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Top Tips for Data Dashboards Done Right with Andy Cotgreave

Andy Cotgreave’s Do’s and Don’ts of Data Dashboards

Dashboards are a powerful data viz tool, but all too often, they are poorly done and not used to the best of their ability. Our guest today, joins us to shed light on what makes dashboards stand out and convey information in an accessible, impactful way.

Andy Cotgreave is a Technical Evangelist at Tableau Software with a cult-like following, a columnist for Information Age, and host of If Data Could Talk. He is also the co-author of The Big Book of Dashboards.

Andy has inspired thousands of people, giving them technical advice and sparking ideas on how to identify trends and unearth their own data-discovery skills.

In this episode, he sheds important light on where so many dashboards go wrong and how they differ from data presentations.. Data alone can only do so much of the work; how it is communicated is as important, yet people often neglect this aspect. We also discuss the value of rehearsing and building fluency in the language you are speaking.

Andy’s passion is truly inspiring, and his invaluable insights into dashboards and presentations are hard to beat!

In This Episode, You’ll Learn…

  • How becoming a magician helped Andy become a better presenter.
  • The most common mistakes Andy sees people making when it comes to presenting data.
  • Tips on how dashboards can be tailored for presentations.
  • How you can adapt images and information from platforms like Google Analytics for presentations.
  • Insights on how to present complex graphs in a digestible way.
  • Andy's takes on color and how he believes it should be used in dashboards.
  • Characteristics that make dashboards stand out, according to Andy.
  • Some of the visualizations people are using which are doing more harm than good.
  • The book Andy is loving right now.
  • Exciting developments at Tableau that are exciting Andy most currently.

People, Blogs, and Resources Mentioned

How to Connect with Andy Cotgreave:

Thanks for Listening!

Thanks so much for joining me. Have some feedback you’d like to share, or a question? Leave a note in the comments below, and we’ll get back to you!

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If you liked what you heard, I would love if you could leave me a rating or review in iTunes. Ratings & reviews are extremely appreciated and very important in the rankings algorithm. The more ratings, the better chance of fellow practitioners getting to hear this helpful information!

And finally, don’t forget to subscribe to the show on iTunes to get automatic updates and never miss a show.

Always remember: viz responsibly, my friends.

Namaste,

Lea Signature

[00:00:00] LP: Hello, hello. Lea Pica here. Today's guest is known worldwide for helping data viz practitioners put on a show using the one and only Tableau. Stay tuned to find out who's popping the bubbly charts on the Present Beyond Measure Show, episode 65.

 

[00:00:16] ANNOUNCER: Welcome to the Present Beyond Measure Show, a podcast at the intersection of analytics, data visualization, and presentation awesomeness. You'll learn the best tips, tools, and techniques for creating analytics visualizations and presentations that inspire data-driven decisions and move you forward. If you're ready to get your insights understood and acted upon, you're in the right place. And now your host, Lea Pica.

 

[EPISODE]

 

[00:00:43] LP: Oh, my goodness. Hello and welcome to the 65th episode of the Present Beyond Measure Show, the only podcast at the intersection of presentation, data visualization, storytelling, and analytics. This is the place to be if you're ready to make maximum impact and create credibility through thoughtfully presented insights and ideas.

 

Well, well, well. I've come to what I hope to be the final long hiatus of this show. You may or may not be aware, but I've spent the last six months every day chained to my desk for six hours a day, sometimes more, writing my very first book manuscript. This was the most intensive work project I've ever undertaken. Unfortunately, I had to reduce all other business activities down to critical power levels only. So I'm so thrilled to announce that my first draft is in the hands of my very capable editors, and I'm taking a very needed break from the book before the edits come back. It’s a break that's finally letting me get the show back online because I miss you guys, and I really miss getting to share the wisdom of my amazing guests.

 

Luckily, I have a killer lineup waiting for you over the coming months, and I'll also be going solo to share snippets from the book along the way, which is due out, fingers crossed, by the end of this year. So if you're interested in being the first to know launch details about the book, plus exclusive goodies like bonus chapters, resources, and how to even be involved in the launch, then you can join my book waiting list at leapica.com/thebook. I can't wait for you to join me on this very exciting new journey.

 

Now, as always, I am extremely excited about today's guests that never changes. But in particular, I really had the privilege of connecting with some folks on the dashboard side of the equation in the data visualization world, and I've got several amazing experts and one of your favorite data viz platforms lined up. That's right, Tableau. So I'm really excited to bring a guest I have been trying to catch in my net for the last few years. He finally said yes, so let's get to it.

 

[INTERVIEW]

 

[00:03:19] LP: Hello and welcome today's episode. Today I have with me one of the most celebrated practitioners and authors in the data visualization space today, especially when it comes to our favorite data viz tool, Tableau. He is the co-author of The Big Book of Dashboards, and he's not kidding. It is big. Here it is right here. He is a Technical Evangelist at Tableau, a columnist for Information Age, and host of If Data Could Talk. He's inspired thousands of people with technical advice and ideas on how to identify trends in visual analytics and develop their own data discovery skills. He's keynoted and presented at scores of conferences around the world. It's possibly likely you've seen him speak already, so I would love for you to help me welcome our latest and greatest guest, Andy Cotgreave. Hello.

 

[00:04:15] AC: Hello. It is fantastic to be here. Yeah, that's quite an introduction. It just obviously means I must be quite old.

 

[00:04:23] LP: Yeah. You've been around for a while. Okay.

 

[00:04:26] AC: Yeah. It’s the longest I've ever been in one job in my life but it turned out most of my career led me to this place.

 

[00:04:33] LP: Well, that has to be a good sign, right? First of all, I've been a fan of yours for years, Andy. I'm so thrilled you're finally coming on the show. But I have to say as a lifelong Trekkie, I completely fangirled when I saw in your book that you took a photo with Brent Spiner, who famously played a character, an android named Data on Star Trek. 

 

[00:04:58] AC: Yeah. That was amazing. That was –

 

[00:05:01] LP: I mean, that’s like the pinnacle of a data career and a Trekkie obsession like right there.

 

[00:05:06] AC: Yeah. There's one photograph in the book, and it's me with Brian. That was a data conference five years ago, and they paid for him to come over and [inaudible 00:05:14] because his character was Data. He was great. He was amazing. Yeah.

 

[00:05:18] LP: That must have been such a moment. But a lot of people know what you do now. But everyone also loves a great superhero origin story. We would love to hear how you fell into this line of work.

