 So today we're gonna talk about taking data and making it do some work for you, because otherwise we're just collectors and hoarders. I'm gonna tell you a little bit about our guest experts. So, Amelia Kahn is the founder of DataVis for Nonprofits, which delivers high quality visualizations that helps organizations quickly grasp their data, improve their work, and show their impact. Amelia has more than 25 years of experience studying, funding, and evaluating human services, initially as a researcher at Champaign Hall at the University of Chicago. Amelia has also done some hard work. She earned her PhD from the University of Bath and was trained in data visualization at the University of Washington. But with that, I'm gonna get out of the way because you actually gave me a familiar. So good to be with you all today. I'm gonna go through this at a pretty fast flip so we can get through a lot of information in a short time. So with that, I am going to ask you for the next about 30 seconds just to train your attention to this at a pretty fast flip so we can get through a lot of information in a short time. Feel free to put your questions in the chat. I'll take most of them at the end of the presentation but you can submit them at any time. I am going to ask you for the next about 30 seconds just to train your attention to the screen. Great. So my plan today is to take you on a sort of transformative journey to a version of yourself that makes better use of your superpowers or secret weapons if you prefer. And okay, so I'm sure each one of you has multiple superpowers but today we're gonna focus on just two of them. The first is your ability to process visual information at lightning speed. And the second is the data that your organization collects every day. Data visualization puts these two superpowers or secret weapons into action. It will help you to understand and show what you do and to craft new strategies to improve like you do. So here's the journey which I've visualized as a sort of graph. We begin now by discussing the very rational reasons that many people in nonprofit organizations often don't make good use of data. Then we'll consider your two secret weapons. Then we'll consider your challenges and how do you use those secret weapons to address those challenges. And I'll drive to a close by pointing you to more resources that will allow you to hone your secret weapons beyond this workshop and then we'll open it up to questions. Just a little bit more about me before we jump into our journey. I love evidence but I hate spreadsheets. I have more than 25 years of experience studying funding and valuing human services. As Elijah mentioned, I worked as a researcher at Chapin Hall which is a child policy center at the University of Chicago. At Chapin Hall I worked with a lot of organizations and their data. And I came to know the difficulties that many organizations face in using data well. And I felt there had to be a better way for organizations without high level data in-house to extract meaning quickly and painlessly from their data and put it to work for them. And that's when I got interested in data visualization or data vids for short. As Elijah mentioned, I got training at the University of Washington and I now help nonprofits, consultants and foundations to visualize their data through my consulting business which is called Data vids for nonprofits. All right, so let's start step one why you don't use data or probably more accurately why don't you use it as effectively as you could? So in my work with organizations that come across six primary reasons why people don't make better use of data. Let's talk about each one. The first reason is data naivete or aversion. So nonprofit staff tend to have expertise in the environment, arts, health, education but generally it's not so common to have expertise in data analysis. Some have a downright data aversion they may admit or even proudly proclaim that they're not numbers people. Non-profit staff don't often have the time for data. They're busy staying afloat submitting the next proposal maintaining programming of course addressing the huge and varied needs of their clientele cultivating new donors and digging through data is put on the back burner, fear. So some understandably worry what their data will show. Non-profit staff may fear that they won't be able to control the story that their data will be taken out of context that they'll be compared to other organizations which aren't indeed comparable. They fear that funders will withdraw support based on data no matter how much those funders might talk about continuous quality improvement and such. And dirty data is another reason. Some nonprofits have low level staff entering data into management information systems or spreadsheets. Others may have multiple staff members entering data and each person's entering the data a little differently. And this can result in what we call dirty data that's inaccurate because it's not been entered in a consistent way. For example, if a participant shows up at your organization and is entered into the database as Michael Smith then Michael Smith drops out but comes back six months later and gets entered in again as Michael B Smith then tracking this participant's progress through your program is gonna be really difficult because as far as the data concern he's two different people. Wrong data. Many nonprofits have data on their participants and financials but they often lack data to show impact. A tutoring program for example may not have their participants' school grades or test scores because it's hard to get that out of school districts. An employment program may not have data on their former participants' wages over time because it's really costly and time consuming to track down former participants. And definitely a lot of outcomes are hard to quantify and require surveying participants which again is costly. So organizations often lack the time and funds to collect impact data, disconnected data. So rather than having a central management