Scott Berinato's Best Data Storytelling Advice Ever
Data storytelling is a phrase ripping through the digital marketing and analytics spheres like a dust devil in the Sonoran desert. Unsurprisingly, there’s no shortage of data visualization and storytelling articles, experts, and communities telling you exactly what to do and what not to do.
Sometimes, they don’t all agree, and sometimes, it gets ugly.
Then there’s Scott Berinato, head honcho of data viz at the Harvard Business Review. Scott stands apart in the ocean of data viz gurus in that he’s not here to teach you the right and wrong way of data viz.
He’s here to show you what’s worked for him, and equip you with tools to find your own way. And it’s because of his balanced and approachable philosophy his work has had a profound influence on my own data storytelling philosophies and teachings.
Scott Berinato is a self-described “dataviz geek” and Senior Editor at the Harvard Business Review. He created the successful “Vision Statement” department in the magazine, has written and edited many articles for HBR and other top business and tech-related print and web publications.
Beyond that, Scott is the author of a book on data storytelling that has quickly become one of the most-recommended and most-cited books in my workshops and speaking sessions. The book, “Good Charts,” helps people turn plain and uninspiring charts into effective and smart visualizations that convey ideas powerfully.
Scott is also a champion for creating more compassionate and heart-centered community around socializing and critiquing data viz, which speaks directly to my mission to help make the data viz world a kinder place.
In this episode, Scott breaks down the most important concepts of data viz and creating good charts while also explaining the significant role storytelling plays in presenting data to your audience.
In This Episode, You’ll Learn…
- How he differentiates between a well-built chart and a good chart by determining whether or not it conveys an idea to the audience.
- His method for developing the Quadrant Model, his “good chart” matrix.
- The tools he uses most frequently for his dataviz work.
- How he finds business success by developing and supporting a team model.
- How he uses colors to convey his message, without overdoing it.
- His view on data and storytelling and how they are connected.
- Several of his best practices and mindsets that help him achieve success.
- The resources that inspire him, like the Washington Post article about the Trade War, with nearly perfect visualization.
People, Resources, & Links Mentioned
- “Good Charts” by Scott Berinato
- Present Beyond Measure episode with Garr Reynolds
- Alberto Cairo
- Storytelling with Data Podcast with Cole Nussbaumer Knaflic
- Power Tip: If you subscribe to #dataviz and #datavisualization on Twitter, you can find a wealth of information that will inspire you.
- Also, check out the 1912 book by Willard C. Brinton called “Graphic Methods for Presenting Facts.”
How to Keep Up with Scott:
Thanks for Listening!
Thanks so much for joining me. Have some feedback you’d like to share, or a question for Scott? Leave a note in the comments below, and we’ll get back to you!
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If you liked what you heard, I would love if you could leave me a rating or review in iTunes. Ratings & reviews are extremely appreciated and very important in the rankings algorithm. The more ratings, the better chance of fellow practitioners getting to hear this helpful information!
And finally, don’t forget to subscribe to the show on iTunes to get automatic updates and never miss a show.
A very, very special thanks to Scott for joining me this week. And as always, viz responsibly, my friends.
Do you have a burning question for Scott about creating good charts, storytelling with data, or his upcoming “Good Charts Workbook?” If so, ask away!
[00:00:00] Hello, Lea Pica. Here, today's guest is the author of my favorite data, story-telling book of all time, Nuff said. Stay tuned to find out who's making it rain knowledge on the present Beyond Measure Show. Episode 35.
[00:00:16] Welcome to the prison. 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 your in the right place and now your host Lea Pica.
[00:00:42] Hello and welcome to the thirty fifth episode of Present Beyond Measure, the only podcast at the intersection of presentation, data visualization and storytelling and analytics. This is the place to be if you're ready to make maximum impact and create credibility through thoughtfully presented insights. Whether you're a digital marketer, analyst, SVO, a CSIRO or CRM oh, you are in the right place for ticket telling, compelling and inspiring data stories. If you're interested in getting a really deep dove into my prescription for data storytelling awesomeness, be sure to scroll to the bottom of the show notes page of this episode to sign up for the piqua protocol. It is a super practical and approachable, prescriptive approach, a set approach several times to telling really awesome data stories that inspire action and really communicate the value of your work. And I'd love to hear what you think about that. I would also love to see you join me at the Digital Analytics Hub in October. It's the Premier Analytics Conference in the country. By the end of my workshop and keynote, you will learn how to plan, design and deliver your data story in a way that informs decisions, galvanizes your stakeholders into action and makes you the hero. Seats are really limited. It's a very intimate conference, so you don't want to miss the chance to get this valuable information in your hands. At this rate, so you can learn more and sign up at LEHA Peak AdCom slash DKA hub. Oh man, I am so fired up about today's special guest. I found this guy almost by accident and his work has been so profoundly impactful that he's actually shaped the growth of my teachings. I am psyched to bring him to you today, so I'm not going to waste any more of your time. Let's do it.