 

[00:05:33] AC: Yeah. Well, I have to go back to when I was 18. So I'm going to try it. We're not going to take the whole show talking about my career. But when I was 18, I left school with a place on an art foundation course. I wanted to go and draw comics. Then I went to the Arctic for the summer. I was extremely lucky to go on an Arctic expedition and spend six weeks looking at good glaciers. So I came back from that. I was like, “I don't want to do art. I want to do geography and look at glaciers.” That was the degree.

 

Then I did a computer science conversion. Then I got a software engineering job as my first graduate job and then eventually became a database administrator. Then I spent a year bumming around New Zealand on a bike, doing journalism, guiding bike holidays, so talking to people, managing things. Then I went into business research and in that job started playing, managing that data as well, an Access database. I love Access. From there, I went and started building my first dashboards in Excel in about 2006. I didn't know they were called dashboards at that time. Then became a data analyst at the University of Oxford, discovered Tableau in November 2007, joined Tableau in September 2011.

 

Now, so the origin story is of interest I think because I look at my job. I'm a data analyst who became an evangelist. To be a data analyst, what are the skills you need? You need technical skills, communication skills, programming skills perhaps, project management, communication, a bit of journalism and storytelling. By some fluke, my entire career actually acquired all those skills. It wasn't by accident. So the question is how did I fall into my job. I kind of fell into my career. I followed my nose and ended up with what is a really, really great job.

 

[00:07:23] LP: So you're like Liam Neeson in Taken where he's like, “I have this unique set of skills, all combined to make someone very unhappy.” But in your case, no, I see what you mean. It really is such a diverse sales skill set that goes into being able to communicate data so well. It's fully right brain and left brain engaged. On top of the skills that I would even throw in there are like mediation and psychology and little hairs of that as well. So wow, what an amazing journey.

 

[00:07:59] AC: You're so right. To people listening who maybe want to go into the field or develop their skills. It's like you don't need that entire portfolio. You can be niche. But I think career advice to anybody, breadth of skills, breadth of inputs of information just are going to be hugely beneficial.

 

[00:08:16] LP: I absolutely agree. I believe in more of the Swiss Army Knife approach to gathering skills, rather than going extremely deep and specialized and because it just gives you a better adaptability to whatever opportunities are going to come your way, right? So I think that's great advice, a great way to start the show. All right, I'm dying to ask you, Andy. What is the biggest mistake that you see people making when they are presenting data? Like what really comes to mind first?

 

[00:08:48] AC: I have to calm myself down now. I’ve spent many decades in meetings, looking at people presenting data, and it was – I was in a meeting about four years ago and I realized the presenter was talking about the chart, and the chart on the screen was actually quite a complicated dashboard. They were stuck at that podium, and the slide have 10, 15 things on it. I didn't know what to look at. They went, “As you can see, X is bigger than Y, and Y is lower than Zed. And because of this category, that's happened.”

 

It was the epiphany. I was like, “I literally have no idea what I'm looking at.” I have – You're telling me about things that are on the screen but you're not actually indicating visually where I should look. I actually – That presentation was an hour-long. I just spent the entire hour writing notes about what was – I was like, “Well, hand on. These are all the things that are obviously wrong.” It was an amazing moment. So I think having gone from that meeting and thought deeply about data and presentation, there's many more mistakes right?

 

But I think the cry is if you've got something on the screen and you say the words, “Look at this bit,” and then you don't tell me what that bit is with some sort of indicator, I'm like, “Well, I've got no idea what I'm looking at.” So, I mean, I can feel myself getting angry there.

 

[00:10:17] LP: I'm so sorry. Don't hook out on me, okay? I don’t have a backup plan for that. No, I completely understand. There are a lot of strange cultural habits we've adopted into our presentation scripts, like when we go to our next slide and we don't know what it is because either we're not using Presenter View or we haven't prepared in advance, and people say, “Oh, I put this in here because.” These are like the kiss of death words to me. Why are you justifying why you put something in here to us, and shouldn't it come across as more intuitive?

 

[00:10:58] AC: Yeah. I'll tell you the other one. The slide comes up and they say, “Oh, it looked a bit bigger on my laptop screen.” I'm like, “What the – I’m not looking – I’m not sat at your laptop. I’m sat at the back of a big room. Did you not think?” Because it just shows –

 

[00:11:14] LP: It’s all knowledge, right?

 

[00:11:16] AC: They haven't kind of paid that last bit of attention to think, “Well, I'm delivering information,” right? Everybody looking at that has no idea. I don’t know. I just see it all the time. You just think, “But if you care enough, either turn the slide off or make the slide work,” right? Do it without a slide. It’d be much better in most situations.

 

[00:11:36] LP: Yeah. I get caught between asking if it's a matter of caring or just knowing because a lot of us grew up eating unhealthy food in some way and then it takes a health crisis of some kind to be like, “Wait, what's going wrong here?” But we don't know, right? So I think that just there's just so much lack of awareness that there is a certain way to go about things that engages an audience. To your point, I actually want to touch on something you said. I have this philosophical debate I'm always running in my head. I have my own position but I'd love to know yours. How do you feel about practitioners presenting dashboards, like just throwing a dashboard up on a screen during a live meeting, sometimes in lieu of a linear presentation deck?

 

[00:12:29] AC: Dashboards are designed for small screen or even a cell phone, a smartphone these days. They're designed for consumption when the individual is – Their eyeballs are close to their screen. The user of the dashboard kind of is in control of the flow and moving that mess around, right? A dashboard is fundamentally not designed for a big screen. First, you have to take the steps to ensure your audience know what you're talking about.

 

Imagine we've got a dashboard with three KPIs at the top and, I don’t know, four charts, right? If I go, “Okay, Lea. Here's the dashboard. The KPI for podcast listeners is going down this month.” I’m like, “Okay.” At this point, I as the speaker have to draw your attention to that part of the dashboard, right? Now, the thing I recommend, everybody should have this piece of kit on [inaudible 00:13:19] now, which is the Logitech Spotlight. I don’t get commissioned. Yeah. I do not get commissioned for selling these, but this is a clicker that will put a spotlight on your screen. It's just a brilliant presenting tool and even better in lockdown and remote meetings, just genius piece of kit because that means I can do any presentation. I can bring up any spontaneous visualization image and I can instantly spotlight.