information system some nonprofits may store their data in separate Excel or Google spreadsheets. So Michael Smith's demographics might be on one sheet and his attendance in various programs are other sheets and this makes analysis of say the relationship of age which is a demographic to program participation impossible because the data is disconnected. It's in different places. Okay, so we just talked about why you don't use data and guess what one of your secret weapons is? As I already mentioned it's data. So most organizations have lots of data packed away in databases and spreadsheets that they're not putting to good use. Data of course is the way we know like what we do is worthwhile. You can think of it as a tool and we don't invest in other tools say computers and then just store them away and never abuse them but that's pumped in what we're doing with data. Even small organizations collect lots of different types of data and here's just a sample of the types of data that many nonprofits collect. Okay, so this is the point of the presentation where I give my little commercial or public service announcement if you will about data. Now, I understand that you may feel that data doesn't need a commercial. That's already, we hear and talk about data all the time. We hear about evidence-based practices, data-driven solutions, key performance indicators but in all the hype and talk around data I think we often lose sight of what its core importance is. So that's why I'm offering you this little commercial. Data is important because of how perception occurs. What we perceive is based not just on what we actually observe in the outside world what we might call data but also what we expect to observe. So this is how it works very roughly. The brain evaluates which of a variety of probable events are actually occurring and what we perceive is based in part of what decision the brain makes. Sure what we perceive is also based on all those incoming signals from the outside world but there are far more signals coming from within our brains that affect perception. As Ana Nislin once said, we don't see things as they are, we see them as we are. So a great example of this is a YouTube video that went viral several years ago. I'm not gonna show it to you because I bet you've already seen it. If not, I'm gonna give you the elevator version of it which is that if you're like me you saw it as part of a presentation the presenter gets up and says I'm about to show you a two minute video and your job is to count how many times that basketball gets passed among the people in the video. So if you're like me you dutifully watch the video you counted the number of passes and then the presenter comes back on and says raise your hand if you saw a guy in a gorilla suit walk through the frame. And again, if you're like me you saw no one in a gorilla suit. And sure enough, the presenter shows the video again that gorilla suit guy is really playing to see but many people don't see him because they don't expect to see him. So these inner brain signals or expectations can distort our understanding of a situation. Thus, those incoming signals or data from the outside world are quite important to confirming or negating our expectations. But the trick is we have to pay attention to data to make that happen, progress in organizations and indeed in all of human history often starts with the concession that our expectations might be wrong and that data might have something to teach us. Of course the scientific method primes us to focus on data rather than expectations. We formulate a hypothesis and then use data in the outside world to see if the hypothesis holds up. Okay, commercial over but here's my little disclaimer. Data is indeed part of the answer but it's certainly not the whole answer. Perhaps love is, I don't know. Indeed, data is more about questions than it is about answers. It can show you what questions you should be asking help you to refine those questions. It can also point you in the general right direction but you are never going to have perfect and comprehensive data. Data will never take you all the way to your answer. Also, we should be wary, although not fearful of data. We should always be asking whenever we confront data, where does it come from? Is the source likely to be biased? If it's drawn from a sample, is that sample representative of a larger population we're trying to learn about? What's missing from the data? Are the groups of people or perhaps time periods missing from the data and why are they missing? Are some survey respondents, for example, hard to reach or do they like trust in the data collector? We really need to know about our data. Okay, let's see. Oh yeah, skipped one there. Now let's talk about your second secret weapon, your visual superpowers. So quick question, which image do you comprehend first? The Mona Lisa or the spreadsheet? Okay, please do not put your answers in the chat. I'm gonna just take a wild guess that you understand the Mona Lisa more quickly. And that's because our visual system has evolved over millions of years to process images essentially in parallel. So what do I mean by that? Essentially in parallel. We don't read the Mona Lisa from top to bottom and from left to right. We take it all in together and understand almost instantly that this is a picture of a woman in front of a landscape, sporting a dark dress and an inscrutable smile. Words and numbers only appear 2,000 years and have been humans for less than 1,000 years by most humans. By contrast, we and our ancestors have been processing visual information for millions of years. And while all types of sensory signals barrage, the brain especially geared for visual signals and uses about half of its value for visual analysis. That's because from most of human history, survival depended on detecting danger, such as predators lying in the tall grass and distance using our visual systems. So to be just at the beginning of our journey with data, processing words and numbers is quite a recent activity for