[00:02:44] Hello, and today's guest is a self-described gate of his geek and a senior editor at O! I don't know, just the Harvard Business Review. And in addition to creating the successful Vision Statement Department and magazine, he's written and edited many articles for HCR and other top business and tech related print and web publications. But the reason he's here today is he is the author of a book on data storytelling that I not only devoured Harry Potter style. And that's saying something for business book. I couldn't put it down, but it quickly rose through the ranks to my most recommended most cited books in my workshops and speaking sessions. The book is called Good Charts Nice and Simple. And being able to create them is exactly what he helps you do. And it turns out he's also a super friendly and approachable kind of guy. So with that, I'd like to introduce you to Scott Baron Nado. Welcome.
[00:03:44] Thank you, Rick. How you doing?
[00:03:45] I'm great. Thanks for asking.
[00:03:47] And so the book, is it art supplies?
[00:03:49] Yes. Yes. And here this goes everywhere with me. And it exceeds my luggage weight allowance. But I don't care. It's it's amazing. So, Scott, I discovered your book by accident as a recommended author on Amazon. And I was like, how did I never hear about this? And I'm not sure I'll ever be the same. So, you know, I'm using your teachings in my workshops and blog posts and things, and I just want to get right into it. It's great. So you start the book by making a key distinction between a well built chart and a good chart.
[00:04:29] So they're not necessarily the same thing. What is a good chart, Scott?
[00:04:34] Sure. And this is sort of the entirety of the reason I wrote the book was because I felt as I sort of did a lot of data visualization as I go to resources that a lot of the really focus on the chart making. No, always use this type of situation. Never use this type. Use these colors. Don't use these colors to do a little script, a little sort of. Not helpful, because what I was discovering is I could create really nice looking charts, but they often didn't say what they needed them saying you still aren't getting it. So when I say good job, really what I'm talking about is you get the context right. You know what the audience needs. You know how it's going to be delivered if it's on the screen or on a piece of paper. And that tells you sort of how to build it and what to build. And you just focus on the ideas you want. You get across. I think one of the cardinal sins or one of the big mistakes a lot of people make when they make the charts is they just visualize the data. You know, they have some data in a spreadsheet. They tell it to take this column in this row, make a line chart. And that's not really what we're doing. Using the chart, we want to get an idea across. And so that sometimes involves the data plus other things. Sometimes it involves subsets of the data and false calculations. There's lots of things you can do to bring an idea forward beyond just visualizing the data. So good charts really for me or anything. That's context that understands its context. And I tell people what I do speaking jumps to know. I'd rather see a hand sketch chart that says exactly what's supposed to say. It is somewhat messy, right? The perfectly built beautiful chart that doesn't say the right thing.
[00:06:06] Mm hmm. That's such a great point. And I heard you talk about context, and I thought you gave a great example of a really well-built chart that totally lands flat because the presenter wasn't taking into account the specific experiences and biases and preferences and needs of the audience that they're presenting to. So do you feel that the audience needs plays into a lot of what makes a chart good?
[00:06:34] Completely. It's almost the entirety of what you're doing. So an audience gets the idea they need, right in only the idea because what happens and we had to do a lot of research on a lot perception to understand this. When you show something visual, someone, their mind immediately goes sort of into overdrive to understand what it is they're watching. And the easier you can make it for their minds to get to the idea, the more they're going to actually learn and retain that information. People to turn up there, it's got forty five data points. It's got a lot of labels, it's got lots of color. It's got pointers going everywhere. You know, maybe there's some notes on the side, seven bullet points underneath it.