 

These beat laser pointers are awful because to use a laser pointer, you have to turn around often on the projector screen. Sometimes, they don't appear. If you've got two or three TVs behind you in the meeting rooms, you're only going to choose one to point out, and so that's going to disappoint the other audience. You can show dashboards but you’ve got to appreciate nobody knows what the heck they're looking at, right? So it’s about taking people's eyes to the part of the dashboard that you are talking about and zooming in if you can. Yeah, like that. So, yeah, be careful with the dashboards on screens.

 

[00:14:19] LP: Yeah. I want to highlight something you said that's so important, and this goes for dashboards or slides, charts, whatever. It’s not just about showing something. It's focusing their attention because we have squirrel brains. The second there's too much to look at where our attention isn't focused, you've lost any control over where everyone is looking. This is why I'm in 100% agreement with you. Not only for me is a dashboard too much and not meant for alarm screen, but it's really meant for self-consumption. If it's constructed in an effective way, it's designed to empower a lay user, not business side user, to understand the vital systems of the business like a car and make very basic decisions on their own and even prompt more questions that warrant deeper analysis.

 

But when you're putting all of that in front of a person and then you start talking, it's like I want to point out like you know they're not listening to you, right? They’re trying to take the whole thing in at once.

 

[00:15:43] AC: Sometimes I bang on about this at work so that people will listen to me. So, hi, everybody listening. Now, your audience is engaged.

 

[00:15:49] LP: I’m sure someone in here will listen to you.

 

[00:15:50] AC: Yeah. Now, your audience is engaged in this. But I sometimes think people, “Oh, yeah. Andy, why? Why is this important?” I think why are you standing in front of these people? You are going to try and inform them about something, share some insight, and persuade them to make a change, right? If you're not doing those things, I mean, I don't know why you’re standing in front of people. So therefore, you should care that what people are seeing matches the passion that hopefully you're bringing to that presentation.

 

I often say, imagine an Apple developer keynote and Steve Jobs putting a crappy chart on the screen. I mean, it just wouldn't happen. That would be hilarious to see it, right?

 

[00:16:30] LP: There would be a major intervention.

 

[00:16:32] AC: Yeah, absolutely. Because he knew that in order to sell Apple, sell the brand, sell the change, it’s like the visuals have to match the words. Anything else is, as you say, you're making your audience turn off, just be distracted, so yes.

 

[00:16:50] LP: I agree. I want to I want to give some love to the practitioners, listeners, presenters. I don't think people realize how important they are as presenter. They kind of come in as the sort of delivery mechanism for the deck. The deck is the important part, but I think even a strong enough presentation, if it's just given by a person with a really compelling way of storytelling and a command and a presence, that is going to be more powerful. Just that we use visuals to more powerfully communicate, but you're the star. You're the reason why. You have the expertise they're coming for and all of that. So I just want to lock that in for people. I don't think they understand how vital they are to the equation.

 

[00:17:40] AC: Yeah, absolutely. I was lucky to get presentation training in my first graduate job over 2020. It’s a quarter of a century ago.

 

[00:17:50] LP: Last year.

 

[00:17:54] AC: That training was so important. I feel like I've got to the stage now. I can turn on the presentation voice and understand when to bring the voice down and pause before going back up again. You can see it in the audience that when you do it properly and you do it well, it’s like, yeah, I could be talking about anything. I could be talking about coffee cups, right? But it takes – Everyone starts somewhere, and that just takes practice, practice, practice, and developing that muscle.

 

[00:18:25] LP: Practice, guidance, knowledge. Absolutely.

 

[00:18:28] AC: Yeah, and feedback. Lots of feedback.

 

[00:18:29] LP: Yeah, and feedback.

 

[00:18:30] AC: Lots of feedback.

 

[00:18:32] LP: Absolutely. So there's another habit that I see, and I'd love your take on this too, is when people copy and paste visuals right out of an analytics tool like Google Analytics right into a live presentation. What do you think about this already intuiting?

 

[00:18:51] AC: Well, it's slightly similar to the dashboard thing, right? But imprinted on my memory that somebody had copy and pasted a chart into the slide and then said, “Oh, this chart looks a bit smaller and look bigger on my laptop.” But as you can see, our sales targets across from here, across Europe are bigger next year than they are this year. It's like I can't see that. I took that person’s slide deck and I started a stopwatch and I was like, “How long does it take actually take me to decipher what it was the person claimed this chart showed?” It took me 90 seconds, right? It was a bar chart with some circles on it, so it was quite a simple chart, but it still takes time to pass.

 

Then so the two of us worked on it, and it was like how can we design this chart, so we it can be understandable in seconds? What are the changes you make? When you copy and paste something from Google Analytics or Tableau, you're copying and pasting something probably with an 8 or 10-point font. Guess what? Nobody can read that at the back of the room.

 

[00:19:48] LP: That’s right.

 

[00:19:50] AC: You build that chart, so you have justified every pixel on it, right? So you don't need to know that the circle means next year's target and the bars means this is target. But you put it on the screen. Nobody knows what they’re looking at. There's just orange and blue circles. So how do you communicate that? Now, the way we change that chart, we put in a really obvious title, which the title was the question. No, it was the statement actually. It was like, “Our target’s not going good next year.” But we used color encoding in the title as the color legend, so there's blue and circle dots, and there was blue and circle in the title, so to refer to the two things that may, right? It’s just about thinking, “I'm taking this chart. If I copy and paste it, I’m copying and pasting something designed for a small screen. How can I make sure the person at the back of the room knows what that chart says in half the time that it's going to be on the screen for?”

 

That's something I really had a slight epiphany about because I remember reading Seth Godin saying charts – He was talking about Charles Bernard’s chart, the famous Napoleon's March chart, which is on my book, for those of you watching. He’s like, “This is a [inaudible 00:20:58] chart.” Charts should be digestible in a matter of seconds. I'm like, “No, that's absolutely 100% wrong.” You can show a very complicated chart on the screen. But you have to reveal it, explain it, and build up the story, and bring the user along step by step so that they can get the insight from it, right?

 

I’ve done that with Bernard’s chart. It takes about three to four minutes to describe it fully. Then I share the pie chart version of it, which can be understood in a matter of seconds about how many soldiers died. So, yeah, I can't even remember what the question was, Lea, but that was – You got me in full roundabout there.