humans. Perhaps as our brains evolve, we'll be able to discern a spreadsheet just at a glance. But until then, we should consider visualizing our data by translating words and numbers into visual formats using color, shape, size, and placement. Data visualizations greatly speed up our processing of data and so addresses in part that time crunch we talked about. Data Viz also is a quick way to assess how dirty, disconnected, or irrelevant our data is. If the picture doesn't look right or complete, we need to do something to improve our data. Maybe that means collecting fewer data elements but doing some more accurately. Okay, let's test out our visual superpowers. I'm gonna give you two more seconds to determine how many, nine in this array of numbers. Okay, so now I'm showing it to you with the visual cube color and you can see it's much easier to pick up the nice. Okay, let's do another little test. I'd like you to quickly try to answer the three questions you see here on the slide using the very simple spreadsheet above where foundation or corporate grants consistently higher over time and what were the trends for each one of these types of grants, foundation grants and corporate grants. All right, two more seconds, one, two. Because when I show you this exact same data with a graph, it's so much easier. We can see right away that corporate grants were consistently higher over time than the foundation grants. Foundation grants remained relatively flat with one exception that sort of spike in May and corporate grants exhibited a cyclical pattern over time, but up, up, down pattern that repeated itself on a quarterly basis, always reaching the peak in the last quarter of the month or I'm sorry, the last month of the quarter and then declining dramatically in the first month of the next quarter. That pattern would be really hard to see just in spread. Not only is data displayed visually easier to process, it actually appears to have more impact. So a June 2018 Washington Post article described a series of experiments in which data displayed in charts significantly reduced the misperceptions of subjects, both liberal and conservative on important political issues. So this chart that you're looking at shows the results of just one of those experiments. In this experiment, Republican, and I'm in the U.S. and we're talking about Republicans in the U.S. Republican respondents were asked whether they believed that the average global temperatures were increasing, decreasing or staying the same over the past 30 years. The respondents were divided into three groups. There was a controlled group and they're represented by the orange bars on this chart. They received no information. They were just asked that question about what's happening with global temperatures over time. Then there was another group, those represented by gray bars in the chart and these people were given a textual summary of the scientific consensus on global temperatures. Then the third group represented by the blue bars got a chart which showed temperature change since 1940 as measured by four climate institutions. And as you can see, the people who saw the chart, again, those represented by the blue bars were the least likely to draw the wrong conclusion about global temperatures. Strangely enough, the textual information actually exacerbated the perception problem for Republicans who strongly identified with the GOP. I don't have time to show you the results of the other experiments, but even the experiments with liberal subjects had just the same results. Charts really did a good job in correcting their misperception of various topics. Okay, so you have these so-called secret weapons. What are they good for? Let's consider what some of your greatest challenges might working in a nonprofit and then we'll consider how to address those challenges with data visualization. Okay, so today I'm gonna just focus on four common challenges. One is showing the impact of what you do. Another is distinguishing your services from that of other organizations. Of course, evaluating your progress over time and showing that progress to various people is also a challenge for many organizations. And we've reached the heart of the presentation, which is how do you use DataViz to conquer the challenges that you have? So one way organizations use data is to make the case for the need for their services. And here's an example from over 200 years ago. In the 1800s, Lawrence Nightingale and a statistician named William Barr analyzed army mortality rates during the Crimean War. And they discovered that most of the soldiers hadn't died in combat, but rather by preventable diseases caused by poor hygiene. So Nightingale decided to make her case with pictures rather than tables of numbers or pros. And Nightingale's own words, quote, to affect through the eyes what we fail to convey to the public through their word-proof ears, end quote. So her invention was the polar area chart. And as you can see, it's a variant of the pie chart. So each slice of the pie showed deaths for one month of the war, growing larger if the deaths increased in color coded to show the causes of death. So the blue portions represent the preventable deaths due to poor hygiene. The red portion showed deaths due to wounds in battle and the black portions were deaths due to other causes. So the queen and parliament could see at a glance the importance of hygiene. They quickly set up a sanitary commission to improve conditions and indeed death rates fell. Nightingale became one of the first people to successfully use data visualization for persuasion specifically to influence public policy. There's lots of different ways to show need with data visualizations. You can show the prevalence of a problem with icons such as in this chart. This chart also makes use of color and it may make more intuitive sense to many people than a bar chart. You won't want to overuse an icon chart. You want to have a whole bunch of them side by side could be pretty overwhelming. But one well-placed icon chart can be