[00:07:14] People are overwhelmed by that. Is where he wants to get it. But it has to do someone's work about where to start, where to go. What does it mean? Should I be focusing on the green thing or the red zone? Right. Hang on there. And so what we have to do is sort of understand how we take these things in, and that allows us to focus down on just showing the show.
[00:07:36] Yes, I love that. And I think Gar Reynolds said, you know, the importance of. Reduce and simplify, I'm missing a step. Reducing what you have and simplifying to the core piece that you're trying to communicate, I think is what we are not empowered with as a skill. When we think in men. Yeah.
[00:07:57] I'm sorry. I think in some ways it's not surprising that I'm doing this because I'm an editor. And if we're really a lot of what good visualization is, is that any creative pursuit? I tell people this all the time. Whether you're a chef or cabinet maker, it doesn't matter. Any creative pursuit really comes down to editing them. You can learn all the skills in the world and you can apply all the skills. But if you're not editing for context, then you know you're not going to have a good outcome.
[00:08:23] It makes so much sense. And now one of the great parts of your book is you have these really helpful visual aids for understanding your concept. So there's a great quadrant model and the beginning for kind of decoding the world of data. So it's the good chart matrix. Can you tell us about that?
[00:08:41] Sure. Yeah. I actually created this matrix because what I found when I started talking to people is they said, well, I'll just find the person who's visual and never have. His problems will be subtle. There's a couple problems with that. First of all, researchers will tell you there's no such thing as a visual thinker. We're all visual thinkers, visions. Why does. So we all have about the same capacity to learn visually. People like enjoy visual, some joy more than others, what we all heard say. But the other thing was a different visualization tasks require different skills, right? So in that matrix, really what I do is I divided on two axes, you know, on the the axis conceptual or data driven. And on the Y, it's either sort of declarative or exploratory. When you cross those things, you get more types of bits, declarative, data driven. That's your everyday database, your charts and graphs and the presentations all. This is what happened. Declarative conceptual. Those are your call consultants corner. You know, rebrands, you know, like arrows just going in a circle saying pyramid's funnels all those kinds of exploratory conceptual is really like brainstorming sessions. Whiteboard sessions come up with the fun.
[00:10:01] How do we come up with the metaphore that's going to visually describe our idea that's really about so siltation skills and really has very little to do with your ability to manipulate software or even draw some interesting script on a whiteboard. You can begin to the bottom cause. Most complicated sort of the most exciting is the exploratory data driven. And that's sort of your data science, your data analysis, your hypothesis former. You know, you've got a lot of data you want to see. Hey, maybe people buy more umbrellas on Tuesdays when it's quality down. If that's, you know. Yeah. And so every one of these quadrants is a different sort of skill set, you know, up in the top right. Where we have the sort of everyday data of this design skills are important. Some data manipulation is board. But when you go down underneath that to the exploratory data driven, the sort of data wrangling is much more important than the design skills. And design skills can work against you there because you don't want things to be overly sudden. You want to be moving fast, iterating so on and so forth.
[00:11:01] Makes sense. Yeah. Yeah. No, I think that's great. And I think it's before I read the book, I was like, isn't everything declarative? And I was like, oh, no. I mean, there is so much exploration that happens. And, you know, one of the first questions that came to my mind is I wonder what kinds of different tools he's using. Is he using the same thing for everything or is there like a tool of choice for each type?
[00:11:24] Yeah, the it is the number one question. I get that I end my presentations now just by saying, I know you're going to ask me, do that. I wish the answer was simple and I could say I use X and Y, but it's not that simple. So I'm going to give sort of a bloviated answer if that's OK. So there is no one tool that does everything well and all the tools do some things well. My last count, I typically use between 20 and 30 tools on a regular basis. There are hundreds. And it depends on what I'm doing, whether it's exploratory or or conceptual or whatever. Having said that, there are a few tools that rise stops. And by the way, the tools that sort of do more well require more training. So if you want to learn Tableau, it's fantastic. You can do a lot of things well, but it's a much steeper workers chip. So in these are not I'm not endorsing any of these guys. I'm just telling people what I tend to use. So I've been using a tool called cloth with P O T ELWA. Mm hmm. And what I find is it's the best kind of every day. Dump some data and get some views out. Get a chart that I can work with. Mm hmm. And then I have a workflow when I've got it to where I want it, where I export it as an SPG and actually refine the design and illustrate.