 

[00:21:37] LP: No, about pulling data visuals from tools. You're making an interesting point. I've heard different opinions and I've had different experiences with presenting complex charts to a lay audience where it's really like walking a high wire. You can so thrill them with the novelty and also some deeper insight they are not used to seeing. You can also just fall, plummet to confusion. For example, my audience may be aware of this, but I'm publishing my first book this year, following your footsteps.

 

[00:22:11] AC: Congratulations.

 

[00:22:13] LP: Thank you. One of the techniques I talked about is for when I give kind of a list of very effective, nonstandard charts. They're not terribly complex but they're not something someone's going to be fluent in. I give a technique where I'm literally showing one piece of this chart at a time, walking someone through a tutorial of that chart, rather than displaying the whole thing at once. I did this once with a dumbbell dot plot with voice of customer data that it was a very complex ask. I was like, “I don't know how we're going to do this.” That was perfect. But luckily, the way we stepped through it, it made everything so clear, and we were able to backtrack if needed, so people didn't get lost. What's your perspective on training people on charts essentially?

 

[00:23:06] AC: Well, I think really important. One of the things I do in some prep when I'm presenting about presenting data is I show a slide with a really complicated scatterplot on it. It's got 180 marks. There's a bunch of sizes, loads of different colors, no title. I'm like, “Is this too complicated for presentation?” Yes, Andy. It is. Then it’s like, “Okay. Well, let's watch two minutes of Hans Rosling.” Then we played two minutes of Hans Rosling’s 2006 TED Talk, essentially. So the scatterplot I show is the scatterplot he shows.

 

So if anybody's not seeing this title, just do go and watch it because it is absolutely. He brings 180-dot scatterplot to life through animation and through his presentation, his movement, his language, his humor, his drama. It's just an absolute master class. It takes him a bunch of time to explain the chart and then a bunch of time to narrate the story. You're like, “Well, I've just learned something about the world.” So you can do it. Again, the reveal stuff is really, really clever. I want to ask you a question. When you do a reveal, do you start with – What do you do first? Do you do the axes first or the marks first?

 

[00:24:17] LP: Definitely the axes because I'm trying to explain. I'm trying to create a playing field. It's like if you're explaining the rules of a sport. I find it easier to explain how the field is set up and what the goals are, the dimensions, and then actually put the data, which actually creates anticipation because people are like, “Oh. Now that I get this, what's the result? What's the result now?” I don’t know. It’s funny.

 

[00:24:40] AC: No. So have you come across a website called slowrevealgraphs.com?

 

[00:24:45] LP: No. I don’t know how but –

 

[00:24:47] AC: Right. It’s run by a math teacher in the US, and what she does is she completely inverts it, and it's – I’ve not tried this thing, but she shows here all the bars, right? The axes are there, but there's no labels, no title. So there's one bar that's bigger than another, for example. It's like, “Hey, everybody. What does this mean?” They’re doing this to kids, but you could do this to grownups. The kids are going, “Well, there's one thing that's bigger than another,” da, da, da, da. Then she’ll show one axis without X, just the numbers. They’d be like, “Oh, it's a billion compared to a million.” It’s like, “Oh, something is – Is that money?” So she completely does it the other way around.

 

Finally, I mean, the example I remember is spending on police departments across the United States. You just got these crazy outliers. That method is so compelling because it forces you to be like, “What am I seeing in the data?” So you're talking about this. Well, one thing’s over with outlining, and that means all. There's a long tail. Then piece by piece, you’re like, “Wow, it's police spending.” It's really good. So, yeah, go check that out.

 

[00:25:57] LP: What I love about this is every time I have a perspective on how I do things, I get to see the benefits of doing it a completely different way, where I understand that now the beauty of the reveal is withholding the context. So create your opening a story loop, where it's like a story starts, and you're desperate to finish it. That's what the power of story loops are. Works great for like email subject lines and things like that. I use it for observations on chart titles by starting a story loop, finishing with an ellipsis, and I leave a space open for a future graph, which I bring in and I close the story loop. But that is so interesting that you can create the story loop by removing the actual context for the data.

 

[00:26:44] AC: That's brilliant. You just articulated it in a really, really clever way because the –

 

[00:26:48] LP: Thank you. It’s what I do.

 

[00:26:52] AC: I’m trying to articulate. I’m trying to apply the theory of storytelling to data visualization. It’s been a big trend. You’ve been working on it for years. It’s big in the field.

 

[00:27:00] LP: Right. I’m trying to capitalize on it.

 

[00:27:02] AC: But I’ve never thought about it in that way. It's like yeah, but the slow reveal grass way is you start with a punch line and bring you back, and the more traditional way that most of us do is build the playing field and then give you the data last. Yeah.

 

[00:27:16] LP: That's fantastic. I can't wait to check that out and see how to apply it. You talked about color. You touched on this. It’s one of my favorite subjects in the field of data viz. In your book, you rightfully assert that color should not be used to spice up a dashboard, which is what many people want. It’s not decoration. It's a tool for highlight and emphasis.

 

Now, here is one place I've struggled when it comes to dashboards because when you're taught to tell stories with data in like a linear slide format, you're taught to set all the data to a baseline gray color, which creates like a backdrop for the data. Then you use a standout color like blue or orange to emphasize a particular data point. How does that translate to a dashboard when a dashboard look really grim if it's all gray. Then you have to kind of dynamically use color as the data changes because you're not painting the data story in a dashboard, right? It's this evolving, changing entity.

 

[00:28:22] AC: Yeah. The short answer is some of the most beautiful dashboards that I've seen are just gray and the highlight color.

 

[00:28:28] LP: Okay, fair enough.

 

[00:28:30] AC: I think when you pare back the design, when you pare back the color, it can be really helpful. Now, in the book, the dashboard that is shown on the front cover of the book, that's one of Jeff Shaffer’s dashboard. That's gray and blue. That’s only gray and blue when you look at the one in the book. There's another one in the book by Matt Chambers. It's gray and blue, right? As you interact and change parameters, the blue shading grows or shrinks.

 

The reason that it’s a really good discipline to minimize the colors down as far as you can go is because it creates a creative constraint that really forces you to pass and consider what do they actually need to see, right? When you don't think about that, then you're like, “Well, I'll just throw a color legend on here and a gradient over there.” Well, yeah, maybe that was the right way to go, right? Maybe it was, but it becomes too easy. It's so easy to add color to any visualization than if you start going, “I'm not going to but I have to make it still look good.” Then that's a good system. So really only add color when you have to.

