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Advanced Data Visualizations, Charts, and Graphs with Jon SchwabishPresent Beyond Measure Ep. 084Jonathan Schwabish is Here to Help You Bring Your Data to Life with Advanced Data VisualizationsEver wonder what chart horizons lie beyond the basic bar, pie, or line graph? Ever heard of bee swarms, waffles, or dumbbells charts? These are not terms commonly associated with the data visualization space, but if you really want to take your graph and chart-making skills to the next level, it will benefit you greatly to become more familiar with them.

Jonathan Schwabish returns to the show to give us a tour of advanced chart options that can work well for business data storytelling and a few exotic graphs that are simply a fascinating and novel way to view data. From choropleth maps to slope charts, tune in to hear it all!

He is an economist at the Urban Institute in Washington, DC. In addition to his research on programs that support low-income communities, he is a writer, teacher, and creator of policy-relevant data visualizations. He is considered a leading voice for clarity and accessibility and how researchers communicate their analyses.

Jon is also the prolific author of Better Presentations, Elevate the Debate, and Data Visualizations in Excel, and today’s episode samples charts from his most excellent read, Better Data Visualizations.

Jon will open your eyes to a whole new world of possibilities in the advanced data visualization space!

In This Episode, You’ll Learn…* The pros and cons of Excel, Tableau, Datawrapper, Flourish, Power BI, and R for data visualizations. * Why a bee swarm chart is one of Jon’s favorites. * The advantages of a waffle chart over a pie chart. * When using a dumbbell dot plot is recommended. * Examples of use cases for Voronoi diagrams and choropleth maps. * The importance of asking for feedback on a graph or chart you have created. * How to overcome your dislike of scatterplots and why the slope chart is Jon’s go-to.

People, Blogs, and Resources Mentioned Bee swarm chart * Waffle chart * Voronoi diagram * Dumbbell dot plot * Choropleth map * Slope chart * Tableau * Datawrapper * Flourish * Power BI * R * Better Presentations * Elevate the Debate * Better Data Visualizations * Data Visualization in Excel* * My free 30-second online assessment to find out and overcome the #1 silent killer of your data presentation success

How to Connect with Jon Schwabish:* Jon’s LinkedIn and Twitter profiles * PolicyViz * Urban Institute Where Lea is Speaking Next:I’d love to meet you, in-person or online! Here are the data storytelling, analytics, digital marketing conferences and events I’ll be speaking at:

There are no upcoming events.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!

Now, I’m going to ask two favors from you:

1. If you’re digging the show, don’t forget to hit the Follow buttons on iTunes or Spotify to never miss an episode.
2. If you liked what you heard, I would love if you could leave me a rating or review in Apple Podcasts. 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, always remember: viz responsibly, my friends.

Namaste,

EPISODE TRANSCRIPT[0:00:01.4] LP: Hello, hello, Lea Pica here. Today’s guest is a data visualization pro who is here to take us through some super-advanced charts and when to use them. Stay tuned to find out who is taking us to school on the Present Beyond Measure Show, episode 84.

[0:00:16.9] 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.

[0:00:44.6] LP: Hello, hello, and welcome to the 84th 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.

Today’s interview is loaded with tips on advanced charts you may have never heard of and graphs that you’ll love to use from a true data visualization expert and esteemed author. So be sure to stay tuned in.

Now, as usual, I’m excited for today’s guest because his contributions to the field of effective data visualization, communication, and presentations skills are unrivaled and he’s super funny to boot. Let’s dive in.

[INTERVIEW]

[0:01:45.3] LP: Hello and welcome. Today’s guest is an economist at the Urban Institute in Washington DC. In addition to his research on programs that support low-income communities, he’s a writer, teacher, and creator of policy-relevant data visualizations and he’s considered a leading voice for clarity and accessibility and how researchers communicate their analyses.

He’s also the author of amazing books including Better Presentations, Better Data Visualizations, Elevate the Debate, and coming soon, Data Visualizations in Excel. I have the Better Data Visualizations right here, one of my favorites, and his contribution to the space is nearly unmatched so I’d love for you to help me welcome today’s guest, Jon Schwabish. Hello.

[0:02:36.0] JS: Hi Lea, thanks, good to see you again, it’s been a while.

[0:02:39.0] LP: I know, it took a while to get this going.

[0:02:40.5] JS: It took a while.

[0:02:42.3] LP: We had a few detours.

[0:02:43.1] JS: Yeah-yeah-yeah.

[0:02:44.1] LP: But it’s great to have you back, as long as you keep publishing new books. I’m just going to keep having you return.

[0:02:50.3] JS: Yeah, I don’t know, this is the fourth one, it could be the final, we’ll see. Someone will drag me somewhere and I go kicking and screaming and then I’ll say, “Okay, yeah, it’ll be fun to write.”

[0:02:59.9] LP: Right. Next is your data memoir, right?

[0:03:05.3] JS: Step one, “When I was four, I made a bar chart.”

[0:03:07.7] LP: Right, exactly. Well, your experience is vast in the space just helping researchers but also spilling over as a major player in the data visualization and presentation space. I’ve been using your information as a digital marketer and data analyst for many, many years.

So I want to jump right in and talk really tech stuff because that’s what the people want. So what I thought I would ask is the first question that I always get in my workshops: what tools do you use for your visualizations? Talk about the tools.

[0:03:45.5] JS: Yeah, tools, always the big question. It’s like, “Oh, you’ve shown me all these graphs and all these good techniques but how do I actually make the thing?” Yeah, I hear this question a lot too. So I mean, there are a lot of tools out there and my view on tools is that they are just tools. You know, you hear a lot of people say, “Oh, you should never use this tool or that tool or that tool because they’re garbage or they don’t do this, they don’t do that.”

My view is, they are just tools. So for example, I spent a lot of my time in Microsoft. I use Excel for probably three-quarters of my visualization, and when I hear people say, “Excel doesn’t make good graphs.” I’m like, “Well, first off, I am making the graph, Excel isn’t doing anything,” you know, “Until the terminators take over, I’m still in charge,” right? But would I use Excel to create an interactive feature on the New York Times website? No, that’s not what it’s good for, right?

But my main toolkit is Excel, particularly, obviously, for static stuff. A lot of the work I do at the Urban Institute, we work in the Microsoft suite for a lot of things for our writing and for our presentation. So Excel makes sense for a lot of that work. I also use Tableau and I’ve been using Tableau a lot more over the last year or so. I’m just trying to get better at it and for no particular reason other than I just kind of like the challenge.

There are some weird things in Tableau. For folks who ever look at my Twitter feed every once in a while, I’ll have some question like, “Why is Tableau doing this weird thing?” And what’s great about Tableau, maybe even more so than a lot of these other tools, is that the community around Tableau is so helpful. It’s extraordinary. You can ask the question on Twitter and you’ll get an answer immediately. You do that for Excel, it’s not the same.

[0:05:19.1] LP: No one wants to raise their hand and be like, “I love Excel.”

[0:05:22.0] JS: I love Excel, right? “Hey, look up, over here.”

[0:05:24.5] LP: Yet we all use it.

[0:05:26.0] JS: Again, I see what Tableau is good for and what it may be not good for, it’s just a tool. A lot of people ask me, “Do I use Power BI?” I personally don’t use Power BI because I spend most of my time on a Mac and Power BI not on a Mac yet. So once Microsoft and Apple can get over whatever and it comes out, I’ll probably use Power BI. I used it early on when I was able to run parallels more on my computer and be able to have both operating systems.

I do like it because it taps into the Microsoft systems so much more. I mean, it’s just natively into Microsoft so, if you’re an Excel person and you want to make dashboards, Power BI is great. I will say that I just don’t think that the Power BI dashboards, as a lot of what I’ve seen and people do amazing stuff with it, but sort of the general stuff, I don’t think it looks as glossy and is as polished as Tableau. But I don’t know if that matters.

I always think about like, if you think of all the dashboards that are created in any tool, what portion of them are these bespoke, custom, awesome looking things but really don’t help you do what a dashboard should. The goal is to explore the data and the rest of those that are sort of your more standard dashboards. How many of them are not public and people are just using them in their day-to-day work and sitting around the office?

And for those, who cares if it’s shiny and glossy and looks polished? The point is, for you and me Lea, to dive into data so like, who cares how, right? So anyway, back to your core question. So I use Excel, Tableau, I use a lot of Datawrapper and we’ve been using more of Datawrapper at Urban now. Datawrapper is, for those who don’t know, a browser-based data visualization tool. It has a very robust opportunity to use it that’s free. So you don’t have to pay for it, you can do a lot with just a free version. Basically, you can use every visualization they have a library you can use for free. The only thing you can’t do is build in your custom default templates in the tool but fine, okay.

It’s a browser-based tool so it has its own downsides. So you wouldn’t want to put social security numbers into Datawrapper, right? And it’s a limited library and at least for me, I can’t really hack into the HTML code but I do like it. I do like that tool. I also use Flourish, which is a similar kind of tool and it’s more focused on animations. I do like Flourish for some sort of more different types of graphs. So I’ll use Flourish when I want to make a bee swarm chart or some other charts that are not necessarily in that sort of standard graph type.

Okay, so I’ve mentioned Excel, Tableau, Datawrapper, Flourish, and the last one I use is R. So I use the R programming language primarily, when I’m making maps and when I’m doing small multiples. So small multiples of course, we have these smaller multiple charts. We’re not packing everything into one graph and in R it’s just so easy to make small multiples. It’s one extra line of code, you do facet wrap, and bam, your line chart that had 10 lines in it is now 10 separate graphs.

[0:08:28.8] LP: Interesting, wow.

[0:08:30.6] JS: So I will say personally that I am a mediocre R programmer but I also can’t – I mean, I have not learned, I’ll be positive here, I have not learned how to do the statistical part of R, so I don’t run regressions or clean data. I use tools that I’ve been using for a long time in my career, Stata and SAS to do the data cleaning and analyzing and then I’ll package it up and bring it over to R to do the data vis.

So again, I use a lot of different tools and the book that you just held up, the Better Data Visualizations book, I use all five of those tools, plus a bunch more. Like you know, I mean –

[0:09:06.4] LP: Yeah, I saw one in there, I was like, “There’s no way.”

[0:09:12.4] JS: Yeah, and I would say most of the charts in that book can be created in Excel. The ones where you end up in trouble are maps, excel is just not great at maps, and a lot of stuff that’s sort of curvy or swirly because Excel is just not very good at curves.

[0:09:31.4] LP: It’s very angular.

[0:09:31.8] JS: Yeah, exactly right. It’s very angular. So you can work in lines and you need lines of sort of a non-trivial length and you can kind of do almost anything in Excel but if you think about the border of your country or your state that’s jagged or irregular, Excel is not going to be good at that because a curve is infinitesimally small lines stacked together and Excel is just not really good at that. So –

[0:09:55.5] LP: That’s interesting.

[0:09:56.4] JS: Yeah, but for most of the other stuff, it’s great. So I just use a combination of tools and it really does vary on what I need to do and where it’s going to live and does it need to be responsive or does an image work? You know, just a straight-up image? And do I need people to explore the data so then I’m in an interactive world? Am I just telling a story, just making a point? Yeah, so I think there are a lot of tools out there that people should explore and should use because there is no tool that rules them all, right? There’s no Lord of the Rings.

[0:10:30.5] LP: Right.

[0:10:30.9] JS: Now, other people would disagree. Lots of people get into their little camps of, “My tool’s the best,” and I’m just not there. So come back to me in a couple of years and maybe I’ll be into the Tableau world so deep that I’ll be like, “You should use Tableau,” but I don’t think I’ll be there.

[0:10:45.7] LP: You know, what I really appreciate about your answer is it depends, which for me, is the answer. Ideally, for any of the questions that I always get like, “What’s your favorite tool? What’s your favorite chart? What’s your favorite platform? What’s the right thing?” and I’m like, “No right, no wrong.”

[0:11:01.2] JS: Yeah, no right, no wrong.

[0:11:02.0] LP: You have to look at the specific situation, the environment it’s being consumed in, like you said, “Is it interactive?” That’s going to change. I know what you mean where with Tableau, I’ve been experimenting for quite a long time, trying to do an analysis of something called the Bechdel test, which is a test criteria to measure whether something Hollywood puts out, like a movie or TV show, meets the criteria for being women empowered. Not even women-centric but equality.

[0:11:33.3] JS: Yeah, right, equality, yeah.