[00:12:40] Oh, interesting. OK, that gives it those like the journalistic beautiful views that you have.
[00:12:47] They can get it pretty close in way itself, but I have some certain styles I'd like to use. Sure. I've invested in making those styles so sort of temporary. So please, when I use a lot. There's one called exploratory. I use a rope. I use a little bit of tableau. But what I often do is I just Google, you know, different tools that I want to play with and just see how they see how they work.
[00:13:11] I will say and I always say that the most important tools, Google or browser that you know.
[00:13:18] So you can look at pictures of what other people are doing or find dates of his Web sites where people talk about tools.
[00:13:24] They use a pen and paper or some paper. I do a lot more on paper or virtual paper. I've started to use a notepad, sketch, sketch quite a bit. It's called an app called Sketches that uses the pencil. That is really good. But you can get 90 percent of the way they just draw. And I actually prefer that to trying to manipulate in the program. I like to get to my ideal on paper or virtual paper before I start messing with software.
[00:13:54] I am totally with you on this sketching. I don't have a tablet. So I've tried to use online sketches with my mouse and I'm like, oh, damn it, I can't.
[00:14:04] And I'll just just look at what I'm trying to do fast enough, because if I said the book to the key to sketching is to be open a generative to just go fast and be messy about getting it right. Just getting ideas out on the paper that you can look at and define later. So my sketches are you know, I could show you some sketches that were sort of, well, your mind and how messy and incomprehensible they are.
[00:14:26] But the guy. No. I mean, it's it's so true. You cannot filter the brainstorming process. It's totally counterintuitive. But what I love about your book is how many sketches were in there rather than these polished final products and even the notations that you made as you were going through your thought process of. Well, you know, here's our starting point. But I really wanted to know what it looks like if blah, blah, blah. But what if we change and was like, this is exactly how our brain works when we're in a state of inquisition. And I think that's one of the most beautiful arts to analysis that maybe we're skipping over when we're in a rush to just produce something that looks finished. And I. Yeah.
[00:15:09] And I think to the the tools short-circuit that process. Right. Is called click.
[00:15:14] It is where I can hear it and click a button. You get a bathroom.
[00:15:18] It just sort circuits that thinking classes. But I can tell, you know, that idea of just throwing ideas down paper where if we did this, what if we tried? This is exactly how I do it. You always end up with much better results. It's just sort of take the time to do that.
[00:15:33] It's so true. I remember when I was doing a think tech change data visualization course and there was a steffanie evergreen had put out a challenge. I couldn't figure out how to do any of the things I wanted to do, but I knew what they I wanted them to look like. And drawing them and handing them off to someone a bit more skilled than I was actually allowed me to get to the goal. And that's something else that you actually talk about, is leveraging the right resources for the heavy lifting of what you're trying to accomplish.
[00:16:07] So I think one thing that's happened in the industry and as information design becomes more and more important, which I argue has, is people are looking for unicorns in sort of do some design, do some statistics and do the presentation on the subject matter as well. And I have a whole presentation about this. Why I advocate for a team based approach. Shahryar Really subject matter expert, plus data person plus designer and they come different times. And you know, sometimes one person can have two of them. Very few people can handle all three. And I keep a kitchen cabinet of people and we also keep a kitchen cabinet. You can't do and say, help me. Design is what am I doing wrong here or I can't. I can't really get this data sort of wrangle the way I want to chair the supersecret ism, terrible spreadsheets. I'm like, I can I come in? But it's really I struggle with some of the advanced functionality of them.
[00:17:03] So I have friends who just help me. And it's getting easier as the more I do it. But it's it's absolutely crucial that we think about this as sort of a team sport.
[00:17:14] I couldn't agree more. I had the vision pop up of instead of looking for a Swiss Army knife. You're building you're curating a tool belt. Right, from a strenghts perspective. It's so funny because on the storytelling with data podcast, with CONUS Bomber Netflix, she had Alberto Cairo and I was like, oh, I get to hear him. And he admitted that, you know, he actually is very simplistic with. Visualisations he doesn't program or code or do these very exotic visuals, and it occurred to me that people are looking for a Swiss Army knife of someone who knows stats. Someone who can communicate effectively with the stakeholder, someone who can program in are. And I think it's so true that finding a curating a dream team of these complementary skill sets is a great strategy.