 

I mean, I'm well-known for it depends. It’s there behind me. It says it depends because I can't tell you definitively whether you should use 1 color or 20. But I'd lean to fewer colors. There's always times when you might need lots of colors, but you can do one-color dashboards.

 

[00:29:58] LP: Yeah. No, I 100% agree. I always feel color should carry meaning. That's what makes it a tool, rather than a decorative element where I once worked on a dashboard where literally every chart and every line and every marker on the lines were all different colors. It looked like rainbow sherbert. It was very interesting. None – There was absolutely no meaning to those colors. So I guess even in a dashboard, if you're working with related metrics, they can be shades of a similar color, and then you're using color to separate out segments or – But the point I think you're saying is use it to communicate something, right?

 

[00:30:46] AC: Yeah, absolutely. Otherwise, it's a rainbow.

 

[00:30:51] LP: Rainbow sherbert. What are some of the dashboards that impress you? Because, I mean, look, you know a thing or two about this. So I'm always curious like what makes you stand up and go, “Wow, they nailed it.”

 

[00:31:06] AC: In the world of data visualization, there is an operational business monitoring dashboard, right? That's what the book is about. That's a big chunk of it. Then at the other end, you've got absolute Ballengee data art, I mean, to the point that it’s just a piece of art, right? It's just not even really showing insight but it's data visualized as for art. Then along the way, you've got a spectrum that takes in infographics, jazzy Tableau public style, shiny things, all the way to art. They’re all wonderful. They’re all part of this brilliant tapestry that is in this field [inaudible 00:31:41]. They all have appropriate places.

 

For a dashboard, you are trying to get the most insight into the right people's eyes via the medium that is most relevant for them, desktop, mobile, paper, so that they can get the insight and know what actions to take in the shortest time possible, right? We didn't really define what a dashboard is in our book. I mean, we did but we just like something to monitor understanding. What's more important is that a dashboard should get the right information to the right audience in the right method so that they can get the insight as quick as possible, right?

 

Therefore, you've got to be super-duper careful with the shiny things you put in your dashboards. It’s like it's boring bar charts, lovely line charts, simple – As you said, color for meaning and nothing else. Interaction is super simplified. Titles have to be crisp. If you've got interactivity, is it obvious how to interact with your dashboard? One of the things with Tableau or with all dashboards is as a Tableau user, I can build really, really sophisticated interactivity, which is hidden because you have to click on a mark or you have to click on interact. But then if you come to my dashboard and you've never seen it before, well, how do you know you can do that, right?

 

The tricks to great dashboards are those that are crisp and pure and austere simple grid layouts. Now, so the question was are there any that I've seen that are amazing? Yeah, I've seen many amazing dashboards. Most of them are kind of kept behind customers’ doors, customers’ things. But I think one thing I could recommend to watching listeners is Mark Bradbourne who works at Tableau, he started a project called Real World Fake Data. So he was saying those projects like makeover Monday, sports is Sunday, all these brilliant skill growth projects. But they lean towards shiny, attractive visualizations.

 

What we know at Tableau a lot is customers Look at that and think that's how the business dashboard should look. It’s like, “No, no, no, no, no.” So what Real World Fake Data does, Mark is creating fake data sets around an industry. He’s like, “Well, okay. Let's not do make over Monday. Let's build a business dashboard using this data.” It is brilliant. It's absolutely brilliant. It’s – I mean, we could now use this in our selling cycles because you can go, “Look, here's loads of business dashboards. Here's the versatility of Tableau on a single data set.” They're all great KPIs, big ass numbers. They're simple and plain, so it's great. No one dashboard but lots of real world [inaudible 00:34:30].

 

[00:34:32] LP: No, that's great, and it's great for to find a place where people can get inspired and test things out. I like what you're saying is that you're seeing a general characteristic for effective dashboards. They're intentional. They're well-constructed, austere. I like that word. They're well-organized. They're not out to impress. They're out to inform, right?

 

[00:34:56] AC: Yeah.

 

[00:34:59] LP: We interrupt this interview content for a brief message brought to you by me.

 

[BREAK]

 

[00:35:04] LP: There's never been a more important time for presenting data accurately, confidently, and impactfully to your stakeholders and clients. If you're a leader or agency owner whose team is responsible for driving database decisions and keeping satisfied clients, and if you've tried other data storytelling instructors in the past who just missed the mark, I get it. With over seven years of experience training data and digital practitioners in the unique art and science of presenting data, who knows the unique challenges of this field having been at it myself for 12 years, I'm ready to help. I offer both live, virtual, and online course offerings with ongoing learning support options that suit your organization's specific needs. Visit leapica.com/workshops to schedule your strategy session with me, and we'll get started on your custom training solution today. That's leapica.com/workshops.

 

[INTERVIEW CONTINUED]

 

[00:36:06] LP: There is the question of the dashboard consumer. Because in working with a lot of data practitioners in this field, I often find that they are getting requests or demands for dashboards looking exciting and snazzy. Do you find that consumers ever negatively influence that final product because they're trying to give them what they want not necessarily so, like if a VP wants 12 pies on one screen and all of their 10 brand colors incorporated? It’s more like a poster. That's not the right word, but it's less a data product and more like a placard of the brand in some way.

 

[00:36:51] AC: Yeah. It’s such a hard thing. You've got several issues going on there. People want shiny things, right? I think actually here’s a great book, The Design of Everyday Things by Don Norman. Classic in design since it’s a design engineering book but –

 

[00:37:07] LP: That sounds like my favorite book.

 

[00:37:09] AC: Yeah. It's really good. He talks about how important it is to make things look good. In fact, I'm going to read a quote because I love this. “Engineers and other logical people tend to dismiss the visceral response, something looks good, as irrelevant. Engineers are proud of the inherent quality of the work and dismayed when inferior products sell better, just because they look better.” I find that joke hilarious, partly because it's quite an insult to most engineers. I think [inaudible 00:37:35] 20 years ago. I think –

 

[00:37:37] LP: I’m a daughter of engineers and I a little bit resonate with that.

 

[00:37:42] AC: [inaudible 00:37:42]. But you think Apple engineers appreciate what a good design is, right? I think in 2021 that's slightly outdated, but you do have to make something viscerally appealing, and it has to look good, right? But it doesn't have to be your corporate purple.