[0:11:35.7] LP: And I found a really interesting use for Tableau which one day I’m going to release it, I’m excited, where I did a timeline of the past fail rating of different studios over time, starting from the thirties when the data begins and it was able to show me a very curvy, like you said, line that expanded in width or shortened depending on their percentage success rates. So you could see wider areas of the line are good years, thinner areas not so good, depending on how many movies were released that year. So you just have to look at the various strengths of each.

[0:12:17.9] JS: The way I think of these different tools and some of them have a philosophy behind them, right? So Excel, for example, it works in lines, bars, and circles and they need to be a non-trivial size, right? And then it works in a 2D X-Y space. That’s sort of the underlying philosophy and you could sort of describe a philosophy in lots of different ways.

Like, R for example, works in a layering philosophy. Let’s say you wanted to make a world map where the countries are colored by different, you know, they’re colored from a light blue to a dark blue, and then for whatever reason, you want to add bubbles on top of it, circles of different sizes. That’s literally just one extra line of code because it’s just taking the map, layering on the colors, and then layering on the bubbles. So the GG plot package in R is really layering.

And then Tableau, there are two ways at least that I think about it. One is sort of what it calls measures and dimensions. So it’s things you group and then things you count and then the other piece I think is the biggest barrier for people just learning Tableau is that it generally, not always but generally likes its data to be in a long or a tall format as supposed to wide and if you come into data or data visualization, you’d probably start with excel or Google sheets and we tend to work in kind of a wide format.

So let’s say you have countries and we’re going to have two variables for countries. We’re going to have GDP and we’ll have life expectancy. So in a wide data set, you would have in your first column, all of your countries. Let’s say, there are 230 some odd countries down that first column. In the second column, you’d have GDP, some number, and then in the third column, you’d have life expectancy, some number. And that, for most of us, is just instinctual or it’s just, you know, “Okay, I can put those into two different graphs and good to go.”

In Tableau, generally speaking, it prefers to take that data and not have it as three columns in the way that I just described it but longer, so that you would have three columns but they’d be different. The first column would be the countries and they would repeat. So you would have, instead of 200 rows, you’d have 400 rows. The second column would be the name of the data field, so you’d have 200 rows with the name GDP and 200 rows with the word life expectancy, and then the third column would be the data value. So all the way down.

And that way, when you bring in Tableau, you have the thing that groups it which is the two categories in your countries, and then the data value, and once you get that more data-based structure as opposed to a spreadsheet structure, Tableau starts to fall into place a little bit more. So each of these tools has their own kind of underlying philosophy and I think once you sort of get and understand the philosophy and then like the challenge of course is like, make it instinctual as you’re working with the tool, then you can really start to build some things and become more fluent in pushing the boundaries of what you can do with it.

Like you said Lea, having a line that curves and then changing the width of it in Tableau, it’s not really that difficult, once you understand how it works and the different markers and that sort of thing. So yeah, it’s just interesting. I mean, again, they each have their weirdnesses, right? But I don’t think any one tool is inherently better than another tool, although many listening will say, “No, my tool is best.”

[0:15:35.1] LP: Pitchfork.

[0:15:35.1] JS: Yeah, right, exactly, yeah.

[0:15:36.5] LP: No, I like being the Switzerland of –

[0:15:40.1] JS: Yeah, and I’m fine with it. I’m fine with it. And then, your listeners work in different places and work with different types of people and for me, my full-time job is in the nonprofit sector so my avatar of the people that I’m generally working with is a data person in a nonprofit organization that has six people or 12 people and that data person, generally in my experience, has been thrust into that position because they’re the person that has some scale or some affinity or some skill there but they’re sort of thrust into this position.

And that nonprofit of six people, they might not have money to buy Tableau at USD1,500 a license, right? Or they don’t have big data, so they don’t really need, you know, or they’re collecting data on a quarterly basis and that’s it and it’s pretty limited so, if that’s your case – I mean, they all have excel, they all have Microsoft so maybe you don’t need these other things. And maybe they’re always printing out their briefing books and so they don’t need stuff online.

As you said, it depends and it doesn’t just depend on your skillset and your affinity for using a tool. It depends on your audience and what they can do and part of what I try to do is just empower groups that I work with to say, “I can show you how to make this dashboard but I want to help you be able to make this dashboard next time.” So maybe it’s not the best business strategy.

[0:17:06.3] LP: Right.

[0:17:07.1] JS: But like, “How can you do it with your data next time?” Or at least update this dashboard, so we’re going to do a little bit of training at the very least to show you how these tools work.

[0:17:15.9] LP: Absolutely. Yeah, no, that makes sense. So, one of the things I loved about Better Data Visualizations, your book, was how many charts I had not heard of and I thought we would dedicate, you know, we spend a lot of time talking on the show about the basic ones that are best for more explanatory executive decision making but I want to give the people what they want and talk a little bit about some of the more obscure items that you have in here and I want to see how many we can cover. So the first one you mentioned, what the heck is a bee swarm?

[0:17:53.2] JS: I knew you were going to come up with that one first. It’s one of my favorites. Yeah, so what’s a bee swarm chart? So it is essentially a bunch of dots. They tend to, when you plot them out, it’s all the dots in your data, when you plot them out, generally because lots of distributions have some sort of mass in the middle, they tend to look like a swarm of bees, which is where the name comes from.

But you can think of it like, for anyone who has never seen one before, you could think of it like, instead of showing a bar chart where you’re showing, say, the mean or the average or the median of some value, you actually show all of your data, and that way, you can see, “Oh yeah, the min of this variable is 50,” but you can see the distribution isn’t from 49 to 51. You’ve got a distribution that goes from one to 90 or something like that. And like every graph type, they work in some instances and not in others.

So for example, if I had a hundred dots, a bee swarm chart might make a lot of sense. If I had a hundred thousand dots, you just going to see like –

[0:18:53.1] LP: Mess, a magic eye.

[0:18:54.6] JS: Right, exactly. So it’s not really going to work. So I really like the bee swarm because you get to show more in your data. You actually get to show the variation more clearly than say, a bar chart or something.

[0:19:06.9] LP: A bar.

[0:19:07.5] JS: And, I think it helps engage people a little bit more. I don’t have any real research to back that up but I just feel like if I see a bee swarm of the 50 states in the US, I live in Virginia so I can go look for Virginia and I think people will engage more with graphs when they can kind of see themselves in the data and you don’t necessarily get that in a bar chart. It’s like, “Okay, there’s the bar, I don’t know where I am but okay.” So I’m a big fan of the bee swarm chart. You know, it’s one of my new favorites.

[0:19:34.6] LP: That’s so cool. I’ll try to have links to all of these but you know, I’m looking at some of these and I’m thinking about the questions. I’m always thinking, if I’m going to use a chart, I want to know the question I’m trying to answer and that determines the chart, not the other way around. A bar is kind of like this totality. It’s a view of totality, it’s just, “This state is this value.”

But in looking at some of these swarms, I think you can ask deeper questions of, “Okay, I see where the general value is, however, there’s a lot of deviation here versus this state, maybe that’s a good place to look to see why it’s so much wider.”

[0:20:13.8] JS: Right and if you think about, because you mentioned earlier the business case, imagine if you have multiple retail outlets. So you have 50 retail outlets in your company and average revenue in some quarters was USD50,000. Well, that might mask the fact that some of them had USD100,000 and some of them had USD10,000 and the means miss that.

I mean, that’s sort of a core tenant of data visualization, like look at your data because that helps you see the variation. And you know, means and medians and even variances sometimes mask those interesting or outliers for different findings but the chart type itself, the bee swarm really helps you see that and especially see those outliers.

[0:20:55.3] LP: And I think it could even be such an interesting storytelling scenario if you’re presenting live and you’re prepared to walk through what a bee swarm is supposed to say.

[0:21:03.6] JS: Absolutely.

[0:21:05.2] LP: I love to start charts or stories with more aggregate views, kind of start them off with something they expect, something simple but then reveal that, “This is what happens when we look at the distribution of this,” and, “Look at what we’re seeing here in these places.” Well, that’s so neat. Now, we love yummy, edible charts like pies and donuts. Tell me about the waffle.

[0:21:29.7] JS: Oh, the waffle, yeah.

[0:21:32.1] LP: Breakfast food chart.

[0:21:33.8] JS: Breakfast food chart, yeah. So I will say that I define the waffle chart maybe a little bit differently than others.

[0:21:41.0] LP: Okay.

[0:21:41.8] JS: So think about what’s generally called a unit chart. So a unit chart might just have squares or circles and they’re just kind of arranged in some grid or stack or something like that. For me, I define a waffle chart as kind of a subset of that, which is, it’s literally a 10 by 10 grid.

[0:21:57.5] LP: Oh, okay.

[0:21:57.6] JS: So that each unit, each object, each shape is a percentage point. So I kind of define it fairly specifically and –

[0:22:05.2] LP: Only 10 by 10.

[0:22:06.4] JS: Only 10 by 10. So now, does it really matter what we call this thing? I don’t know.

[0:22:11.2] LP: It’s Jon’s way or the highway.

[0:22:12.1] JS: Right, right, exactly, yeah. So if you want to call it a unit chart, you can go ahead, be happy. I like the waffle chart as one of the alternatives to a pie chart. I think it is, in a lot of ways, more engaging just because the pie chart’s been around forever and we’ve seen it a million times, which is good and bad, right? I like that you can do a little bit more annotation and labeling on it. You have a little bit more flexibility in how you’re going to do that and you can sort of show groups within groups.

So let’s say you have three groups and they’re 20%, 50%, and 30%, you can even show in that 20% group, you know, maybe there are 10 squares you want to highlight. So add a little border around those 10. So they’re literally just 10 by 10 grids. I use them as one of my sort of standard alternatives to the pie chart, and not that pie charts are bad, like there’s that whole side.

[0:23:02.9] LP: What?

[0:23:04.5] JS: What? I have no idea. You know, there’s a whole side, like, we could, you know, we will need a much longer show to talk about that. Not that pie charts are bad but in some cases, pie charts just kind of look old just because people have used them for so long, and sometimes, like to your point from earlier on the “it depends” part, sometimes engagement is just a goal. You just need to get people over to your website, over to your –

[0:23:26.5] LP: Appeal is a thing.

[0:23:28.0] JS: Appeal is a thing. Right, exactly.

[0:23:29.5] LP: It’s a part of aesthetics.

[0:23:29.8] JS: Right, exactly, and maybe it is just to show, “Look how much of the total this part takes up,” and that’s important to show, right?

[0:23:38.9] LP: Right.

[0:23:39.4] JS: And so you’re not asking people to say, “Is this like 78% or 79%?” You just want to see, “Oh, it’s a lot.” Maybe just engaging people is the goal. I just like that chart as an alternative and it’s easy to make, right? Because it’s just squares. And it’s named after a waffle. I mean, anything where you can get these good breakfast foods.

[0:23:58.8] LP: I know, exactly.

[0:24:00.2] JS: Pie is a breakfast food, for those who are wondering. Pie is definitely a breakfast food.

[0:24:03.4] LP: Pie is definitely a breakfast food. Now, I understand. Even with the pie, you know, being a circle where we’re not the best tuned to determine area with circles. We’re not good at comparing them and I think this is a great alternative to understand what the composition is of a specific piece in comparison to the rest and probably even be a little better at understanding it.

All right, I love this one, I’m so glad you mentioned it. I wish more people would learn to use it, it’s the dumbbell dot plot.

[0:24:37.4] JS: Oh, yeah. So how can we explain this to folks?

[0:24:42.4] LP: It does need a bit of training.

[0:24:44.5] JS: It does. So people call it different things, dumbbells, dot plot, there are other words for it. I mean, you could think of it like two dots on a line just sitting next to each other with generally a line or an arrow that connects them. The way I approach this is the way many – no, I am going to say most, I think this is going to be true, I think the way most people would plot the following type of data is going to be using a paired bar chart.

So imagine you have two observations for multiple groups, so that might be countries, states, gender, whatever, right? So you’ve got value A and value B.

[0:25:24.2] LP: When you say paired, do you mean clustered bar?

[0:25:27.0] JS: Yeah, I mean bars next to each other.

[0:25:28.7] LP: Right, okay.

[0:25:29.4] JS: So paired or clustered, yeah.

[0:25:30.3] LP: I was going to ask you about that, so this is great.