pretty persuasive. This is a quadrants chart. A quadrants chart shows the relationship between two key measures and can be a great way to show exactly where the need is. So I created this chart for a client who was working with a number of school districts to implement what they call deeper learning strategies. And the first thing they did when they started their project was they surveyed principles and teachers at the schools involved and asked them questions to ascertain how important each of the deeper learning strategies were to them and how often they already put these strategies into practice. And this chart shows the overall results for all principals and teachers who completed the survey. So each dot represents one of the deeper learning strategies and each dot is positioned to show the average importance assigned to that strategy and the average frequency of practice of that strategy. So the consultants could see really at a glance that although the teachers and principals already felt the strategies were quite important, you can see that all the dots are clustered to the right part of the chart. They were putting them into practice less than 50% of the time with the exception of directed instruction, which was being put into practice a little bit more than 50% of the time. So they could see that the need wasn't in addressing the obstacles that were preventing these educators from doing what they already believed was really important. So it really helped them to focus on where the need was. Viewers are most interested in data that involves themselves, surprise. So this is a cool way to put quotes to the viewer in the videos. You can ask them to guess what a statistic is rather than just presenting them with the statistics. So this is an interactive visualization. I'm just showing you a snapshot of it, but if you were interacting with it, you would see the question, what percent of black, not Hispanic children experienced poverty in 2019? And as you can see, there's a little slider where you can enter in your guests and then you mouse over those words near the bottom, how'd you do? And when you mouse over those words, you get this little chart which shows you what you guessed, what the data actually show and what others guessed. So you can compare your answers to the actual information as well as what other people guessed. It's a great way to engage people in an issue and in the advocacy. It's another thing you might be wanting to make the case for. Nothing's more effective than a bar chart. It may seem tired, but we all know how to read bar charts. And so the composite load is really low. We can see right away that the goal was reached in April and then exceeded in succeeding months. Let's talk a little bit more about showing efficacy. This chart makes an even stronger point by allowing a comparison of a trajectory for one group that got an intervention to another that didn't. We can see the group A got the intervention and did much better in subsequent. Continuing to talk about showing efficacy, this uses a number of techniques. The chart type is called a stacked area chart. It's also used in a strategy called small multiples where you put a bunch of small charts side by side to make work easy comparisons across charts. They also use a hundred people rather than showing percentages. Humans brains often glitch on percentages, but we can all imagine a hundred people. And the notations here are helpful too. We can quickly see that poverty and child mortality have decreased dramatically over the last 200 years and that basic education, literacy, democracy and vaccination have all increased dramatically in the same time period. Showing unique nips. So this chart shows a comparison of female or male students on what advanced placement tests they take. It actually shows where there is similarity and where the two groups diverge. So if you read this chart from bottom to top, you can see about the same number of people who identify as male or people who identify as female take the Latin tests and statistics tests. But when you get up into tests on language and art, the percentages really diverge. Another way to show uniqueness is in a chart like this. This is called a tree map and it compares one organization to others using shape and color to show the number of clients served. You can see that the organization called Youth Together served more clients than all the other organizations combined. And we'll talk a bit about evaluation and planning. So logic models or theory of change, there's lots of names for them. Get a lot of play in proposals. These are just simple flow charts that show how a program or whole organization is supposed to work. But what I thought is often these sort of flow charts or logic models get a lot of play in proposals and then they collect tests. And so I invented something called a living logic model which is a logic model plugged into real-time data. And it shows progress to date using color and it's interactive. So when you click on any of the squares at the top you get more data on that measure or component of the program below. You can also scroll over components to learn more about different aspects of the program. This information can include images and web links. So it's a great way to track your progress and evaluate how you're doing, where you need to be, switching cores and investing more resources and investing fewer resources. Okay, so now let's talk about how to turn a good visualization into a great one. If you follow these 10 data vests I can almost guarantee you'll be improving your charts. Okay, first is to clean your data. It may seem obvious, but take a look at your data before you ever visualize it. Look for missing data, duplicated data, formatting issues, such as different formats for dates. Everything writes on the quality of your data. So don't skip this step. Encode thoughtful, clean linen McGill as well as some other researchers have studied what type of encodings or channels people are able to decode most accurately and rank them in the