 

Now, the other thing is that people say, “I want to talk pies because I want to be able to compare it to dinner.” Until they've seen a prototypes of what they want and what could – Prototypes and things that might be slightly different to what they look like. They're trying to articulate something they may not be an expert in articulating. Even I, if I try and describe the dashboard I want to monitor the thing I need to mark wanted to tomorrow, my verbal description is going to be way off the mark, right?

 

[inaudible 00:38:26] to compare the 12 months of the year. So then we'll show you the 12 pies. You go, “Okay, Mr. CFO or Mrs. CFO. Does this answer your question?” No. I think there's a big part that people think they want something until they actually see it. I mean, you probably know. You still got to meet the resistance where I want the 12 pies, goddamnit. At which point you're like, “Okay. Well, you're the boss. I can give you that.” But my recommendation to get around this is why do you want the 12 pies. Because then they’ll say, “I want to do X and Y.” Why do you want to do X and Y? Because of Y and Zed. Why do you want to do that? As you’re doing that five whys process, you'll eventually get to what they really want, and that's what you build.

 

[00:39:13] LP: Absolutely. I think curiosity is one of the most underutilized tool in this field of helping. We often take what stakeholders ask for at face value because there could be power dynamics. We're afraid to upset the applecart and all of that. But sometimes, I found that I actually began to establish myself as with higher credibility and trustworthiness by just putting the brakes on for a second before saying, “Okay. Yes, master.” Then saying, “No. Why? Help me understand.” Just because the way I would approach it is this way, and then sometimes I'll even present them with two different versions, and I'll quiz them and say, “Hey, which one answers your question faster or what do you think?”

 

Sometimes, that can be the shift that they need. Sometimes, to your point, you have to just detach and say, “Okay, this is what they want. However, I'm going to have this little back pocket dashboard that I'm going to use.” That's going to communicate, and you just make sure the insights are super clear. That's a tough one.

 

[00:40:21] AC: Yeah. Absolutely. It is.

 

[00:40:23] LP: One of the things I love about your book in particular, and my eight-year-old son is becoming a bit of a data viz buff himself, which is very exciting. So he loves to look at all the charts in these books but he loves the scaredy cat. I think that's what you guys have like renamed it to.

 

[00:40:43] AC: Yeah.

 

[00:40:43] LP: Which is this like – I think the scaredy cat is actually named after a line chart that has gone like – Is way too spiky and is kind of hard to read. If I'm not mistaken, that was the origin. Or I'm totally wrong.

 

[00:40:59] AC: I'm not sure that was but I don't want to correct you because that sounds like a better origin story. I’m like, “So let's go with that. Yes, that's right. That’s exactly it.”

 

[00:41:07] LP: Perfect, wonderful. Yeah. But it acts like a signpost in your book for denoting visualizations that you encourage people to rethink or avoid. So what are some of the visualizations that you can think of? Pie can be an obvious one, but what do you think some people are using and not knowing they're kind of setting themselves up for a trap?

 

[00:41:30] AC: Yeah. Well, I think we've touched on some of it. Too much color is obviously an interesting one but controlling your axes, making truncating axes when they shouldn't be. I mean, even truncating an axis when it could be truncated, that's still a conscious decision you have to make because you're going to tell a different story if you truncate an axis. Depending on the story you want to take, that would be an issue. Too many encodings on a single visualization, a pre-attentive attribute. We can tell different shapes, we can tell different sizes, we can tell different colors, we can tell – Yeah. But if I build you a scatterplot with different shapes, colors, and sizes, no, they don't work together. So having that appreciation of how pre-attentive attributes work together or don't work together is really useful.

 

Then really just charts that – Dual axis charts. That's another one. Dual axis charts can be awesome, but they can also be really confusing. So if you imagine a dual axis chart with one line going up and one line going down, we are fixated on that crossover point, right? But if your scales are different, that crossover point might be arbitrary. So it’s things like that. It's all about drawing the eye to the insight you want the audience to get to. Beyond that, it can get a bit scaredy cat.

 

[00:42:51] LP: It’s funny. I teach the same exact thing around dual axis charts. Even right now, I feel like I can hear a collective global groan of everyone listening going, “No, don't take that away.” But people don't realize Office, Excel, or whatever invented it. They just gave you a way to put two different measures on the same chart with different scales and said, “Hey, do that.” No one's really thinking about what's actually happening mechanically from a visual perspective, so totally –

 

[00:43:23] AC: Yeah. I mean, they can work. But, again, it's all these things. I think that going back to it depends, my path, again, talking about career. The first time I got into data visualization, extremepresentations.com do the thing called the chart user. It was a simple sort of spider graph going, “Okay, what questions do you have?” Then at the outside it was 16 different chart types to answer those 16 different kinds of questions. Blew my mind, absolutely blew my mind. Because I was like, “What? Does these char things have this structure?” It was the first time I'd ever realized there was some sort of language and processors. It was amazing. So that was on my cubicle wall for years.

 

Then over time, you begin to realize, “Well, hang on. A lot of those were kind of arbitrary choices, and I might choose this one over here.” But from a progression of career, I think it's so important to understand the rules. Then at some point, you will realize the rules are merely guidelines. At some point, the only thing you can ever say is it depends.

 

[00:44:28] LP: Yeah, I love that you grow to know less. The more you know, the less you know.

 

[00:44:33] AC: Oh, God. Absolutely. I mean, I'm far less certain about any answer to any question 14 years, 15 years into this than I was after 3 years. That's totally true.

 

[00:44:42] LP: Oh, my gosh. You're so right. I knew everything when I was 25, everything. Now, nothing. I resonate with what you're saying around the encodings. One thing I loved in a Scott Berinato’s book. He's at the Harvard Business Review. He wrote Good Charts. I love that book. Is when he talked about color in particular, he said use color only as the lowest common denominator of distinguishing something. So oftentimes, in bar charts, we’ll color the different bars, just because they're different members of that chart. We’ll even shade – We overlay a lot of signals to say this is different from that but we don't realize we only need to use one encoding to do that right. That really resonates.

 

Now, on dashboards, let's go back to dashboards. So I see still radial gauges and thermometers. I'm curious about your perspective on some of these very dashboard-centric visualizations.

 

[00:45:53] AC: No. I don't like gauges.

 

[00:45:56] LP: Say no more.

 

[00:45:56] AC: Are you familiar with the election coverage concept of a swingometer? Do they have those in the US? I’m talking about the swing from one party to another.