[0:25:33.4] JS: Yeah, so these bars are sitting next to each other. So you’ve got the first value for the United States, the second value for the United States, space, first value for Canada, second value for Canada, and so on and so on. My instinct is that that’s the way most people would plot those data and the challenge there with that chart type is A, there’s a lot of ink on the page. So we got a lot of stuff that’s hard to add annotations, it’s just kind of heavy, sometimes kind of heavy but more importantly, we’re asking people to make a lot of comparisons simultaneously, right?

You gotta compare the level of the bars by their length or height, the difference between the two, and then those same comparisons across the group. So you gotta do within and across comparisons and it’s kind of hard to do.

So dumbbell dot or dumbbell chart or dot plot often makes that task a lot easier because you’ve placed these dots on the same row for each, in this case, country and you can see both the relative differences and then you can see the gap between them. It’s a good approach and I would just say for folks who are really interested in the tool’s piece, that chart is also pretty easy to make because you just have to recognize that it’s just a scatter plot, right?

[0:26:42.5] LP: Ah, okay.

[0:26:43.2] JS: Because the X dimension is your value and then the Y dimension is just some row holder, just a placeholder that you are putting in on a single row if you sort of have the dots next to each other if that’s your image of it. So again, back to our earlier conversation about philosophy, what can your tools do? How do they, man, I don’t want to use the word think because I’ll get into ChatGPT.

[0:27:04.5] LP: Oh boy.

[0:27:06.1] JS: Yeah, I know, right? What’s the mechanism that the tool works in? If you think about that sort of 2D XY space, you just need data in two dimensions and it doesn’t matter what the dimensions are, right? It doesn’t matter if it is a real number and an imager or a real value and something you’ve made is, as long as you have two dimensions, the tool is going to plot it and so that’s all you really need.

[0:27:27.3] LP: That’s great. Yeah, the way I think of it is a list of categories is if it were a bar but I have two values for each category and I loved it for doing things like pre-post analysis of pages or yes-no survey data or demographics, male-female, things like that.

[0:27:46.6] JS: Yeah, which is a really good point because it just demonstrates that it can be used for so many different types of data. 2020 and 2023, male and female, yes and no. I mean, you can kind of use it for any data, which is a great aspect to it. I will say that my personal approach is if I use a dot plot for change over time, I generally add an arrow. So the line that connects the dots, I’ll do as an arrow.

[0:28:08.9] LP: Oh, okay. That’s a good tip.

[0:28:10.6] JS: If it’s like yes-no, I’ll just do a line because it is not a direction, right?

[0:28:15.9] LP: Oh, it’s a great idea.

[0:28:16.9] JS: Yeah.

[0:28:17.2] LP: I like that, it’s clever. All right, so we can check that one out, a dumbbell dot plot. All right, this one has a fun name and I actually came across this the first time during the last election. It’s a Voronoi if I am pronouncing that correctly.

[0:28:32.3] JS: Oh yeah, a Voronoi chart. Yeah, these are tricky. All right, so how are we going to explain this to folks who have never seen one before? Well, let me describe it maybe the way I describe it in the book.

[0:28:42.7] LP: Okay.

[0:28:43.4] JS: This might be a good way to do it. So imagine a city and imagine that there are 20 fire stations in that city around different areas of the city. What you can do with that city is you can look at each of those firehouses and you can split the city up into many areas. So you just kind of cut up your city so that each one of those mini areas you just created has one firehouse in it.

If you create the Voronoi diagram correctly, the fire station and each one of those little areas is closer to all the areas in their section than any other firehouse.

[0:29:21.4] LP: Okay.

[0:29:22.3] JS: So what is often used for urban planning and in the ecology literature is that you could say, “Okay if there is a fire at 4th and Main, fire station A should go there, not fire station B, because they are closer.” That’s sort of like the classic example, the way I’ve seen them used now is more like an alternative to a pie chart because if you think about it, you take that city, just split it into multiple groups and you can sort of get this part to hold.

It’s just a different way. I mean, I don’t know if we are really good at discerning the quantities from that but that is how I see them used more often now. If you go deeper into it, like the one that I saw that was really cool and I remember that is not my field but on fire prevention and forests, like firefighting in forests and how they would like place different teams in the forest to fight the fire.

[0:30:23.5] LP: Oh, interesting.

[0:30:24.5] JS: Because they would like, “Okay if we position this team here, they’re going to go to this edge of the fire and this team over here goes to this edge of the fire.” If you just draw the geography, draw the map or the diagram correctly, you can figure out who should go where. So yeah, it’s a really interesting kind of chart type.

[0:30:39.1] LP: That helps me understand a bit better. The context I saw it during the 2020 election, maybe it was 5-38, I’m not sure, it was almost like a decision tree that showed what would happen as each state was called.

[0:30:54.4] JS: Yes.

[0:30:55.7] LP: And all of the outcomes of the election on every scenario, every combination of every state going a particular way and then they would kind of fill in the state that would be just called, and then the remaining states were filtering a tap.

[0:31:11.8] JS: Were filtering, yeah.

[0:31:12.7] LP: I thought it was so fascinating. I was there for hours looking for certain outcomes.

[0:31:18.2] JS: Yeah, yeah.

[0:31:18.9] LP: And rooting for certain states.

[0:31:21.4] JS: Yeah, it’s a good chart. It’s harder to make obviously because you need these irregular polygons, which is harder to make. So I’ll give you one more that I think is really cool and hopefully, some of your listeners know of Jon Snow, not the Game of Thrones Jon Snow but the Jon Snow cholera map. So real quick, Jon Snow mid-1850s made this map of this area of London and tracked deaths from a cholera epidemic and it is a fairly famous example of early epidemiology.

Now, people call it the Snow Map but there is a second version that he created shortly after his first version. Well, let me backtrack for one second. So the issue that he uncovered was that there were different water pumps around this area of the city and he figured out that the water was contaminated and that was his big contribution to epidemiology. But what’s really interesting is that his later map actually drew this area around this particular region of the original map surrounding this one water pump that had broken and was infected with sewage.

So you can take what people call that map and divide it into a Voronoi diagram because each water pump becomes one of those positions, like one of those fire stations that we talked about earlier. So you could see people were closer to that one pump than other pumps as you draw this out. So when people call the Snow Map, it is actually the Snow Voronoi diagram, which is just kind of a cool dataviz history background kind of thing, yeah.

[0:32:57.3] LP: Easter egg?

[0:32:58.1] JS: Easter egg, yeah. Yeah, it’s an Easter egg, yeah.

[0:32:59.9] LP: That’s great. So obviously, it may not be the most practical for most cases but it’s fun I think for people to just see data visualized in different ways and tap the different ways we are able to understand the information in different forms. So we’ll get a little more practical, we’ll talk about a kind of chart that’s used a lot in everyday business and maybe has a more accurate alternative, which is the geographic map, I think cartograph.

[0:33:29.4] JS: Well, so the standard map would be like a choropleth map.

[0:33:32.9] LP: Oh, that’s what I meant, choropleth.

[0:33:34.1] JS: I am looking at a bunch of them on my screen right now.

[0:33:36.1] LP: All these words.

[0:33:36.9] JS: Yeah, I know, right? So yeah, if you see a map where the states or whatever are filled in with a color, that’s called the choropleth map. The challenge with the choropleth map is that the size of the geography doesn’t always correspond to the importance of the data value. So think about the map of the world, Russia is a huge country, like tremendously huge but pick any data set, the value for Russia may not be particularly important and so –

[0:34:10.4] LP: Right.

[0:34:11.1] JS: Median income is a great one, right? Luxembourg has the highest median income in the world but it’s a super, super, super tiny country and so you can’t even see it and so I spend most of a chapter in the book talking about all these other different approaches where the idea is, let’s resize the geographies so that they’re closer to the data value which has this effect of distorting the data so that Luxembourg no longer looks like Luxembourg because we made it bigger and Russia no longer looks like a Russia because we’ve made it smaller. So tradeoffs, tensions, it depends, back to the kind of theme of our discussion, right?

[0:34:50.1] LP: Right. I mean, you could use a bar worldwide to just show that but you’re also going to lose the sense of spatial location, right? Are there questions or conclusions you can come to because of the spatial location? So one of the things I saw that I really liked were the hexagon or tile versions. So tell us about those.

[0:35:13.2] JS: So one way that you can make a map that does this tradeoff, you know, the tradeoff is that people can find themselves on a map but the geography may not match. One of the tradeoffs is to create what’s called a tile grid map or a hexagon or hex-grid map, where basically you take all of the units, all the geographies in your geography and make them the same shape, be them squares or be them hexagons.

I’m no cartographer. I’m sure cartographers would debate, and I’m sure they can talk forever about whether hexagons are a better fit than squares. When you do that, you have to start making some decisions about where you’re going to place the squares because now it’s arbitrary. So what you often see, and the US is a good example, most of the tile-grid maps I see, Florida is like off to the side and just below Georgia but we know that they are stacked on top of each other in real life.

But if you want to get that sort of curve to look like the US, because Florida kind of branches out there to the east, most people kind of push it over to the side there. So it is arbitrary how you structure it because it is an arbitrary shape but one of the advantages of that is you can start adding more data to your map. So if you want to add little line charts to each state, you can do that because now Texas and Rhode Island are the same size or the same shape, so you can sort of add more data, you can play around a little bit.

[0:36:37.4] LP: And you can use color or color intensity as the measure because now, the area, the size is not working with the color to create additional meaning.

[0:36:50.2] JS: I think it’s just a useful way to think about geography in a different way but I think it’s also worth just noting that people love maps. They love to see maps, they love to read maps but that doesn’t mean that every dataset that is geography should be a map. I think that’s just important to realize that just because it’s geographic data, why are you using a map?

And Lea, to your point earlier, are you telling a geographic story? “Oh, look at this thing that’s happening in the southern part of the country?” That’s an interesting geographic story. But if you are just saying, “I want you to compare these different values across this geography or this country or this world,” maybe a map isn’t the right way because if you look at a map where you’re putting countries into five bins, well now, in this one bin the US and the UK are the same color but maybe their values are very different and you can’t see that in the map.

So there are all these tradeoffs and it’s not that one is right and one is wrong, it is just a tradeoff, and back to “it depends,” which I hate giving that answer because people want an answer but it really does depend.

[0:37:59.8] LP: Right, test and see.

[0:38:01.6] JS: Yeah, test and see. Try. I think that part of seeing is getting the feedback from folks. So you’re testing, you’re seeing, ask people what they thought, like, “Is this clear?” I think a lot of us make the thing, we put it out, we move on to the next task in our jobs and we don’t have time or patience or money to go in and ask people like, “Did that work for you?” but I think that is how you get better.

“Give me some feedback. Did I mess this up? Is this good, is it clear?” I think that is just a big part of the data visualizers tool kit. Data visualizer, I don’t know if that’s a thing like we got to come up with a word.

[0:38:41.0] LP: We just made it a thing.

[0:38:41.5] JS: We’ve gotta come up with a name for what we do, so yeah.

[0:38:44.9] LP: I like vizier, that’s what I like.

[0:38:47.0] JS: Vizier, yeah. Well then, we have to get a fancy hat.

[0:38:49.3] LP: Oh, yeah, you’re right. It’s too much. All right, so this one’s well-known but at least eight people told me how much they hate this chart type this week. So why do people hate scatter plots? What’s going on there and can they be redeemed?

[0:39:08.4] JS: Yeah, I don’t know. I think because they’re not instinctively intuitive but it is important to know that they’re not intuitive not because well we, as human beings, are unable to read them but because we just haven’t learned how to read them.

[0:39:20.9] LP: Right.

[0:39:21.8] JS: Human beings aren’t born knowing how to read a bar chart. We have to learn how to read a bar chart.

[0:39:25.8] LP: Right, that’s true.

[0:39:26.6] JS: I think the way to think about this is if you’re creating scatter plots and you’ve run into this problem where your managers or colleagues or audience like doesn’t get it, instead of having one label for each access, so let’s do GDP again, say you have your country, your bubbles are countries, and on the horizontal access is GDP. Instead of just placing GDP and hopefully everybody listening knows that that’s gross domestic product but I felt like I had to add that parenthetical.

[0:39:54.0] LP: I thought you said ChatGPT.

[0:39:55.2] JS: Right, right.

[0:39:56.0] LP: Totally.