following list. So encodings or channels are just the visual representations we use for data. So as you can see, we can most accurately make comparisons when it comes to position along a common scale or length, but we're not so good when it comes to making comparisons using curvature or shading. So think about it. If I showed you two lines, you could much more confidently say one line is twice as long as the other line, but if I showed you two shades of green, I think you'd be much harder pressed to say that one shade is twice as dark as another shade. Look, we want our viewers to make more accurate comparisons. We use encodings towards the right hand end of the scale, but it's just generic comparisons are sufficient. We can use the encodings towards the left end of the scale. Okay, pies, of course, are delicious, but are often inscrutable when applied to data. And this chart shows you why. So looking at this image, we can confidently proclaim that the E bar is the tallest, but we'd be hard pressed to pick out which pie slice is the largest. That's because we don't do so well with angles. So when comparing the quantities of several things, bar charts are almost always better than pie charts. I came across this eyesore of a chart recently and thought it was pretty funny that the pie chart isn't an effective way to show the number of countries that use different terms for this type of chart. Although it's fun to know that the term for this chart in Catalan translates to cheese portions, graphic, and in Bulgarian to ceremonial bread. The only exception is when a pie chart really is a good way to go is when you want to compare a part to a whole. But once you get beyond two or maybe three slices, it's best to skip the pie chart and dust off the trustee old bar chart. Okay, so adjustment number three is to highlight what's important. Once you visualize your data, the story can still remain hidden, particularly to those who are unfamiliar with the data. So that's when it's time to call out key data points with color and annotations as in this chart. Here is some data from the US Center for Disease Control on chronic illness, which shows an example of highlighting what's important. This is called a parallel coordinates graph. It shows the level or amount to something across several dimensions. In this case, it's showing the percent of adults with chronic illnesses across states in the US. So each line represents a state, each one of those gray lines. And notations of color draw attention to how low residents of Hawaii are in relationship to other states on rates of depression. And so it's an interactive chart so you can choose a particular state to highlight. In this case, Hawaii is highlighted and you can see with all the different chronic illnesses that are along the horizontal or X axis. When we look at depression, we could see that Hawaii is really low. It's the lowest in terms of rates of depression. And that notation notes that Oregon is actually the highest in terms of rates of depression. Another suggestion is to order your data. And specifically ordered data should be shown in a way that our perceptual system intrinsically senses it is ordered. Conversely, unordered data should not be shown in a way that perceptually implies an order that doesn't exist. These charts, the charts on the top show what a series of bar charts looks like with unordered data. Here you're looking at health center data, specifically the percent of health visits that included depression screening across different care teams in family medicine, pediatrics and women's health. As you can see, the ones on top are not ordered. So you can have to do some mental gymnastics to figure out which teams are doing best, which teams are doing worse. Easier just to order those bars in terms of best and worse as the charts on the bottom do makes easier for you more. Okay, my next suggestion is to clarify with color. So don't use the same color cube for two different variables. Don't use the same color saturation for different magnitudes of the same variable. Don't use too many color encodings on one dashboard or presentation. And rightly fully saturated color is kind of like yelling. And you don't want to yell all the time. People think you're crazy and stop listening. So moderate color as you might moderate your voice and only yell when you do something really important to say. And then provide all the other information, softer, less saturated colors to provide color. So here's a big dashboard that breaks all the rules that I just enumerated uses the same color to mean different dates and side-by-side charts. It uses a lot of right fully saturated colors. It uses many different colors. Do these cheats charts look the same to you? If so, you may have red, green color blindness. Seven to 10% of men are red, green color blind. The percentage is much lower in women because it's actually a trait that exists on the X chromosome. And the chances of having both X chromosomes for women is very low. But we should take this into consideration. Seven to 10% of men is a large percentage. And we should try not to put red and green next to each other in charts because it's gonna make it harder for them to make distinction. These maps show that Center for Disease Control chronic illness survey data. And it uses what's called a diverging color palette to emphasize contrast. So the blue tone show where the states where there are high ranks and two health indexes, basically the blue part show where there are healthier adults, darker blue means even healthier adults and the orange shading show less healthy adults and the gray shade somewhere in the middle. So it makes it really easy to pick out, for example, that corridor for health in the Southeast. Next suggestion is to delete what's unnecessary. If you've read anything by Edward Tufty, who's the grandfather of moderate data visualization that the data about the data to ink ratio, which should be as close to one as possible. It's a really good thing to keep in mind. It just means to eliminate non-data