[00:18:37] We can't just make it whiz bang an animated and sort of interactive because we want to. There has to be a good reason to show change or to show Dynex dynamic information in that way or to allow the user to manipulate. Otherwise, people don't play with it.
[00:18:53] They sort of put off, by the way, you know that this makes absolute sense. So, you know, one of the things that most common questions that I'm getting and I'm sure you get a hot topic is how to choose the best chart for your data story. You cite some of the decision tree diagrams that are out there. You have a similar one in your book. But what what stood out to me about your advice was how to use keywords during brainstorming to arrive at a choice.
[00:19:24] People love this when I did some workshops.
[00:19:27] They just love it because it's they don't realize what they're doing. So, you know, part of the framework I laid out, which is talk sketch prototype.
[00:19:34] Right. So you start by talking about what you're trying to accomplish in such a context that it really helps out a good friend. Some of you take notes or sometimes a lot of good friends, somebody with no idea what you're working on so they can ask what the really basic questions. So maybe you you're making assumptions you don't know and they can stop you, but you start talking to them just about what you're trying to accomplish. And inevitably, after five, 10, sometimes a little longer, it's usually five or 10 minutes. I have at least a dozen words. They said that either describe it you use or at least start the point in a direction. Right. Interest zone. In the book, I actually match up some of the words you might say to describe your data or what you're trying to accomplish. The types of chart you use and really there's, you know, three or four different categories. But if you look at distributions, scatter plots might think of curves of yes. Histogram, so on and so forth. There are certain words you can using as you talked about your data that would just say this is a distribution. You know, their performance was all over the place and consistency. So, you know, it is just it ranged from this to that. And you hear these words and as you start to learn to capture. It really helps to narrow down your choices because people softwoods on one of two things. They want to try every different type of chart, see which one they liked best or write the books, the neatest charts and Jackson pie chart. So they never sort of get beyond that.
[00:21:00] There's some really basic ways to capture those words and match those words to attempt.
[00:21:06] Now, what's really wonderful, every now and again, somebody says a sentence that describes exactly what they want to show and say, I just want to show a trend of three years where there was a gap in performance. Yeah. And when you see it and I say, well, I can actually drama right now.
[00:21:24] So sometimes in this talking process, you actually end up describing precisely what it is you're going to show and your choices made for you. It's really great.
[00:21:33] You know, I think that's amazing. Things like, you know, I want to know how a group of these things changed over two points in time. And, you know, you can. Oh, dot plot and start to learn about using things like that. Now, one of the complaints that I most often get is that anything but bar charts, I'm so sick of bar charts and I'm I'm on the fence with this because I think the reason it's so ubiquitous is because it works and there's no learning curve. We're trained in how to understand it and it can be impactful. And things like a dot plot might have a bit of a learning curve for people. So what are your thoughts on the boredom chart?
[00:22:14] Boredom? Yeah, I think there are actually I would argue they're not bored with our charts. They're bored with bad budgets. So if you can make the bar chart effective, it's saying one thing and one thing is really well, they're super effective.
[00:22:30] That's why they're still they're incredibly effective for certain things. Now, I tend to I'm a big fan of dot plots as a way to overcome some of the limitations of our bar charts. In some case, it's not at all clustered, right? Yeah, yeah. Clusters. And when you're looking at when you want to compare the values between the bars, you end up with 17 bars and you're trying to compare value to write that 16 kind of scanner. Isaac Boston estimate distance between them. It's harder than it is with a with a dot bot, right. So they have their place. But I think bar charts get abused because they're the number one thing people think of and you end up with, you know, a bar chart with four categories and four bars or. So you have these clusters of four bars plus four categories, you get six different colored bars.
[00:23:18] Lot of times people put the values that are related into the bar top each bar, you know, so you've got, yes, three point sixty three point two three three point one one. Right. All the way across. And then they label them up and they keep all the grid lines. So it's it's this is where, you know, I always talk about context is important. I don't mean to say design is not important at all. I just think people stress about design too much. There are some basic design principles that I put in the book that are not, you know, does not advance design stuff. It just follows.
[00:23:51] It will clean up a lot of the stuff that makes people sort of second margins and pie charts and so.