[0:39:56.7] JS: Right. So you have GDP on that X-axis label, don’t be afraid to add maybe two other labels that are maybe to the right you say, “GDP is going up,” or, “GDP is higher,” and then maybe a little arrow, and then on the other side, “GDP is lower,” or, “GDP is declining” with an arrow that way. So you add this annotation layer or these labels that help explain to people how to read the graph instead of saying, “Maybe you’ve seen this graph before, maybe you haven’t, here it is, good luck, go figure it out,” you really provide more annotation, more instruction right within the graph.

[0:40:32.7] LP: I agree. I think we don’t train our audience enough in charts and annotate in a way to guide them through even step-by-step to do that. All right, so maybe it’s really not the actual charts. It is about the execution.

[0:40:46.4] JS: I think it is about us. I think this holds for a lot of things, you know, writing and presentations and all sorts of things. We forget that we have been working with our data or with our content for weeks or months or whatever and we forget that people haven’t seen it before and we just expect them to get it right away and more often than not, we need to hold their hand a little bit, right?

So writing is especially that way. I mean, I find a lot of people who write, especially in the academic world, they write as if I know what they’re talking about.

[0:41:15.5] LP: No clue, no clue.

[0:41:17.9] JS: Yeah.

[0:41:18.2] LP: All right, so our last chart question before the next segment.

[0:41:20.8] JS: Okay.

[0:41:21.1] LP: Is there a non-standard chart in your book that you would just love to see people learn how to use more, that you think is useful?

[0:41:28.7] JS: Ooh, that’s a good one. I mean, we’ve talked about a few of them, like I think the dot plot is one. The other one that I like that I use a lot that is related to the dot plot is a slope chart.

[0:41:39.1] LP: Oh, how did I miss that one?

[0:41:41.4] JS: Yeah, that’s kind of my go-to now. It kind of tries to address the same challenge as the dot plot and at its core is just a line chart but with two points rather than multiple ones. That’s really all that is at the end of the day but it is using the same kind of scenario as a dot plot where you have data for Canada for two years, data for the US for two years and you want to be able to make multiple comparisons quickly and simultaneously.

I think the slope chart is a really nice way to do that and it also can often be more compact than a paired bar chart or clustered bar chart. So I think the slope chart would be the other one that I – I do see a lot of people using it but I think that’s the one that I would like to see in particular to replace that paired bar chart.

[0:42:27.6] LP: I’m so glad you mentioned it because I have a blog post about it on my blog, shameless plug.

[0:42:31.7] JS: Okay, shameless plug. Look at that.

[0:42:33.0] LP: And I agree, again –

[0:42:33.8] JS: And we didn’t even plan that.

[0:42:34.6] LP: I know.

[0:42:35.2] JS: That was totally organic, yeah.

[0:42:36.9] LP: Just complete neuro inter-reception there.

[0:42:38.6] JS: Mind-meld, yeah, right.

[0:42:39.9] LP: I love it also again for like pre-post but what I think it really lends itself well for is like building a story like a slide build over a series and you can put a certain number of the lines there that maybe people expect but then really highlight and distinguish and align.

[0:42:57.6] JS: Yeah, one step at a time, yeah, absolutely.

[0:42:59.2] LP: Right.

[0:42:59.8] JS: Yeah.

[0:43:00.1] LP: Cool stuff.

[BREAK]

[0:43:07.6] LP: All right, so we have entered our final wild-card question. Think very hard here and imagine this very plausible, realistic scenario: you are umpiring your son’s little league playoffs when suddenly, 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 are presenting about and what advice would you give yourself?

[0:43:33.9] JS: Ooh, that is a good one. Okay, so a few caveats. I wouldn’t umpire my son’s game because that would be a conflict of interest.

[0:43:41.1] LP: Okay, makes sense.

[0:43:42.1] JS: So that’s the big, big caveat. First presentation, professional presentation, so not like when I was a kid?

[0:43:49.9] LP: No.

[0:43:50.7] JS: One of my first presentations was when I was doing grad school, my job market talk, which is you write your dissertation and you have to sort of plan this one talk that you are going to shop around. That’s your topic. And I gave it to the department, I did my graduate work at Syracuse University, and I did the talk and these were the days, Lea, not all of your listeners are going to be able to relate to this, but these were the days, remember these days where you had the plastic sheets and you’d put them on the projector.

[0:44:20.8] LP: The transparency in the overhead projector?

[0:44:22.4] JS: Yeah.

[0:44:22.7] LP: No, I don’t know what that is.

[0:44:25.5] JS: Oh my goodness. So for those who don’t know that this was a thing, back in the day, you didn’t have PowerPoint, you didn’t have projectors, you had to print your slides on these plastic sheets in a special printer where it would always smell like burning plastic and then you put these sheets on this projector that was like this big lamp and it would project up and then there would be a mirror and it will project onto the thing.

So anyway, that was the day and I had a laser pointer, which I rarely use anymore but I kind of lost it. I remember losing it on my body somewhere and I couldn’t find it and it turns out, I had maybe dropped it on my back pocket but the whole time I was trying to find it, I was stammering through the whole thing. I was like, “Um, so yeah, um…” and then afterward, my adviser was like, “Yeah, you need to cut back on the ah’s and um’s.”

I was like, “I just couldn’t find my laser pointer, that’s all it was, I was really prepared,” so I don’t know what the lesson learned there is, yeah.

[0:45:23.4] LP: Well, this is really relevant to today with our laser pointers.

[0:45:26.4] JS: So that was pretty bad, yeah.

[0:45:29.8] LP: Well, I just appreciate the trip down memory lane on that one. That method for sure is highly designed with neuroscience in mind, no question, And that fan, that droning fan.

[0:45:43.1] JS: Oh, the fan that would just burn and it would just get so hot and –

[0:45:45.8] LP: Just start to doze off and –

[0:45:47.6] JS: Oh, I remember I had a professor earlier in graduate school. His slides were blank transparencies and he would just write instead of a blackboard, he would just write and it’s like, “Wow, this is a thing.”

[0:46:00.6] LP: So trailblazing, wow.

[0:46:01.8] JS: Right? At the time, who knew? Yeah.

[0:46:04.9] LP: Well, that’s a good one. I’ll take away, just have your pointer, have your clicker, and then don’t let it slip back there.

[0:46:11.1] JS: Don’t lose it. Don’t lose it, tape your pockets closed if you need to. Don’t lose your clicker. It’s a good one.

[0:46:18.2] LP: I need a second. Oh, well, I cannot believe how fast this went by. That means we had a great time once again. So unfortunately our time has run out but please let the listeners know where they can keep up with you.

[0:46:33.0] JS: Yeah, absolutely. So my preferred social media is Twitter, so you can find me @jschwabish. I’m also on Instagram but it’s mostly just fun signs that I see outside and take pictures of them but mostly Twitter.

[0:46:44.6] LP: Oh, I’m following that.

[0:46:45.6] JS: Yeah.

[0:46:45.7] LP: It’s like my favorite thing.

[0:46:47.0] JS: Right? And then my website, of course, policyviz.com, and those are the main places they can get a hold of me and if they want to see some of my research, they can go to the Urban Institute, which is at urban.org, and they can find all the great stuff that we’re doing every day.

[0:47:01.6] LP: Very cool. Well again, we talked about Better Data Visualizations, which you absolutely must have in your bookshelf and all of the charts, books, links, and everything we’ve talked about will be on the show notes page for this episode. Jon, it’s always a pleasure. You didn’t disappoint and just keep writing books, you’ll keep coming back.

[0:47:22.5] JS: Okay.

[0:47:23.0] LP: We’ll keep jamming.

[0:47:23.8] JS: Thanks Lea, I appreciate it. Now, I have this whole new task on my list like, “Write new books.”

[0:47:28.7] LP: It’s the only reason, right? Well, thanks again.

[0:47:32.1] JS: Thank you.

[0:47:32.9] LP: I look forward to when our paths cross again.

[END OF INTERVIEW]

[0:47:43.0] LP: All right, I hope you enjoyed that interview. Man, I could have kept asking about super-secret chart types forever and you know, while I recommend keeping it basic for senior executive and decision-maker audiences and presentations, hopefully, these new exciting advanced chart types will whet your appetite for exploring novel ways of seeing numbers in interesting forms.

So to catch all of the links to the resources mentioned in this episode, visit the show notes page at leapica.com/084, and if you’d like to connect, don’t be shy and reach out to me on LinkedIn or Twitter and be sure to send a connection invite with a note mentioning the show. I love to meet my listeners and I respond to every message.

I’ll leave you with a little bit of presentation inspiration by our guest, Jon Schwabish. I located it as his final thoughts for his awesome book, Effective Data Visualizations. I just really found it to be such a wonderful way of wrapping up this amazing work. He says, “I first became interested in data visualization after seeing much of my and my colleagues’ work go unnoticed and unused. I did not come to the field with a degree in design or computer science or data science and because I did it, I believe you can too. In fact, anyone can effectively communicate their data by thinking critically about their own work and the needs of their audience, readers, and users.”

I really can’t improve on that. It really says it all. You don’t have to be a rocket scientist to deliver data effectively. You just have to know where to go to learn the tools, the skills, and the mindsets and luckily, you are in the right place. That’s all for today. Stay well and namaste.

[END]

The post Advanced Data Visualizations, Charts, and Graphs with Jon Schwabish appeared first on Lea Pica | Data Storytelling Instructor.

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Google Data Studio Dashboard Do’s and Don'ts with Michele KissPresent Beyond Measure Ep. 083Michele Kiss is Here to Help You Design Killer Google Data Studio DashboardsThis episode is about the benefits of using Google Data Studio for building data dashboards, creating reports and presentations, exploring and analyzing data, integrating business information, and more.

Google Data Studio is Michele’s go-to tool, and after hearing about its interactive nature, the visualization options it provides, and the way in which it allows one to craft captivating data stories with relative ease, it’s not hard to understand why!

Michele Kiss is a well-recognized digital analytics leader, with expertise across web, mobile, and marketing analytics. She is a Senior Partner at Analytics Demystified, where she works with clients on analysis, training, and process, to help them draw insight from their digital data.

Michele was the winner of the Digital Analytics Association “Rising Star” and “Practitioner of the Year” awards. She is a frequent blogger, writer, podcast contributor, and speaker. You can read her thoughts at the Analytics Demystififed blog.

And in this episode, Michele provides an in-depth explanation of the many different ways you can design award-winning Google Data Studio dashboards!

In This Episode, You’ll Learn…* The multitude of benefits of using Google Data Studio. * The different ways in which Google Data Studio dashboards can be optimized. * How to make the most of the interactive nature of Data Studio. * Her favorite places for finding Google Data Studio templates and best practices. * Why Michele opts for more presentation slides with less information on each slide when presenting data. * Michele’s favorite Data Studio data visualization strategies. * The difference between the Data Studio dashboards and Data Studio reports. * Why simplification is key when it comes to data presentations and how to do it.

People, Blogs, and Resources Mentioned* Looker Studio Blog * Google Data Studio Online Course * Measure chat * Analytics Power Hour * My free 30-second online assessment to find out and overcome the #1 silent killer of your data presentation success

How to Connect with Michele Kiss:* Michele’s LinkedIn and Twitter profiles * Analytics Demystified Where Lea is Speaking Next:I’d love to meet you, in-person or online! Here are the data storytelling, analytics, digital marketing conferences and events I’ll be speaking at:

There are no upcoming events.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!

Now, I’m going to ask two favors from you:

1. If you’re digging the show, don’t forget to hit the Follow buttons on iTunes or Spotify to never miss an episode.
2. If you liked what you heard, I would love if you could leave me a rating or review in Apple Podcasts. 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, always remember: viz responsibly, my friends.

Namaste,

EPISODE TRANSCRIPT[00:00:00] LP: Hello, hello. Lea Pica here. Today’s guest helps data practitioners create Google Data Studio dashboards that create raving stakeholder and client fans. Stay tuned to find out who’s dropping the knowledge on the Present Beyond Measure Show, Episode 83.

[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. Now your host, Lea Pica.

[OVERVIEW]

[00:00:45] LP: Hey, guys, and welcome to the 83rd 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 your thoughtfully presented insights and ideas.

Today’s interview is loaded with juicy and actionable Google Data Studio dashboard tips from a true GDS pro, so be sure to stay tuned in. But before we get rolling, I just have a few fun updates for you.