 

[00:46:06] LP: I haven't seen anything like that but I have to look it up.

 

[00:46:08] AC: No? Okay. So, in the UK, all our elections have been coming with a swingometer going – Well, I mean, it's a hangover from the two-party system was your labors and conservative, and this constituency has swung 6% to labor or X percent to the Tories, right? Great. But I went into a rage during our last election because the gauge, to make it visually interesting, you need – The gauge is always in the middle. Every time they show a constituency, it's like, okay, 3% swing. It’s like, “Well, that's not very visually interesting.” So then they go 8% swing, and the gauge is most of the way to the horizontal line.

 

But that year, the Tories trounced the Labor. So, it was like 8% swing, 20% swing, and they're all in exactly the same position on the scale. So, you're like – I mean, that's not even a dashboard kind of gauge because at least the dashboard gauge has a consistent scale. But you can't tell what the scale is. The ends of the scales are mostly arbitrary, and they take up a huge amount of space. Now, that’s not bad on a TV screen. They've got to fill the space with something visually appealing. It’s a really visually strong image. But on a dashboard, you want to just make it a line with a progress indicator, and then you've got loads more space to show something else.

 

[00:47:21] LP: A bullet?

 

[00:47:23] AC: Yeah. So don’t do gauges. I don't even think it depends. I just think don't do gauges. I mean, even more than pie chart. You can use a pie chart every now and then but not gauges.

 

[00:47:34] LP: Yeah. Well, the Andy has spoken. Don't do gauges. Yeah. So in lieu of something like that, if someone wants to show performance but then again some kind of target, is a bullet graph something they can use?

 

[00:47:50] AC: Well, a bullet graph is great. The challenge you have, so here's the challenge. So, Stephen Few invented bullet charts, what, 2005, 2010? Bullet charts are extremely space-efficient, very well visually encoded means of showing progress to a goal. Problem is most people don't have a clue how to read them, right? They look at them, “I don't know what I'm seeing.” So, you have a challenge. Again, that's the tension of data visualization. You've got to know your audience. Can you educate them to read bullet charts or can you not? In which case, do you pull back? I mean, a progress bar. Something that looks like a progress bar is kind of alright generally, again, with caveats. But, yeah, bullet charts are amazing if you know how to read them.

 

[00:48:34] LP: This is why I always say that charts especially are like scalpels. If you go in and you have no surgical training, you can hurt or kill someone. But when you have – Is it the scalpel’s fault? Or is it the skills that were used to wield it, right? So I just had an idea pop in where kind of using your slow reveal method that you mentioned, where you could show a graph with like a list of sales people or sales regions or something, and they each have performance targets or quotas. You're putting the target lines first to just establish because that's information they may already know. They may already know. “Oh, okay. Yeah, I get it. This is what each person's respective goal is.” Then you bring the actual data in, so they're not seeing both markings at the same time, which might confuse them because that's unfamiliar. But you're leveraging that anticipation to start with something familiar.

 

[00:49:35] AC: Yeah, you're right. I like the analogy of a scalpel. All the tools that enable you to do so much, yeah, and without knowing the basics, you can do a lot of damage. The amazing thing is data literacy, it's not hard. It’s really basic information. It's just not really part of the school curriculums, which would be really nice to change.

 

[00:49:57] LP: That's right. If you're listening, colleges, but it's so true. I look through my book, and there's nothing about coding in our Python or incredibly complex database work. It's actually so simple, but there's a lot to learn about it. It's a very cross-disciplinary field, right? Very cool.

 

[00:50:30] LP: All right. So we've arrived at our next segment called The Upgrade, which is a tool, a resource, a book, something that Andy absolutely is loving right now that he thinks our listeners would absolutely love to check out. He can't mention Tableau. That's kind of too obvious. So what do you got for us?

 

[00:50:53] AC: Far too obvious indeed. So I'm going to recommend my favorite data book of the current period. Tim Harford is a famous economist in the UK, presents more or less on BBC, and is the undercover economist at the Financial Times. This book is brilliant. It's almost in the style of How Charts Lie by Alberto Cairo or Factfulness by Hans Rosling. It’s a really, really good take on the whole way of trying to be a knowledgeable consumer of information in the world of social media, disinformation, governance statistics. It's brilliant. It's really, really good. In the US, it's called The Data Detective, which is challenging to try and do a book tour, because you don't know which continent you're on. But anyway, How To Make The World Add Up or Data Detective by Tim Harford, brilliant.

 

[00:51:45] LP: That sounds amazing. We'll have to definitely put that on the list. Since we are mentioning Tableau, is there anything that is really exciting you about that particular tool right now? Because I know many of my listeners use it.

 

[00:52:03] AC: Yeah. Well, I think, we just talked about education. We have an amazing academic program. So any students or professors can get hold of Tableau for free. We've got loads of resources. So I think that programs have been going for a while, but it's really good if you haven't heard of that. That's Tableau for teaching. We launched a free online data literacy course not too long ago. That's great, a really good way to start that and get that foundation.

 

Then on the nerdy level, in the last release, we did a quick level of detail calculations, right? So if you don't know Tableau, that probably doesn't mean much, but it's a way of doing incredibly, well, advanced aggregations of data, which are very simple to say but very hard to program in any tool. But we give you them in just a couple of clicks. So if you think about percent different sales compared to last year but at the national level. I mean, God, I really want to see that on my dashboard. Then your analyst goes, “Oh, holy cow. Percent annually at regional level but we're showing state level information on the dashboard. Oh, my God. That's good. Okay, I’ll be back in three weeks.” We've added a way to really get to that answer really quickly. So that's cool.

 

[00:53:20] LP: So like a really multi-layered filter that isn't just allowing you to look at one slice or another.

 

[00:53:25] AC: Right. Let's get in the weeds because this is fun. It's a calculation, so what is the sum of sales? But the level of detail, what I mean is if my visualization is showing, say, sales by store across the entire map of the world, that's very granular. But you want to compare it at the level of detail of the nation, the national level. So then it's about specifying at what level you want the calculation on independent of what level of granularity you go on the chart.

 

[00:53:56] LP: Oh, my gosh. I could have used that 10 years ago synonymous for so many things.