[00:23:56] Folks, I I totally hear you. If I really have to think back when I really started getting these things right, including the context and the message, I can't remember anyone complaining that it was a bar chart that I used to present my message. I think about it, but it's so true. You can use a beautiful dot plot and if the message in that context and the execution are great to strike out as well.
[00:24:23] That's right.
[00:24:24] And I do like to say to people, you know, well, I don't want you know, I don't want to learn design or, you know, I'll never get to that point where my shots look like yours.
[00:24:33] I don't think that's true. No one. But one thing I always tell them is if you work on the contacts part, the beautiful thing is a lot of the design decisions are made by setting the context. So here's a simple example. You spit out a bar chart from Excel. And it's got 16 bars, four clusters of four. So you are stuck together for a bar, stuck together and you got the four colors. So you get all this color. Now, if you see the bar and think about your context and you say, well, does each year need its own color?
[00:25:01] Right. Is that important for me to get my idea across? And if the answer is no, then you've just made a design decision by setting your context. Right. So now you just make them all the same color. Let's override what Excel told me to do or Excel just did. And what you find is as you set your context better, you get the idea that you want to convey across using some of these design principles. The design sort of takes care of itself. It's pretty magic to.
[00:25:29] I'm so glad you brought this up because it actually skips down to an area I wanted to talk about. I didn't want to focus a lot on the basic design stuff, but color for me is the thing that I do want to focus on for our listeners. So I pulled out a quote and I use this quote a lot and I love how you phrase this. So you say think of color in a chart as a fraction that you need to reduce. Find the lowest common denominator that still preserves the distinctions you need to convey your idea. Paul, this is the best. I found a fraccing it stuffed with that one.
[00:26:07] Once I thought of it, I'm like, yeah, that's exactly what it's like.
[00:26:11] I don't know how I did it. So please tell us more about that.
[00:26:15] Yeah. It's the number one sits and I try not to use that word. Is the number one mistake people make is they think I've got seven marks, I need seven colors. In general, we need a lot fewer colors than we think.
[00:26:28] All right. And so I always tell people, too. There are two main things is to group things as much as you can. Do you really if there's three variables, if you're dealing with seven offices and you really want to focus on two of them, do the other five really need their own color or can they just all be the same color? Right. Because basically there's five variables, but it's really one variable. It's the other one. Right. So always look for ways to group these together. And that's reducing the fraction. And then always think about Gray as your friend. All right. There is information you want people to go to first. You want their eyes to actually move there first. And there is information you need there to compare it to, but you don't want their eyes to go there first. And that information that is sort of provides the context in a different way than we were talking about before. That provides the context in the chart should and can be gray. It's a very powerful tool.
[00:27:20] Once people start to learn how to use gray, I 100 percent agree. And the line I like to think about it because I try to help my students separate, relaying objective observations to create trust and credibility and then using color as a storytelling tool to overlay a more subjective message for. For persuasion. Right. Right. So that's actually what I wanted to talk about next. This was a really standout part of the book for me. Well, they all stood out. But this one I love this one. It was about ethics, state of his ethics and persuasion versus manipulation. So one of my favorite quotes from the book is saying, before you're going to present something to someone, ask yourself, would I feel duped if someone else presented me with a chart like this and use that as a moral compass for how you're going to render the chart ultimately. So. Tell us more ways about how we either intentionally or unintentionally manipulate our message and how we can avoid it.
[00:28:28] Yeah, sure. And I want to sort of preface this by saying it's almost always unintentional.
[00:28:35] Yes, they tend to become well-known, but there are cases where people are intentionally very, very bad, which hurts, right? We don't like them very bad. I just finished I did a presentation at South by Southwest this year called Facts, Truths and Data. There's about this very, very topic. And we talked about some of the very bad actors. But we also talked about the unintentional. And I always start talking about persuasion by telling people and some people don't like this, especially data scientists hate this. But I say every chart is a manipulation. Your chart is some combination of decisions, conscious and subconscious about what to show, what not to show and how to show. Right. And there's no getting around it. There is no objective chart. And that's okay. That's not a problem. We also need to persuade people with our charts. We're trying to separate them from their money.
[00:29:21] We'll get a promo.