All right, now, as usual, I am stoked for today’s guest. But in particular, I’ve known this analytics rock star since I began my journey as a data storytelling advocate and trainer. Gosh, 10 years ago now, and she has a well-earned reputation as a total pro on building Google Data Studio dashboards. Let’s dive in.

[INTERVIEW]

[00:01:52] LP: All right, hello, and welcome everyone today. I can’t wait to introduce my guest. My guest is a recognized Digital Analytics leader with expertise across the web, mobile, and marketing analytics. She’s a senior partner at Analytics Demystified, where she works with clients on analysis, training, and process to draw insight from their digital data. She was the winner of the Digital Analytics Association Rising Star and Practitioner of the Year awards. She’s a frequent blogger, writer, podcast contributor, she speaks all over the world. I’ve gotten the privilege of speaking with her at many conferences, and we go really far back. I’m so thrilled to finally, finally have her on the show today. So please, help me welcome the latest guest in my superstar women and analytics spotlight, Michele Kiss. Hello.

[00:02:43] MK: Hi, thank you so much for having me.

[00:02:46] LP: Of course, this is way overdue. You were really there at the outset of my journey as I started to break into the conference circuit and get to know the very tight knit family that is in the digital analytics practitioner space. I’ve just always really appreciated, always admired the clarity and the value that you give in your talks, and just really appreciated all the support and friendship that we’ve had along the way.

[00:03:14] MK: I still remember early conferences, I remember when we first met and normally people come at least – I know that when I first started speaking, it was a bit of a train wreck up there, and then you would get up there and you’re like, “Man, you nailed it.” You were brand new to me in the industry. I remember just being like, “It is awesome that we have somebody out there speaking very clearly, very well, and women and analytics, go!” It was pretty exciting.

[00:03:43] LP: I appreciate that so much. I think I remember things a little bit differently. I don’t remember anything [inaudible 00:03:49]. I do remember avid standing ovations for you, including myself, because I think I called you the OG before. For me you are one of the OGs of that space, especially as a woman coming in and really like having the deep expertise as a speaker, but also, I loved how much common ground we shared in the actual communication and visualization, storytelling part of data, as well.

So, we’ve had so many alignments there. That’s why I’m so happy to have you on the show today.

[00:04:21] MK: Yeah, happy to be here.

[00:04:23] LP: Cool. So, I mean, you’re super well known in the space, but just in case someone has been living in a cave the last decade, why don’t you let us know what it is you do and also your origin story. Everyone loves that. How did you fall into this crazy line of work?

[00:04:39] MK: I definitely count as one of the people who fell into it. I moved from Australia to the US in 2005, and I just had to get a job, any job after college. I ended up being employed as a marketing assistant at Kelley Blue Book, like the car valuation company. They had this interesting VP there, and he would hire people, like he didn’t really want an assistant, he would just hire people, like, “I think they’re smart. I think they’d fit into the company.” He would kind of hire them in, and then figure out where in his team somebody needed help.

We had lots of people that came in through this role, and went into user experience, went into editorial, and in my case, went into analytics. So, I started as this assistant, and the analytics team needed help, and they’re like, “Okay, we’ll show you what buttons to press in Excel.” I ended up learning everything on the job. We did a lot of advertising analytics, and a lot of – back then it was Omniture, I learned everything from a mentor that I had at Kelley Blue Book, who kind of took me under her wing. And then, the rest is kind of history.

I, over time, became the manager of the team. And then, I hit a certain point, probably, I think it was about five years in where I was like, “All right, I think I need to be challenged in different ways.” I left Kelley Blue Book, and I joined an agency figuring that gave me a lot of experience with lots of different business models and analytics challenges without having to like job hop constantly. So, I joined there, then I went back practitioner side, and then Eric Peterson from Demystified contacted me, and then that is where I’ve been ever since.

[00:06:34] LP: I know. Actually, I had the pleasure of working as a consultant, like a gun-for-hire, for Analytics Demystified, too. So, it was great to sort of be close during that time period, and work with one of the most stellar teams of all time in the space.

So, what I would gather from what you’re saying is that you present data as part of your work. You don’t just explore and crunch the numbers and make pivot tables all day, do you?

[00:07:01] MK: Definitely not. I think that would be kind of boring. So, trying to make sense of it for other people, and to make it seem as uncomplicated and easy as possible for others is kind of a big part of the job.

[00:07:18] LP: So, when did you actually discover the explaining part of data? Because I know a lot of practitioners find the explaining part to be sort of the unfortunate byproduct that they didn’t realize they’d have to do if they joined as an analyst. I know it took me by surprise. And it’s a very cross disciplinary set of skills. So, what drew you so much to that part of it, and also speaking? And what do you think are the qualities or skills that people should think about if they really want to excel in that area?

[00:07:52] MK: I think, definitely my own explaining and presentation skills developed over time. I can look back at work samples and things from things I did early on in my career. I’m like, “Oh, that’s adorable. That was sweet that you did that. Cool.” But I actually had a really interesting role several years ago, pre-Demystified where the team that I worked on had both, at the time, we were web analysts and we also had a team of statisticians. The statisticians were doing very deep number crunching. They were the ones sitting in SAAS, and creating statistical models and things. And I would work with one of our statisticians in particular, and she was, hella smart.

But we would get into a meeting with stakeholders, and she wasn’t great at necessarily being able to explain it in a simple way. So, we paired up, and I became almost like the business translator. She would do the analysis and then I would be the person that explained it to the business and was like the conduit between some of the advanced analysis work that she was doing, and how to explain this to our VP of sales, or how to explain it to our CEO. It was a really valuable role to be in, to have to figure out how to be concise, and how to explain things.

It’s not about dumbing it down. It’s just about making it so that you don’t need an advanced degree in mathematics to be able to understand it. It was great experience, and I think it helped me later on to do the same thing with my own work because it is hard when you have been doing, like I still do hands on analysis, and you’ve been so like deep in something and being able to come 10,000 feet up and be able to explain it really succinctly is difficult. So, I think it was great practice for having to do that for the rest of my career.

[00:09:57] LP: It’s so fascinating to hear about the evolution of things. You make a really good point that a lot of times people will come to my data storytelling workshops and say, “Are we doing Python and any kind of programming languages? Tableau?” I’m like, “No, actually. You’re going to be drawing less on your experience with stats and code and more on your experience from parenting if you have kids. “Can you make a bedtime story exciting and make it really simplistic and use things like suspense and an animated way of speaking?” I even suggest sometimes to people think about how you read a bedtime story to your kids, or get a book and practice that, and now you have more of the energy type and the way of speaking, and the simplicity that you need, as you said, to get people to understand things.

[00:10:58] MK: It’s funny you say that, because part of my son, I have a six-year-old, part of his bedtime routine is a made-up story, which is always something like –

[00:11:05] LP: It’s a great idea.

[00:11:06] MK: He opens the letterbox, and in it, he discovers a magic portal that goes to a mystery world full of talking gummy bears or something. It’s always really outlandish and crazy. But it’s like part of our routine every single night.

[00:11:19] LP: I am so borrowing that idea for mine. We’re always going off of pre-written things but I’d much rather the creative process, and I like how ludicrous that can be. So, that’s really great. Also, I think it helps instill a sense of, you know, when I think about a bedtime story, I look at how little text is on each page. They’re super visual. They’ll start a sentence and then have a dot, dot, dot, and you have to turn the page. That’s kind of how I try to create slides in a presentation. Are they pages that you can turn in a story? Have you created a page turner? So, I love that idea, and I hear that you resonate.

Well, one of the reasons why I’m glad you’re here is people love the tools. There’s one tool in particular that you are super proficient with that I can’t wait to talk about. I haven’t had anyone on to really talk about it, and I’m going to guess you know what that tool is. Drop it like it’s hot.

[00:12:18] MK: Data Studio.

[00:12:20] LP: Google Data Studio. Yeah. So, we’re going to talk about that here. I’m so excited. So first, why Google Data Studio? Why that versus all of the other data visualization options that are available out there.

[00:12:37] MK: I work with a lot of clients that live really heavily in the Google ecosystem. So, I just find that it just fits very naturally and well if you have teams that are using a lot of Google Analytics and other marketing products. Also, if you live in the G-Suite world of Gmail for work, and all of that kind of stuff, it does fit very naturally. I’ve used Tableau and some of the other data visualization platforms, but I like that Data Studio is just kind of there and so easily accessible for people.

I used to, when I was a very young analyst, I remember sitting in like these conference presentations, and everybody would talk about these amazing things that they did. But they did it by having expensive MicroStrategy licenses, or huge enterprises like Tableau work. I would just kind of sit there, like, “I don’t have any budget and I can’t do those things.” So, anything that I can immediately use to add value, and to start working right away is key for me. It’s like one of the things that makes something have value in my world. So, Data Studio has definitely been one of them when it just so easily integrates with the rest of the Google ecosystem.

[00:13:56] LP: For you, time to market, and ease of implementation are really key. I will admit, for the same reasons I’ve used Data Studio in the past and what I was particularly impressed with, versus a lot of the other tools that we’ve just grown to adopt everywhere that are available everywhere, is I have a chart detox process that I take students through and it had preemptively eliminated a whole bunch of those steps before even getting started.

So, I was like, “Wow, someone was listening to The Wall Street Journal guide to information graphics.” They definitely implemented, it seems like they implemented a lot of best practices out of the gate so that they’re eliminating some of the guesswork that people have, or default formatting that ends up really distracting people. So, I appreciated that as well.

[00:14:52] MK: Yeah, and don’t get me wrong. There’s default formatting in Data Studio that I turn off immediately. I have my process where I go through and I change things. No tool has perfect defaults but I do think that it gives you a lot of flexibility to be able to really almost start from this blank canvas and design something exactly how you picture it working, and in a way that works for people’s brains. As opposed to thinking about tools, like if you think about in Adobe, and you’re creating something in Analysis Workspace, and everything is boxes, and everything has very defined formatting. I like the freeness of Data Studio and having this canvas where I can control how it all appears.

[00:15:39] LP: Yeah, there is something to having a greater degree of creative license to create the shapes and the dimensions of things. Sometimes other platforms can feel constrained in terms of having two modules side by side, and then the whole page has to look that way and follow that format. So, I hear that. Obviously, this is a data storytelling podcast. I love to hear stories of use cases that people have, especially for using these tools. So, do you have any interesting particular cases that you implemented in Data Studio and saw really great results for communicating?

[00:16:20] MK: I mean, I use Data Studio for a lot of different things. I use it for building dashboards, I use it for building bigger, more complicated reports with more information than what I would consider a dashboard to have. I actually used it myself to do analysis, because I find it so easy to build something that lets me look at a lot of different data at the same time and then add a couple dropdowns, and then I can filter and I can like actually engage with the data without having to pull up 17 different tabs in Google Analytics, for example. I can really play with it. I can view like, “Oh, what does this trend look like? What does this trend look like?”

So, I actually use it as an analysis platform as well and I do build things that I don’t expect anybody else will ever look at. They’re just for me to process the data.

[00:17:14] LP: Right. Of course.

[00:17:17] MK: But I also like, I have some clients who I effectively build presentations in Data Studio. So, what I’ll do is I make Data Studio, going back to constraints and how free Data Studio is, I will make it look like slides. I’ll have my title that is talking about what the data is saying, but I use it to like live pull the data in. It means that, especially if it’s like – sometimes if it’s a smaller client, where maybe they don’t have necessarily a ton of analytics resources, you can build something that’s very easy to go back to in three months, or six months, or a year, and basically, it’ll repoll all the new data. You just need to change the timeframes and then you can go through and kind of update your commentary or change the titles of the slides so that they’re reflecting like what the new data says. I just kind of make it look like a presentation with dynamic elements, and I find it really great for that.

[00:18:17] LP: I was going to say, because I’ve always struggled a bit with feeling like, I don’t want a presentation to feel super cookie cutter in terms of a slide. Like, “This is our conversions, and that’s the story.” Right? I use storified slide titles, where it’s like an observation of the data. But I love that you’re creating a sort of format or template that the information can be updated on the fly, but then you’re making small adjustments. I think that’s fantastic.

[00:18:45] MK: Yeah, it’s definitely a time saver, too. So, versus having to build a whole slide deck from scratch in a couple months if you decide to revisit something. It makes an analyst’s life a lot easier to be able to do that and have it just update all the charts. When you do that, there’s always other directions you’re going to go in where you’re like, “Oh, I just noticed something. Now, I need to insert a new slide with some new charts that’s going to break down this observation that was in the last couple of months that wasn’t there a year ago.” But just to be able to pull that data in a pretty automated way, eases a lot of the heavy lifting, and is a pretty good way for people to communicate.