ink or pixels to help make the vis look more clean and to focus attention on the key data points by eliminating any distracting visual elements. So here are two charts showing the exact same data, but on the chart in the bottom, I've removed the red lines and the data labels and even the color legend. I just re-placed the color legend with labels for each line and with fewer distractions that you are can comprehend the trends of the two groups much more easily. This suggestion for no unjustified 3D comes from Tamara Luncer at the University of British Columbia and it's referring to those Excel charts that you've seen before where it distorts the chart to make it look three-dimensional. It does that using a technical shortening in which you reduce or distort in order to convey the illusion of three dimensions on the two-dimensional space. So parts of the image that are supposed to be perceived as closer in space are made larger like the red slice of this pie and parts that are supposed to be perceived as farther away are made smaller like the green and blue slices. But as you can see, the angles get distorted as a result of the 3D of it and it can make it much harder to figure out which pie slice is larger and which is smaller. 3D one makes it look like the reds slice is largest, but indeed all three slices are exactly the same size. To create the illusion of three dimensions, another technique is to obscure some objects with others to make it appear that one object is in front of another. But of course, this is a problem for accurate assessment and data visualization as well. In the top chart, the green bars are fairly visible and rather than have that third axis, you can just use color to distinguish three groups as the chart on the bottom does, but both charts do already. So that third axis is really completely unnecessarily unnecessary and only serves to make the chart more difficult to read. Much easier to discern that the A group in October is larger than the C group in the chart on the bottom than in the chart in the top. A rate, this suggestion also comes from Sparrow Munser which is that eyes beat memory. It's easier to compare two different charts by moving our eyes side to side rather than having to hold one chart in memory and compare it to a chart that you're looking at the time. But I mentioned before this idea of small multiples charts, they really address the power of the eye over memory. So small charts are laid out together to make it easy for you to scan across them. In this chart, which comes from Doug McHugh who has a great blog, you can quickly scan to see the difference between the different types of causes of depth. The yellow bars on top show number of daytime depths and the blue bars on the bottom show number of evening depths. And you can see right away that driving under the influence and drunkenness are more often to occur at night and suicide and trespassing are more likely to happen during the day. All right, so adjustment nine is to zoom in. So when showing a series of visualizations, it's best to start with the most general and then zoom into the more specific. Here's an image of a series of visualizations, two of which you've already seen showing that Center for Disease Control chronic illness data. The maps give you a general sense of where there are health concerns according to the data set, such as that corridor for health in Southeast and the parallel coordinates chart helps to compare states and specific illnesses and conditions. And then there's a final chart, which I didn't show you, which allows the viewer to really dig into the data and interact with it and look into trends and possible predictors of good and poor health. And finally, my last suggestion is that visualizing data is not always the way to go. Tables can work better when you have a diverse audience or many units of measures. So let's talk about diverse audiences. So let's say you already have an engaged but really diverse audience. These folks are highly motivated to access certain data and won't be annoyed by having to find the data that interests them in a table. But they're also diverse in their interests. Tables make really good use of paper or screen real estate. You can fit a lot of rows and a lot of columns in a small space, allowing users with different interests to find the data that they want in a single table. Or you may have many units of measure. For example, you wanna show the height, weight, location and satisfaction level of 100 participants in a healthy eating program. This data involves four different units of measure. So inches, pounds, latitude and longitude and survey ratings. Such complexity is difficult to represent in a single visualization because you have to come up with a different way to represent each one of those measures. But you could do so really easily in a table. So that might be another case in which you might wanna use a table. Okay, I'll quickly go through some resources. So you might wanna check out this website. I'm gonna be sharing the slides with Elijah. So you can get these slides if you want and you won't have to write down these resources right now. But this website is just a sampling of different data visualization software and online tools you can use to visualize data. You might be particularly interested in this list because these are all free data visualization tools that are out there. And there's also some great tools online that will help you decide which charts you use for different purposes and different types of data. The most basic one out there is this one. It's called chart suggestions of thought starter. And it's really a classic. It's based on Jean Zalasny's book called Saying It With Charts. And it's really just a decision tree that starts in the middle with the question, what do you wanna show? And then it provides for options, comparison, relationship, composition, and distribution and walks you through some different questions to help you arrive at a chart that will work for your purposes. But there's other ones out