[00:29:23] Right. And so we we really need to be persuasive. We have to be responsible with. And I think. As the more I thought about it, because it's getting easy news, you create databases, we need as much data as literacy from the consumers as we do want to see from the producers. I think it's interesting that we as you know, we're pretty good at reading the news and thinking that doesn't sound real or that does sound real.
[00:29:47] We're not as good with visuals that way. We see chart. We want to believe sharks. Researchers call call high activity. They have a very a sense of being true to us, just sort of in our minds just by seeing it. And so we have to overcome that by saying, well, how could they manipulate it? And so some of the ways that happens, either intentionally or not, the most common that people talk about is the truncated Y-axis.
[00:30:13] Right. And the famous translator, Y-axis.
[00:30:15] So if you don't start your y axis at zero, the principle is that fewer values on your y axis, the more distance between changeit, right? So curve's becomes deeper, change becomes more dramatic. Right. And and we have to think about that. Right. Because. Ultimately, people don't recharge thinking about statistics, they don't look at the statistics in the chart. They see a story, right? So they see a curve that goes way up and way down and they think that's volatile or that's a big up in a big down. Right. So our brains process this process these more as narratives than you do. You don't look at things. It started at 0.7 and went up to one point three and then looked down.
[00:30:56] But this is not how our brains might think big change or with no change in flat or rising up and down, all kinds of sort of more narrative way. So we have to understand how people are going to proceed at motor to do it responsibly. I'm not somebody who says never truncate your asses. There are reasons to do in time, too. But you have to know when you're doing it, what the effect is, and you better be able to defend your information in front of you.
[00:31:24] When somebody calls you on and I've seen for where somebody says, well, that looks impressive, but what if we just started at zero? It won't work so impressive. And you have to be able to say, well, it still is a significant change from 1 to 2, even though it doesn't look like as much if we don't truncate analysis.
[00:31:39] So it's right up there, obviously, but I put as much on it on the consumer's learning how to think about these things as I do on the producers.
[00:31:49] I think that's an excellent point. You know, we as consumers, we naturally have a cognitive bias. Right? That's what you're referencing when you're talking about when we observe something goes right through a very unique set of lenses that are completely unique to our own experiences, our wants, desires, fears. And we're overlaying our own emotions on that, which is why I think that news outlets are so successful that that sort of thing. And that is exactly how I pictured it, telling people like make sure you can defend this in a court of law and that you can sleep at night. You know, if you can sleep at night, then you're on the right track. And the example, because I'm still trying to wrap my head around the difference between bars and the difference in the slopes, in lions and things. But I guess the best way for me is I think of a type of body temperature. You know, you're going to look at a body temperature over time and it will look flatline if you keep it at zero. But if you zoom in on that specific range, you're going to see changes that actually mean a lot in the context of the data. It's a tiny change in temperature, means a lot to a person who might have been sick that day.
[00:33:04] So that's right. I did use my blog post about that because that's something I actually read.
[00:33:10] Oh, really? No.
[00:33:13] So I wrote about the sort of delusional flatlines, OK, because there was a famous chart, those tweeted out. And again, I don't care about people's politics, but it said the only climate change chart we need to see and it was a flat line that went from, you know, was the world temperature. The average global temperature temperature over 100 years and was a flatline.
[00:33:32] They are the axis is zero. I went to one hundred and fifty degrees. Really only changes a degree and a half. That's a really significant degree and a half. Yes.
[00:33:41] And so that's where I talk about this sort of gestalt principle, pregnant, which is we look at a flat line. We don't look at the statistics and say flat mean sea life means a flat means change. In fact, something's changing. And to prove that point, I did a body temperature line exactly like that and labeled it with normal bottom body temperature on one end and on the other end sort of approaching death.
[00:34:06] So you're right on with that. The sense is that we had this is you becoming a good consumer of charts and understanding sometimes flatlines means, you know, I am a good consumer of choice and everyone listening will be now, too.
[00:34:21] It's great. So I actually I want to get to another part of the book that really stood out to me and sort of the ocean of data viz books that are out there. This made it unique. It was asked on really what telling a story with a data means. I've never seen so many clear, detailed approaches to this. So my favorite tool that you mention is creating tension, you know, and giving a more cinematic experience to the people 'cause my god, we get them in a room. We should entertain them a little bit. But can you talk about the benefit in creating tension and how they can go about doing that?