[00:19:26] LP: And keep the charts looking unified and consistent and clean. Wow. So, are you using one page for each slide? Is that the general idea? Or how are you structuring that?

[00:19:39] MK: Yes, so each, like what would be a slide is one Data Studio page, and I tend to follow the, you know, you have one point you’re making on each of those slides. So, often some of these decks can be long, because there might be a lot of things that I’m trying to cover in that time. Most of the time, it is typically like only one, at most, two charts per one of these. It’s not like a ton of information crammed on, but it saved me lots of time in revisiting different analyses, because inevitably, somebody always is like, “Can you update that and see if that’s changed?” It makes life a little easier.

[00:20:18] LP: I really want to make sure everyone heard what Michele just said, that you might end up with a larger slide deck, but that you have simpler slides with one main idea on each of them. This is one of the philosophies that I think we both got aligned on, most likely from Nancy Duarte’s perspective on the single idea per slide tenet. Even just this week, during a workshop, I was asked, it happens every time, “But I’m going to have a lot of slides.” I know my take on having a lot of slides that you use as many as you need to communicate your idea clearly and that people understand it. I’d love to hear your take on that as well.

[00:21:02] MK: We have had in previous events and things like that, my company Demystified, in the past, we’ve done a conference called Accelerate. At times, I’ve shared my deck for us to be using with my boss, and he’s like, “There is no way you are getting through 217 slides in a 20-minute presentation.” I’m like, “Oh, watch me.” Because each one has such a small amount of information that it fits this flow where I’m talking and I’m flipping slides as I go. Sometimes the slide is just an image, that’s the only thing that’s on there, or it’s one chart, or it’s a specific point that I’m trying to make. But I am definitely within my organization. I am the crazy number of slides person because I don’t like jamming six things onto one slide. I don’t feel like when you present, you should be like, “Okay, now in the top left corner, this chart is saying that’s now in the top right.”

[00:22:02] LP: Like a zoo tour guide.

[00:22:04] MK: Yes.

[00:22:06] LP: I so hear that. And I think Accelerate was the second or third conference I ever spoke at. I got called out on the slide count, as well. There was a lot of doubt there, too. So, I think we were staging a mutiny during that one. But I think, to your point, not to divert from Data Studio but in general, I think this is a really interesting cultural trope that we’re struggling with in the field, where no one really learned how to bring proper storytelling techniques to the data communication and presentation process.

The way that I think of transitioning between slides is editing cuts. When you’re watching a movie or a TV show, if the frame sat in the same spot, but shoved a whole bunch of things happening in that frame, you’d eventually lose attention. Like, that’s not what’s actually going to keep yourself engaged. So, not a lot of people know that the general best practice out in the world is don’t sit on a slide for more than a minute. I think the Duarte average, I think Nancy Duarte mentioned that their average is three per minute, which is pretty fast.

[00:23:20] MK: That makes perfect sense to me. It’s the style that I tend to follow as well.

[00:23:24] LP: I remember. I was like, “Oh, she knows. She knows.”

First of all, I love that you mentioned using Data Studio for analysis. I haven’t thought about it that way as much. I’ve always sort of imported into Tableau and sort of messed around with stuff in there. But that’s interesting to hear that you’re using Data Studio and filters for analysis. But what I’d love to know is, how do you differentiate between creating a Data Studio dashboard versus a report?

[00:23:54] MK: I tend to go by the rule of dashboards as being everything in one view. So, I follow the rule that once I have multiple pages, and, “Oh, this part is talking about our traffic, and this part is talking about our conversion. And this is talking about what products people buy”, then you’re talking a report. I cringe a little inside when we start calling everything a dashboard, because I’m like, “It’s not.” It should be your at-a-glance operational metrics type of thing.

So, it’s semantic. I totally understand I’m making a semantic distinction between the two, but that’s how I tend to think of them. When you’re building something that’s kind of at a glance, that’s your dashboard. Soon as you start having a ton more information, you’re building out a broader report.

[00:24:41] LP: I really appreciate that. I so agree, when you say at a glance, that’s what really resonates for me where I keep trying to think like, what is my car dashboard for? What is the thing that this was based on? How is that build? What was that designed to help me do? Which was be able to instantly gauge the vital systems of my car and make simple decisions between doing something myself or taking it to an expert in a glance, without being a car mechanic. So, I appreciate that distinction. I want to make sure people really hear that because I absolutely have encountered dashboards that are really reports, or something much more long and involved. When do you see Data Studio being used well? What are the hallmarks of a well-used Data Studio?

[00:25:34] MK: I think the number one thing is that putting aside any tool that you use, it’s used well when you are following general data visualization best practices. So, you can use an abuse any solution, it doesn’t matter what it is. I’ve definitely seen Data Studio used poorly for those reasons. Anything that’s built in a very – following the rules of data visualization, following the rules of what a dashboard is, what a report is, in a user intuitive way, kind of like a good user experience, is something that I would consider a good use of Data Studio.

But I also think that it does open up the ability to be something else, which is not necessarily just building static reports. But Data Studio allows you to create almost a safe space for other people to do analysis. So, you can do that by building the foundation of what the report looks like, but allowing user engagement with it via filters and having different dropdowns, or the ability to like change timeframes, or pick a particular like, “I just want to look at this for mobile.” For somebody who may not know how to do all of that in Google Analytics, or whatever analytic solution you’re using, you can provide this like space where they can do a little data exploration, and you’re not trying to build out every chart that could possibly be built on every cut of every dimension.

But you’re giving them some ways that they can break things down so that they can look at like, “I want to view this by my particular country, or I want to view this by mobile or desktop, or I want to view this based on my specific landing page,” whatever that may be. But I find that that’s a good valuable use of something. Data Studio is actually – it’s not really built to be printed out or PDFd and build static reports. It’s intended to be interactive.

So, I think successful uses of it are when that interactivity is built in in an intelligent way by analysts who know the data, know what they can do with the data, and give business users the tools to be able to engage with the data the right way. I find that that’s a very helpful use of it.

[00:28:05] LP: I really liked that. I pulled out intelligent interactivity from that, and what I appreciate about the role of data practitioner is not always just spoon feeding the insights. I do think there’s a place for really taking the time to craft a full story and narrate it during presentations. But also enabling and getting people excited and drawn into the process of exploring their own data, but creating a very clean sandbox for them to play on. They’re not the ones trying to figure out the data hygiene and tripping up on misinterpreting something, because something wasn’t supposed to be there, right?

[00:28:49] MK: Yes. I mean, everybody can’t be an analyst, and so you have to have people that have different roles in your organization. Having that analyst data subset, almost, is really useful. I also find that it’s valuable when it allows you to pull together other information. So, it might be pulling together multiple data sources so that somebody doesn’t have to go to six different places to get a view. But it can also be integrating with business knowledge. So, adding things into Data Studio like time-based annotations that are going to give, when the business user is looking at something, it’s going to give them some context that an analyst might have been managing the annotations to say, “Hey, this happened on this day. You should be aware of it as you’re looking at this data.” But I think that as you can bring all of that information together, you can make something that’s built in Data Studio really valuable for your business users.

[00:29:54] LP: Yes, I love the idea of creating something that is really empowering and alerting the audience as much as possible because I know on the client or stakeholder side, they can often feel like they’re in a black box, and relying just on the word or however it’s being communicated. It’s definitely not that they want to know all of the nitty gritty. They have their own work and priorities and sort of business disciplines that they have to worry about. But it is really great when they want to get involved and have that sort of hand holding and direction that they need. I love it.

[00:30:32] MK: Yes, and provide some level of self-service. Because you don’t want to have everything that somebody wants to know always having to come through an analytics team and have a personal look at it. So, if there are things that you can do, to make some information available, like get stakeholders fingertips and give them a little bit of control, and the ability to play around with it and do some of that slicing and dicing themselves, I think that that ultimately can also free up your analysts to do more valuable work than just constantly pulling data based on somebody’s win this week.

[00:31:07] LP: Being the gopher?

[00:31:10] MK: Yes.

[00:31:11] LP: Yes. Teaching them to fish. You’re able to catch bigger fish when you’re having more resources. Are there charts or anything that you particularly love in there? Are you pretty basic? What do you tend to lean towards?

[00:31:25] MK: I combine charts a lot in the sense that I use a lot of scorecards with line charts, for example. So, that you’ve got one thing that tells you the number, and one thing that shows you the trend. I use a lot of combos like that. I also use a lot of in-table bars, as opposed to a bar or column chart, because I feel like they take up less real estate and kind of give you some of that same visual at the same time.

[00:31:53] LP: The data bars, yes.

[00:31:54] MK: Yes. So, I would say that I’m a pretty heavy user of those. I also like the ability in Data Studio, where you can make charts interactive, so that if you click one chart, it’s going to filter the other things in either a group of charts or on the page so that you can create dropdowns and things for people to select, but they can also literally engage with the charts themselves and have that feed through to other visualizations.

The other thing I would definitely say that I’m a very heavy user of is I build a lot of funnel visualizations, like a lot. That seems to be a lot of what people want to see with a lot of the projects that I’m working on. It’s like how are users flowing down through this particular path? So, I have taken different approaches. I will sometimes use bar charts as a funnel, so everything’s left aligned, but it’s just kind of stepping down, and I use scorecards to create the metrics and the percentage drop offs and things.

I’ve also used some of the community visualizations. Some of the funnel visualizations, some of the community ones are incredibly ugly. And then there’s a couple of them that are pretty good. If you tinker with them, you can get them to look pretty good and they do a lot of the math for you. So, I both kind of DIY it using base visualizations that are available in Data Studio. But also, sometimes I’ll use some of the community visualizations for places where it might save me a little bit of time.

[00:33:30] LP: Interesting. So yeah, funnels are always interesting because they’re sort of a lightning rod of controversy of the understanding that first, it’s not really technically a funnel, it’s more of a sieve. Also, the visualization tends not to be the most easily interpreted, accurate representation of both people remaining and the fall off because of that, sort of like pyramid structure. So, I’ve generally always used bars creatively for that. But tell me about community visualizations, because I know there are a lot of templates available out there that you can adopt, but are these individual visualization types that you can bring into your report?

[00:34:11] MK: Yeah, and you can say like, “Oh, I want a sunburst visualization or something like that.”

[00:34:15] LP: Oh, cool.

[00:34:16] MK: I would say I’m not a heavy user of them. I tend to use the default visualizations in Data Studio because I very much subscribe to the rule of, I shouldn’t have to explain a chart to you. The chart should make sense. Somebody looking at it should know how to interpret it. And when I have to explain, “This is how the chart works,” it’s too fancy, and it’s just adding too much mental overhead for people.

So, I will confess that I don’t use a ton. I tend to use default chart types much more than community visualizations, but I have had them come in handy a couple of times. They definitely do require though, if the community visualization, the person making it, at all of a sudden doesn’t keep it up to date, or pulls it down or something like that, you’ve got broken dashboards. So, there’s a little bit of risk inherent in that. There’s also been times where like community visualizations broke in Data Studio. So, default charts would work fine nut the community ones didn’t work for some period of time, just like random bugs with that in particular.

I think that if it’s a well thought out chart that is going to make intuitive sense to people, and it exists in a community visualization, then by all means, use it. But I don’t love using them because they look fancy, or just because I have too many line charts on this page. So, I’m just going to make this another chart for no reason when the data is actually suited to a particular type of default visualization.

[00:35:55] LP: You say you apologize because you stick with the basics, but I actually think that’s the answer I’d be in alignment with as well. Because for the most part, the most default basic chart types are the most universally understood. They don’t have a learning curve. As soon as you add the need to learn something new, you are slowing down the process of the actual communication. And that’s especially problematic for a self-driven, self-consuming environment like a dashboard or a report.

What I will say is that I have a few cases where there are chart types that are not universally familiar, like a bar or like a dumbbell dot plot. But it’s actually a very well-designed chart that accomplishes something very specific, which is allowing you to visually compare the difference between two values across a list of categories and also across the categories. I’d have to put up an example to show it.