there. There's this one which allows, this is from a great website called Flowy Data that also walks you through and helps you make that decision. And this one is from Stephanie Evergreen. If you just Google the words chart chooser, you'll find a whole bunch of options. There's also this data visualization catalog. It's like a data visualization dictionary. So if you come across a chart and you don't know what it is, know what its purpose is, and what it's best for, you can look it up in the data visualization catalog. There's lots of data visualization gurus out here. Here's a list of some really well-known ones. They all have websites and blogs and books that you might wanna check out. And I hope that I can continue to be a resource for you through my organization, Databiz for Nonprofits. I help organizations clarify the questions they wanna answer. And I help, I create for them interactive data dashboards, reports, presentations, infographics. And I do workshops like this one. We should feel free to sign up for a free consultation with me on my website. I think Elijah's gonna put in the chat my address for my website, but it's nonprofitdata.com. I also have a blog and we'll send you a 60 second data tip on a weekly basis if you sign up. Elijah's gonna put the link for that in the chat as well. And actually, if you just put your address, your email address in the chat right now, I will sign you up for you. And then you'll start getting these 60 second data tips each week. All right, we are at discussion. We have just a few minutes left if there are any questions. Thank you, Mr. Amelia. Thank you so much. Lots of really great practical examples of how do we take this story we wanna tell in this hunk of data and what are some possible ways we can, as opposed to just doing the three things that Excel does easily. One question that sort of came up earlier was around this issue we always saw around bad data quality. And I wonder if you have some tips on how we can start improving our data quality within our organizations. Yeah, a really important thing to do if you're not already doing it is to have a data dictionary. So a data dictionary is kind of exactly what it sounds like. It's usually just a spreadsheet and it has every data field that's in your database or your spreadsheet or whatever and it defines the data field. So what you should include, what you shouldn't include, it will give you the format that needs to be used. So if it's the data field is a date field, it will say what format you should be putting the data in. It may give it some examples of acceptable values for that data field. And that's if you have that kind of handbook or dictionary, it really helps to make sure that data is entered in a consistent way. And of course, a lot of databases have data validation. So it won't allow you to put things in the raw format or may give you like a little error message to ensure that your data's being entered more accurately. Of course, none of that helps if you're not entering your data on a regular basis. So that's just making sure that you have routines and you're entering your data a lot. I really feel most people who are working on profits just see data entry as a big time suck and it's not worth their time. They are entering all this data into a black hole because they don't see anything coming out the other end. What I do for a living is create interactive data dashboards that help organizations to visualize their data so that anyone, no matter how data versus data naive they are can understand what's going on. And I feel that when organizations provide those kind of data dashboards for their staff or the board members or potential donors see the value of the data and maybe much more motivated on the input end to enter data regularly. So yeah, if they actually see that it has value they're actually gonna do the work as opposed to just like it's another make work project. Exactly, exactly. So one last question which is you offered a couple of interesting tools for people to explore around how we can start doing some data visualization. If someone was curious, what one tool would you recommend they start? Just like to start playing. My favorite tool is Tableau. It's one of the major data visualization software out there. And the really cool thing about Tableau is that you can use it for free. So they have a free version and the subscription version. The main difference between the free version, subscription version is one, you can't save interactive data visualizations that you build on Tableau public, the free version on your own server or your own desktop. You have to save it to Tableau server. So it's really for nonsensitive data but you can use it with like a free dataset that you get off the internet just to learn Tableau. And I use it with a lot of my clients who are using nonsensitive data. They may have identified their data so you can't, there's nothing, there's no first names or first dates or addresses in there. So a lot of organizations feel quite comfortable visualizing their data using this free tool because no one could figure out who these people are. So anyway, I would suggest that you start playing around with Tableau using the free version. Tableau advertises itself as out of the box. Start visualizing your data tomorrow and I get to meet anyone who agrees with that characterization. It is a learning curve but if you're willing to invest some time the payoff is really big. It's incredible tool, lots of versatility. What I often do is because I already climbed that steep learning curve is that I create a really sophisticated data dashboard for you and then I teach you a little bit of Tableau so you can maintain it yourself. You don't have to learn all of Tableau. So that's another option. Smart and just a heads up. So in addition to Tableau Public the subscription offerings with Tableau are actually available at a nonprofit discount through TechSoup US, Canada and I think a couple other flavors as well. Excellent. Somelia, thank you. That was fabulous and super inspiring.