 

[00:53:59] AC: Honestly, yeah. It’s one of those things. [inaudible 00:54:02] came into Tableau maybe four years ago, and it just opens up the analytical space hugely. Now, we know that much more clickable. So I like –

 

[00:54:14] LP: Well, if you're a Tableau enthusiast, you'll have to check that out when it's available. That's a really great tip. Love that.

 

All right, Andy. This is our final question, so think very hard here, okay? I want you to imagine this very plausible scenario. You're in the final rounds and about to win Terraforming Mars, the board game, at the World Board Game Championship when the lockdown is over. When all of a sudden you trip and fall into a vortex that pulls you back to the moment you're about to deliver your first presentation. Do you remember what you're presenting about? More importantly, what advice would you give to that person?

 

[00:54:58] AC: Wow. Terraforming Mars is a great game. There's going to be an episode of If Data Could Talk either imminently or already released, which is about board games and data visualization with one of the leading board game designers in the world. So we talked about Terraforming Mars. Anyway, my first presentation that I can remember was when I was writing software for primary school children in the UK. One of the pieces of software I wrote was a spreadsheet. I wrote an amazing calculation engine. That was good. I'm very proud of that. But we really encourage kids to do 3D charts using that tool. This is the leading primary software in the UK, so I might have been accidentally responsible for a lot of the children that love 3D pie charts. I apologize right now. I know the damage I did. But I did a presentation for people who were developing tools for that PC.

 

Advice that I would have known is silence is fine. You do not need to fill silence with so, or like, or you know, or um, or uh. Silence actually makes you sound more authoritarian. Or not authoritarian. Authority, right? It only makes you sound more clever. So silence is good, and then practice, practice, practice, practice, practice. One of my superpowers, by the way, I present is that I'm always encouraging people to throw me off and take me on tangents. I love going down a tangent but I can do it because I know my script so well. That if I've gone down a tangent, I know how to get back to the main path. What I found is if you try and do that, when you don't really know your notes, you go down a path. Then you're like, “I have no idea where I was.” So you fumble around trying to get back to your flow. There you go, practice and no soft words, right?

 

[00:56:48] LP: Soft words but filler words, right? To your point, I am a big proponent of practicing. I feel that people who wing it can only do that if they're operating at such a high advanced level. For me, what practicing does is it leads me to a fluency in a language of my content so that if someone does throw me off or take a tangent, you're still speaking in this language that you're fluent in, versus you're asking Google translate to translate everything that you're saying because you actually don't grasp the fundamentals of the language, right?

 

[00:57:25] AC: It's absolutely right. I mean, I could go on about this forever. I mean, [inaudible 00:57:26]. I actually became a magician about eight years ago because I got fascinated by the whole cognitive science of magic and how it overlaps with the cognitive science of data visualization, right? But then becoming a magician made my speaking skills – I mean, just took them to – Level them up. Because if you think I'm going to do a magic trick and I know all the procedures, I need to get all the cards in the right place in order to create the effect that is going to amaze you.

 

What changes with every single performance you do in front of one people, one person, or ten, is the audience. They are all going to react differently. You'll get gobby people. Some people are amazed. Some people who want to interfere, everything, absolutely everything. So you have to learn to be able to control your process but also roll with the punches that the audience gets because you're trying to – The goal is to make it a fun experience for everybody involved. So, yeah, I’ve got into magic because of the neuroscience side of it. But then it became a public speaking skill development, so there you go. Practice, yeah.

 

[00:58:28] LP: Amazing. All right. So becoming a magician, helping speaking. I love it. Well, Andy, I truly enjoyed our conversation. Unfortunately, our time has run out. So please tell the listeners where they can keep up with you.

 

[00:58:42] AC: You can find me on Twitter, acotgreave, A-C-O-T-G-R-E-A-V-E. I’m on LinkedIn, Andy Cotgreave. I have a newsletter, The Sweet Spot Newsletter, where I try and find interesting tangential stories about the world of data. You can find a link to that on my Twitter profile. I'm sure we'll be able to put a link somewhere in the notes.

 

[00:59:01] LP: Perfect. All of the links, all of the resources, books, everything we talked about today will be available on the show notes page for this episode. Andy, it was such an honor as a longtime fan, and I really enjoyed this. It was great to kind of take your lens of experience with dashboards but also show about what can be done with presentations too. So I want to just thank you so much for taking the time to join us and I really hope our paths cross again.

 

[00:59:30] AC: I hope so too. Thanks for having me and congratulations on the new book. I’m excited to see that coming out.

 

[00:59:35] LP: Thank you.

 

[00:59:36] AC: Yeah, you’re welcome.

 

[END OF INTERVIEW]]

 

[00:59:47] LP: Phew, all right. Wow. I hope you guys enjoyed that as much as I did. It's really important for me, especially as a speaker and a trainer and a teacher of sorts that I continue to incorporate all of the facets of the data communication process. Even though my specialty is data presentation and the live meeting, there's so much to be learned from mastering dashboards, and it's such a critical aspect of our role as practitioners of data. So I really hope you guys enjoyed that.

 

To catch all of the links of resources, everything that we mentioned in the episode today, please visit the show notes page at leapica.com/065. I would love if you could leave me a comment or suggestions because I want to hear about the challenges you face when you're presenting information. Feel free to hit me up on LinkedIn, send me a connection request. Little tip, I always respond to the ones that have really nice notes attached to them. So I would love to connect there.

 

Please, please, please, if you like what you've heard and you're not already subscribed to the show, just go to the main show page on your iTunes or your phone and hit that subscribe button. It's on Spotify too. Just hit that subscribe. Then, please, also leave a rating and review because they're so appreciated. They really help affect the rankings of the show, they help make sure I can keep the show going, and they help get the information out to other practitioners like yourself.

 

I'll leave you with today's presentation inspiration, which is by David McCandless of Information Is Beautiful theme. It's a really simple one. It says, “Data is the new oil? No. Data is the new soil.” So here's my take. I love the simplicity of this quote because it really makes you think. What I think it means is that it isn't the numbers and the digits that inspire and motivate. Data is the soil in which ideas and insights are planted to grow into plants of progress. So I'm so honored to be on this journey with you to become the master gardeners of insight and change for your organizations and clients and customers.

 

Don't forget to take my new assessment and find out what's stopping you from getting the glory and recognition and rewards you deserve from presenting data impactfully. Get on over to leapica.com/assessment if you haven't already. That's it for today. Keep rocking that data dashboard dance floor, guys. Stay well, stay safe, and Namaste.

 

[END]

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