I used it once in a presentation, but what I made sure to do is only reveal – I actually used the unfamiliarity of it to sort of create some anticipation, but then only revealed each part of the chart after I had explained each component, and that actually built the story as I went along. So, the big final reveal was sort of the punchline to the story of the places we really needed to focus on, that this chart was showing. But also, once they got through the explanation, step by step with me walking through, I was amazed how quickly they got it. But I know if I had shown it all at once, and said, “This is what the chart is saying.” They would have been zooming all around going like, “What is this? I don’t know what this is. How do I read it?”

[00:37:43] MK: Yeah. I will use very lame methods, whether it’s in slides or whatnot, to literally just – I might have the same thing on five slides, but I’ve just blocked pieces of it, just covered them in white, and I will reveal them slowly. Or even if people are going through the slides themselves, when they progress in that way, it definitely helps explain little pieces of information at a time so that then at the end, the whole makes sense, rather than, “Nam, here’s the entire complicated thing.” And they’re like, left with heads’ spinning.

[00:38:20] LP: Yeah, it’s like trying to watch a Christopher Nolan movie where they explained the entire plot in one sentence. You’re not going to get it right away. You have to have it paced out. That’s actually why I call that technique shape pacing, where I use shapes and animate them in and out in order to reveal or obscure pieces. I always say, “Think about how Hollywood is showing you things. And then think about how differently we’re doing it and ask yourself, why are people so confused?”

All right, so we talked about some of the best practices. Where do you see Data Studio not being used so well? What are some of the missteps that you see?

[00:39:01] MK: I mean, the general bucket of things that don’t obey data visualization best practices. I remember being at a GA Partner Summit early on, and they were showing a Data Studio report and just the amount of crap on it was insane. I think there was like a giant picture of a lion in the background, and I was like, “What is going on?”

[00:39:26] LP: It shouldn’t even be allowed.

[00:39:31] MK: It just was a total violation of the data pixel ratio. It was just putting all of this stuff on there. I do find following some of the defaults, so I mentioned that I turn a lot of them off, things like heavy gridlines, stuff like that, that’s not really necessary. Where it tells you like, when you have a table, it puts row one, row two, row three, I’m like, “I don’t need to have those numbers there.” I don’t need the number of pages because Data Studio is intuitive, people can scroll through something in particular. So, just pulling things like that off.

I find clutter, I guess, is actually probably one of the things that can make it more offensive, is when people don’t think through thoughtfully how to clearly articulate information and remove the clutter that’s on the page. I think that that is definitely something that is challenging. That would be the case no matter what tool you were using. If somebody’s going to create something that is not thinking about data visualization best practices, they would do the same thing in Tableau. They would do the same thing in Excel.

[00:40:46] LP: It’s pretty universal.

[00:40:49] MK: I would say huge behemoth, multi-page reports. But I’m also guilty of it myself. I have things that I have built, that are two and a half years old. And over time, we added more and more to it, and they are massive, but I think you still have to step back and say, “How can I organize this? How can I structure this? Can I put it in sections? Can I title the pages in such a way that it’s still intuitive to somebody to know how to think through it?” So, I think those are kind of the places where I see the biggest missteps. And in a way, they’re small things, but they’re the big things. They are the things that are going to stop people from being able to clearly understand the data, if you’re not thinking through it from an end user perspective.

[00:41:38] LP: That’s really the key, Michele is, what about them? I think Simon Sinek always said it best, “Make it about them, not about you.” That’s why you are absolutely right. The clutter issue is universal. We’ll bring that to every tool. I’m still dreaming of a tool that will have a clutter alert, like, “You are reaching your clutter threshold.” We’re not there yet. But with technology advances, if Google is listening, you never know.

But we don’t always know how to think about it from the end user perspective. And that’s why, again, I don’t get bogged down by questions of, “Is this too many slides?” Because with every single slide or visual or dot I’m adding, I am asking, “What is this in service to?” Is this in service to comprehension, to elimination, to visual appeal? Which is not a bad thing to go for. People do actually prefer to look at information that’s visually appealing, but not at the sacrifice, to your point, not at the sacrifice of the actual information, the accurate, clear interpretation of the information. So, I appreciate that a lot.

[00:42:55] MK: Well, and I have a little bit of an example of that, too, because Data Studio has a limit to the number of elements that you can put on a page, and I have myself hit that limit. Ironically, sometimes it’s not because I’m trying to put too much clutter on a page. It’s because I’m trying to simplify. So, I am doing things like putting a white box over axes that are unnecessary because I already have data labels there. So, they don’t need to have both of them. They’ve already got a guide to read the data. And so, I’m putting something on top, I’m putting text that is like a cleaner simplification, rather than what Data Studio does by default. I’m hiding things. I’m annotating it in such a way and I end up with a lot of stuff on the page. But ironically, it’s trying to make it more minimalist. It just takes a lot of things to do that.

[00:43:46] LP: Right. Because you know what’s going on behind the scenes, and it’s in service to creating something more simplified. Again, it’s not about the number of slides, and it’s not about the number of objects. It’s how things are designed and arranged to make things clear, right? I appreciate that.

[00:44:02] MK: Yes. My very first boss in analytics once said to me that when you’re an analyst, you have to think of yourself as an information architect. That’s your job; to think about how to put together information so that it’s easy to digest other people and that has always stuck in the back of my head. It’s always there whenever I’m doing any kind of work.

[00:44:21] LP: Yes, absolutely. I always say it’s about the skill and intention. Just like if you’re performing surgery, I use this example all the time probably ad nauseam of the scalpel, where a scalpel is this inherently neutral object but if the surgery goes wrong, generally, people don’t blame the scalpel, it’s the skills gap and intention of the person using it with other variables. So, these are all just tools. These are all just scalpels. It requires our skill and intention for using that.

[MUSIC]

[00:45:02] LP: All right, so we’ve entered the next segment of the show which is called The Upgrade. The Upgrade is a tool, a resource, a book, a person, something cool that people can check out that you’re loving right now, or was really important to your journey as a data storyteller, and people just love to check out. So, what do you got?

[00:45:23] MK: I have two. One is, if you are a Data Studio user, one of the things that I both love and hate about it is that it is always changing, sometimes significantly. So, things that weren’t possible a month ago are now suddenly possible because of new features. I would say that keeping track of what gets released is often the difference between being able to wield that scalpel in an intelligent way, because like, “Oh, this is now something that I can actually do.” Whereas before, I was saying, like, “Actually, that’s not possible within Data Studio. You can’t do it.” So, I would say that that is a good thing for people to be keeping up to date on.

There’s also one blog in particular, it’s wissi.fr/blog, W-I-S-S-I. His name is Mehdi, and he has some really creative uses of Data Studio, just like really clever stuff. Oftentimes, when features come out, he will be one of the people that is posting about how to use it. So, I think that that’s a good place.

If you’re just starting out, though, I do have a Data Studio course on the CXL Institute. So, people are welcome to check that out, if they’re trying to get up to speed and learn some of the foundational basics.

[00:46:45] LP: Oh, those are two great resources, and CXL is the best. So, I’m sure it’s super high quality.

Well, those are great resources. I’m definitely going to be checking them out. We have actually arrived at our final question. So, I want you to think hard here, and imagine this very plausible scenario. You’re entering the cooldown of this amazing group fitness class that you’re leading, when suddenly 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 were presenting about and what advice would you give to that person?

[00:47:24] MK: So, my first conference presentation, I remember vividly.

[00:47:28] LP: We all do.

[00:47:31] MK: I was talking about the way that we did advertising forecasting back at Kelley Blue Book, because we had to sell ad space up to 18 months in advance. We had to literally forecast down to the ad level, to be able to tell advertisers what they were buying. So, there’s very direct revenue consequences. And I was trying to explain how we do all of that. I have at one point, in the past, looked back at my slides, and oh, boy, they are special from that presentation.

[00:48:03] LP: I want to see.

[00:48:05] MK: I would probably tell my past self to read a couple books before putting together the presentation, because that would have been a good place to go, and that an internal presentation is not the same thing as something that you’re giving externally at a place like a conference. They’re very different. But I think the mark for me of what makes a good presenter, versus not, is sometimes just the enthusiasm for it.

Obviously you need to have a certain level of skill. You can’t go up there and be talking BS and not know what you’re talking about. So, I consider that to be like, the bar is that you know your topic and what you’re talking about. But when you actually care about it and are excited about it, you bring the audience along with you. I think, being able to harness that, when delivering a presentation and being able to say to people, I’ve done lots of presentations on Data Studio, and being able to say, “This is a really cool thing that you can do with it.” It does engage people in a way that no matter how knowledgeable you are, if you’re not excited about it, it doesn’t come across in the same way.

[00:49:15] LP: That’s right. And even if you’re a great speaker, and not knowledgeable or you’re a great speaker and knowledgeable without that passion, that passion is really the thing that engages and unites and draws people in. Because people want cool stuff and they want to know that you find it cool when you’re sharing these things. That’s why I think public speaking skills are such an amazing value add in terms of something for data practitioners to invest in themselves, even though it feels like not a critical skill to have in their toolbox.

That’s great advice. I’d probably give myself all the same ones. But Michele, unfortunately, our time has run out. It flew, which means we must have had a blast. I know I did. So, please tell the listeners where they can keep up with you.

[00:50:05] MK: Yeah, I have a blog on the Analytics Demystified site. You can follow me on Twitter. I will certainly confess I have not been as talkative as I have been in years past. Life has gotten away from me a little bit. You can find me, I’m on all of the usual interwebs. So, blog, Twitter, LinkedIn, all of those places. Feel free, if anybody has questions, definitely reach out to me. I think I’m pretty accessible.

The other place that I spend a lot of time that they may want to check out if they haven’t already is #measure chat. So, it’s join.measure.chat. It’s a community of – it’s now well over 15,000 analytics professionals. It’s a lot of people. But some of the smartest people are there, and whenever I have questions, like that’s the first place that I go, and you can find me there as well.

[00:50:54] LP: That’s the Slack channel?

[00:50:56] MK: Yes.

[00:50:57] LP: Oh, it’s known as #measure chat now. I know, I gotta get back on there, too. It is an incredibly lively resource for sure. Great, well, all of the links that she mentioned will be available on the show notes page for this episode. Michele, what can I say? It’s amazing to see you after the last few years, and I’m really hoping that our paths cross again at a future conference now that things are actually starting to be on real stages again. I appreciate all the value you bring to this field and inspiration as a woman especially, and I hope to get see you again soon.

[00:51:32] MK: Thank you, you too. And thank you so much for having me.

[END OF INTERVIEW]

[00:51:44] LP: Okay. Well, that was an interview 10 years in the making, and it didn’t disappoint. Michele’s breadth of knowledge is unparalleled in this field, and I’m so glad you got to enjoy it. It’s now a real milestone to have hosted both Kiss sisters on the show. The other is Moe Kiss, host of Analytics Power Hour. So, if you haven’t checked that out yet, you must.

So, to catch all of the links to the takeaways and resources mentioned in this episode, visit the show notes page at leapica.com/083. If you’d like to connect, don’t be shy and reach out to me on LinkedIn or Twitter, and be sure to send a connection invite with a note mentioning the show. I love to meet listeners and I respond to every message.

I’ll leave you with today’s data presentation inspiration by Jagat Saikia, and that is, “The effectiveness of data visualization can be gauged by its simplicity, relevancy, and its ability to hold the user’s hand during their data discovery journey.” I love this quote, and I think it’s so applies to dashboards. I believe a well-designed dashboard puts your decision-makers in the driver’s seat of the most important and basic decisions in their business. So, here’s to creating an effective data visualization in dashboards to guide them on their road to business victory. That’s it for today. Stay well and Namaste.

[END]

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How to Keep Audiences Engaged during Boring Business PresentationsPresent Beyond Measure Ep. 082This is a snippet from my upcoming book, The Ultimate Story-Driven Data Bible. To be the first in line to get updates and goodies, be sure to join my waitlist!5 Secrets to Keep Your Data Presentation Audience EngagedEPISODE SUMMARYThis episode showcases another top-secret book snippet where I am sharing my top techniques and secrets for getting a business presentation audience to stay engaged, entertained, and enthused about your analytics and marketing insights.

I share the two most important questions I ask myself from my audience’s perspective before presenting, and three cinematic data storytelling techniques you can use to create suspense, anticipation, and rapt attention.

It’s a top-secret snippet from my upcoming book, The Ultimate Story-Driven Data Bible! Click here to get on the book waitlist to get exclusive updates, goodies, and more.

