sub-questions to see if any of them, when you read through, are any of these things that you've wondered for your organization. And to just check out the different types of analyses, examples, and what you might do is scroll down first to the visual and then see if you can tell what it's showing you. And then if it's something you're interested in, you can go back up and look at what data you need and how to create something like this. And let me show you how easy it is to go between the different questions and the different types of analyses. So you can scroll across the top here to get at all of the questions. And then if you click on one of the analyses, you can scroll through, and again there's all these different examples here, you can scroll through. And so I think what I would suggest is just spending some time clicking through and reading things to see what sparks your interest. And I do think that these questions are in probably the order that I would recommend, which is to look first at who is eligible, although if you're not comfortable accessing external data, which is what this question is getting at because you're looking at community data, maybe jump right over to who requests assistance, intakes, who do we help, cases served, and how do we help, levels of service. Those three might be the first place to start with internal data. But more than anything, I think poking around, again, all of these questions were generated by legal aid organizations, the high level questions, and then those sub-questions. And then all of the examples that you see are using real legal aid data. So next question here is, can you tell us a little bit more about MOBI as a tool? Oh, is it MOBI? I wonder if they were referring to the Microsoft Power BI tool that I mentioned? It could be very likely because the Microsoft Power BI is free at this point, right? Yes, it is. And I think it's a really fantastic tool, especially for folks who are not as comfortable with creating their own types of charts and graphs and getting them to look lovely. So let me get to an example that shows that tool. So it's wonderful because you can download data to Excel, either internal case management data or external census data, or both. And you can link Microsoft Power BI to those data. And then with just a few clicks, you can... Here, let me get to this one. With just a few clicks, you can create these different types of charts and graphs. And it might be kind of awkward and clunky if I tried to pull up the program right now just for a live demonstration. But if whoever asked that question wants to reach out to me later, I could certainly do that with them. But it's really fun to play around with, too, because you can say, I think I want to look at this in a pie chart. And just with a click of a button, you click on the pie chart option. It creates it for you. And then if you say, oh, you know what? That isn't giving me the kind of data. I actually really need trend data over time. Then you just simply click on the trend data button or bar chart data, and it'll just change it for you. And so it's really, really easy to use. And again, I'm happy to share that presentation that I did back at the January TIG on that particular software. I'm also happy to talk to folks offline about it. That would be great. And there was also a request here to possibly do an online training over that. And we will definitely look at that for next year's training. Yeah, I'd be happy to do that. Yeah. And there's a question, is it better than Excel charts? Well, it probably depends on your level of experience with Excel. If you're really advanced and know how to create pivot tables and pivot charts on your own, and you'd like to have a little bit more control, then go for it. But if you're just starting out or just aren't as comfortable, this is, I think, really easy to use and really a lot more efficient. Because I think those of us who have used Excel can attest to the fact that sometimes you get sort of buried in the minutiae of trying to make it look exactly how you'd like it to look and what's the best way to depict different information. And I feel like Microsoft Power BI kind of does some of that for you. So I would say if you're starting, well, even if you're really comfortable with Excel, I would say dip your toe in the water and test it and see what you think. But then, again, for beginners, I think it's great, easy to use. That looks like all we've got at this point. I will also remind people that we have done some past trainings in Excel. They're archived on our YouTube channel. They don't specifically cover this, although when we do trainings for next year, we will definitely consider that. Any parting words? I guess the parting word would just be that I know that it can be intimidating sometimes to try to figure out how to tackle the amount of data that you all have. But I would urge you to just jump in and start playing around with some of these things because it can provide you with such important insight about your clients and their needs and how well you're serving them. And it really doesn't have to be hard. I hope that you'll find all of these examples and instructions easy to follow and replicate with your own data. And I would just say if there's any technical questions, feel free to email me.