FULL EPISODE TRANSCRIPTChapter 4.8: Beast Mode techniques for maximum presentation audience engagement“Cinema should make you forget you are sitting in a theater.”
~ Roman Polanski

Several years ago, I was pinged by my fellow analytics expert and dear friend Tim Wilson (aka Gilligan on Data aka The Grumpy Cat of Analytics) to do something I’d never done before. He asked me to co-present a session at a popular analytics conference. Now, I tend to be a lone rider on stage, so this was a stretch mission for me. Since we were sharing a time slot, it meant I had to drastically cut content from my signature keynote.

The issue was, I didn’t cut nearly deep enough, and I awkwardly realized this during the session. There was no clock in the room and my screen was too far away to watch the time.

Suddenly, Tim called out and informed me that we had gone over our time limit. Like, not a little, but way over time. I nearly froze in shock; this had never happened to me before. I groveled to the audience and sped through my remaining content. Thankfully, The audience was quite forgiving and responded warmly to our closing.

Nevertheless, I left feeling really down on myself; as a professional speaker, it’s a big no-no to run over your limit. I deeply respect my audience’s time and conference organizer’s agenda. But there was a silver lining peeking its way out of my raincloud: I didn’t notice I was over time because no one had got up and left.

The next day, I conducted a post-mortem on my talk as I always do. Playing back the mental tape, I gathered two important insights:

First, I needed to prepare more effectively to streamline my content and hit my mark. And Next, to keep a clock nearby since there wasn’t one available. I did not want to earn a reputation for blowing past my time slot. But it was the third learning that gave me a lot of encouragement:
I presented at a level where my audience lost track of time.

For most conference sessions I’ve attended, a certain percentage of the audience typically “defects” in the last ten minutes or so. This is often the result of uninteresting or unclear conclusions. And if the presenter is truly boring, sometimes you’ll observe an exodus as early as halfway through.

Audience movement is an excellent gauge of a presenter’s ability to hold attention. I’m lucky to report that I typically witness very little movement during my sessions. This isn’t meant to brag; it’s just an observation that tells me something is working, and I believe I know what.

When we think of social gatherings like parties, weddings, and concerts, notice that they center around one thing: entertainment. Same thing with storytelling: books, movies, and songs are all forms of entertainment, agreed?

I realized that the corporate meeting is one of few social gatherings with no entertainment of any kind. Could this provide a clue as to why we despise them so much?

I pondered, what would happen if we used elements of cinematic entertainment in our meetings? Could that lead to longer attention spans and better engagement?

Now, I’m not suggesting you do an interpretative dance at your next readout (although I did once hear of an analyst who would operatically sing his insights to his executive team to wild acclaim). I am suggesting that your meeting doesn’t have to be completely devoid of intrigue.

I studied various cinematic storytelling techniques to find out what keeps audiences on the edge of their seats. Now, I have a secret. All the information in this book will not only benefit you in the conference room but on the conference stage as well. There’s something I learned while becoming a pro speaker that will help you whether you’re making it rain insights in the conference room or the conference stage.

Two Questions Your Audience is Silently Asking YouI notice something interesting in most data conference sessions I attend. The content is usually quality, and the topics are relevant to the audience. There’s a lot of brainpower on those stages!

And yet, I find myself walking away, time and time again, without a clear understanding of what I was supposed to walk away with. I feel unchanged and uninspired. Here’s the thing:

The more and more presentations we sit through and aren’t moved or changed or inspired by, the more of our life we never get back.

That’s not how we want our audience to feel about our sessions, right? So, over the last 20 years that I’ve been attending and creating higher-stakes presentations, I’ve identified two important questions our audience is silently asking us when they show up to the room.

If you answer these questions for them, I guarantee you will be blown away by the audience’s response. Let’s dive in:

Question #1: Why am I here?
As in, why should I come to your session, especially if I have a choice between you and someone else? Conferences are busy, distracting places and often have simultaneous session tracks, so why should they choose you? Why is your session a good investment of their time?

Often, a well-meaning and knowledgeable presenter gets up on stage and projects slides, yet their content isn’t speaking to the audience. They’re transmitting information, not facilitating transformation. It is answering this question that separates talkers from speakers.

The last question you want to hear an audience member ask someone else at your session is, “why am I here?” This is the best way to avoid that.

Question #2: What can I do differently starting tomorrow?

It sure is nice to hear that someone thought your session was “great” or “amazing”. But for me, as a presenter, I want to know that I hit my mark. I want to know how my presentation will help my audience in the future.

That’s why I pack my sessions with practical tools that people can get started with right away. I do this because I found that after most sessions where I was the audience, I didn’t know what to do next.

So whenever someone tells me they enjoyed my session, I reply with a gracious “thank you” and one or more probing questions like:

• “What resonated for you the most?”
• “What is the one thing you’re going to do differently starting tomorrow?”
• “What’s your biggest takeaway?”
• “What’s something that you learned that you didn’t know before?”

Doing this has several benefits. First, you make them feel that you genuinely care that they got value from your talk. Next, it helps you understand what tool, practice, resource, or mindset made a lasting impression.

Make your presentation goal that they have something new to try right away. The trick is learning how to present it in a way that they’re going to remember and integrate into their work.

Focus on these two inquiries, and your next session will undoubtedly rank as more memorable, actionable, and inspirational.

How we typically open our presentationsI want you to close your eyes and imagine this scenario with a well-known story I’ve already mentioned:

You’ve finally set aside exactly two days, 22 hours, and 15 minutes to binge watch Game of Thrones. You’re all snuggled up under a blanket, you fire up the Fire Stick, and prepare to get lost in the land of Westeros. But instead of the spellbinding preview daring you to change the channel at your own peril, you see a white screen titled “Agenda” with a list of bullet points that reads:

• “Introductory recap of previous episode
• Dramatic, sweeping medieval events set to dramatic, sweeping medieval music
• A few naughty bits
• Gratuitously violent ending
• Musical credits”

Then the screen refreshes with a new slide called “Executive Summary” with a new list:

• “This show is about dragons.
• The throne is made of a bunch of dead people’s swords.
• Any character you care about will likely die by the end.”

(Sorry if that was a spoiler in case you’re planning a GOT bingefest). Now ask yourself: how excited are you to watch the show? Does revealing everything this way up front make you more interested to stay, or tempted to switch to The Witcher instead?

This one isn’t hard to answer. Let’s first explore one of the ways we lose our audience’s attention the fastest, and see what alternatives are available.
How to invoke suspense

There is another underutilized and powerful cinematic technique that is also incredibly simple. But you must have the courage to try it.

I’m going to use it on you right now.

Would you like to know what it is?

I’ll bet you do…

[waits, leans forward which prompts you to lean forward]

It’s…making you wait! Was that a weird and tense reading moment or what? And it’s exactly how I planned it, by invoking suspense! Waiting and suspense are fundamental storytelling devices; whether it’s the beginning of the next episode in a high-octane television series like 24, or the simple turn of a page in a children’s bedtime story, we are a species who loves to hate to wait.

Invoking suspense by making an audience wait to hear what you say next is extremely effective for two reasons:

1. For those who are listening, waiting triggers suspense which activates the brain
2. For those who aren’t listening, the momentary pause alerts their brain to pay attention and then triggers suspense, which activates the brain

In an article by Kim Eckart of the University of Washington, she explains how anticipation works to activate the brain through an executive function called “selective attention”:

“‘Executive function’ is a broad term that encompasses various skills necessary for organizing information and controlling one’s own behavior. Selective attention — the ability to focus on a specific thought or task at the expense of others — is an executive function skill related directly to anticipation, because it involves knowing what to expect of an event, however small, and how to respond to it.

Anticipatory brain activity prepares for the future, making incoming information a little more predictable so it’s easier to focus attention on what is important.”
This means that anticipation grabs attention so that an audience member can prepare themselves for the future, which diverts them from anything else in that moment.

If you’re curious, that little waiting game was inspired by my friend Nir Eyal, neuroscience expert and bestselling author of Hooked and Indistractable. Several years ago during a digital marketing conference, I watched him use this exact technique to great effect and I never forgot the audience’s heightened reaction.

So the question is, how can you leverage the cinematic power of suspense in your business readouts?

Three Ways to Make Them WaitSuspense is an extremely effective tool for keeping an audience’s attention engaged, and there are several options to use it in even the most mundane-appearing work meetings.

#1: Before A Big Reveal
As you read earlier, one of my favorite techniques is to split one of my baby storylines over multiple slides. I’ll set the stage by showing a data point with an expected result that isn’t shocking, and then reveal a surprising turn of events.

I might say something like, “When we initially looked at our paid search campaign, we weren’t surprised, everything looked fine. Everything was chugging along. But when we looked a little deeper at our outdoor furniture campaign, we found something that surprised us.”

And then…I make them wait for a long beat. One that will feel almost too long, which will snap them back to attention.

Then you advance to the next slide and reveal that the landing page is losing 80% of your visitors and needs immediate remediation. Now, no one misses that point because they were secretly shopping a Zappo’s flash sale instead of listening to you.

#2: After a Big Reveal
The second technique I use is to make them wait until after a big reveal. I learned this from Good Charts by Scott Berinato. In it, Scott Berinato says to use this in a situation where you might have an important insight that you want to land really hard, like the proverbial piano falling from the sky.

In the paid search example above, it would go something like this:

• You’d still set the stage the same way with “When we looked a little deeper at our outdoor furniture campaign, we found something that surprised us.”
• But this time, you’d foreshadow that the situation is dire, with something like, “We learned something that we believe is critical for us to address.”
• You click to advance to the next slide immediately, and announce that “We found that 80% of our landing page visitors abandoned the page right away.”
• Now take your dramatic pause. Boom! That is a big problem, and the pause afterward allows the gravity of the information to sink in the way you desire.

#3: Play “Guess the Outcome”

The third way I make my audience wait is to have them guess at a particular result or outcome before I reveal the answer. This is a fantastic tool if you are in the landing page or conversion rate optimization fields and you’re running lots of tests.

What you can say is, “Okay, here were our two best contenders. Who would like to take a guess at which one was the winner?” or “Show of hands for A as the winner? B?”

This strategy leverages gamification to increase attention; the audience will love their playful involvement, because play activates the brain’s signaling systems and elevates the nervous system (in a good way). You relish the pause and ask, “everyone ready for the answer?”

You then reveal the answer slide and triumphantly announce the winner! The audience’s attention is now your invisible trophy for such stellar storytelling.
As you can see, there’s a lot more to data storytelling than even the actual story and the data. Doing this right is truly an art and a science, but it’s not hard. You just have to know the techniques, and put them into practice.

So how do you know you’re doing this right?

When your audience loses track of time. That’s how you’ll know.

CHAPTER RECAP:• The three most common phrases that open live business presentations are also the most boring.
• Invoking suspense by making your audience wait is a powerful tool for snapping the audience to attention.

Where Lea is Speaking Next:I’d love to meet you, in-person or online! Here are the data storytelling, analytics, digital marketing conferences and events I’ll be speaking at:

There are no upcoming events.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!

Now, I’m going to ask two favors from you:

1. If you’re digging the show, don’t forget to hit the Follow buttons on iTunes or Spotify to never miss an episode.
2. If you liked what you heard, I would love if you could leave me a rating or review in Apple Podcasts. 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, always remember: viz responsibly, my friends.

Namaste,

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A five-step method to craft and deliver powerful, influential recommendations that empower business decision-making using a popular goal-setting framework.

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Listen as analytics trainer and speaker Nidhal Firoze shares his top tech tools for creating engaging presentations, maintaining the interest of audiences, and communicating clear ideas.

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Listen as Christina Stathopoulos describes how effective data presentation fueled her meteoric rise as an analytics speaker and her advice to practitioners.

The post How Christina Stathopoulos Presented Her Way to Data Analytics Stardom appeared first on Lea Pica | Data Storytelling Instructor.

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Listen as Anna Shutko showcases the incredible analytics benefits of the Supermetrics platform and all about their own podcast, The Marketing Analytics Show.

The post How Supermetrics Will Clean Up and Speed Up Your Data Analytics Work appeared first on Lea Pica | Data Storytelling Instructor.

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Listen as Rehgan Avon of the Women In Analytics Conference describe her incredible annual gathering of data practitioners and experts and why it should top your attend list.

The post Why You Need to Attend the Women in Analytics Conference in 2020 appeared first on Lea Pica | Data Storytelling Instructor.