[00:34:57] Absolutely. You know, storytelling data. This is a big topic in the attention and some of the other techniques I talk about are really about presenting stories.
[00:35:05] So I'll talk to them in that context that it's such a good way to keep people's brains engaged because we are fighting when we're presenting data. We are fighting everybody's impulse to look at it, to read it, to stop listening to us and to try to figure out themselves or to look deforms because they're bored or if they look up and they see a massive chart is just going to give up after about 400 milliseconds. No, you don't. But when you use. This idea of creating tension in the way I sort of describe it in the book and when I do presentations is to watch into a room, I can't say that I'm gonna walk into a room with all your colleagues and you stood in front. You said Twinkle, twinkle. Well, Star. What's going on there? Your brain seeks resolution, your brain needs to resolve that melody. And so when you create tension in a chart where you say, here's some information that I've made it easy for you to understand that something's about to happen to that information and you withhold it, which them to become engaged because their brains want to know what happens.
[00:36:14] They start gesell. Sometimes they start guessing what amount to an interesting was that it went down. I bet. I bet we beat expectations and you can really use that to keep an audience engaged. But to do that, you have to understand where and when to break down the chart, where and when to sort of break the story up. Where's the setup? Where's the conflict? There is no resolution. And that's sort of when I talk about storytelling there. That's all I focus on. I think the whole storytelling in the world has gotten so overwrought with story points and it's really just following story format, which is as old as time it is. There's a set of some reality. It's conflict changed the reality, which, by the way, can be positive. A promotion is a conflict resolution, a new deal. Right. And if you can stick to those three things, you can do wonderful things. Because once once you start thinking that way, you look at charts, you can actually you oftentimes see that set up the conflict resolution in the chart itself. And then it becomes three jokes or it becomes five.
[00:37:16] Right. And I think these builds are so crucial when we present in terms of teasing out that information bit by bit. You know, I love the big movie buff and I love looking at movies and TV shows for those kinds of techniques that they used to keep us hooked. And think of every TV episode of a drama. Does it ever really wrap into a nice bow at the end of each show? No, of course not. It's a cliffhanger and it leaves you coming back for more.
[00:37:47] When I when I do when I do talks, I always say this is why HGTV is so successful.
[00:37:52] If you watch a show, they're they're redoing the house. Right. And they show you little bits, but they never. And you need the reveal.
[00:37:59] You're looking us the narrative part of you.
[00:38:03] And it's just a feeling thing craves narrative. Yes. Needs that resolution and the keep teasing.
[00:38:08] What we're going to show you, the video we're going to show you to reveal and that's it. You can do the same thing with charts. You know, we can all be Chip and Joanna gains have solute me.
[00:38:16] And I love those three steps. And what I'd love to overlay on that in addition in storytelling capacity is really interesting characters. Right, that make a great story. And for me, I like to think of the characters as the audience is the hero. You're going in there to help them, to guide them through a story. The challenges the conflict is in a way like the villain. And you are their guide. You are like Yoda to their Luke. And that is a winning combination for sure.
[00:38:49] And if you think of your job is to take them along the journey, you're going to create good stories. And it's going to be fun to have the information they need. And if you can control the flow of that information, stop at the right times, go at the right times, keep them guessing, keep them asking questions. All of those techniques, the pausing works really well. I like to speak thingswhich a little bit called the word procedure. OK. You say something like a classic example I use. The book is, you know, you show them a chart that's empty and say this shows how many jobs were lost when robots were deployed to manufacture. Right. What did you expect to see? Right. And everybody will say, I expect to see more job losses with more robots and children. You say that this is reality and then you slip to reality, which is not that at all costs do not correlate to robot deployments and it increases moment of dissonance meetings where they have to say, I thought it was going to be this other thing. Why is it not? And now some more were talking about the ideas, because zombies, you have to resolve this cognitive dissonance that. Right. This is not the reality I expected. And you only get there because you showed them what they wanted to see, what they thought they'd see, and then show them reality and they have to resolve them. And that's a case of, you know, like you were saying, you're there, you're carrying them along the journey and telling that story in such a way that they are forced to think about.
[00:40:18] Why isn't reality the way I thought?
[00:40:20] Well, yes, I can see that. And I love the idea of letting them get a little comfortable and then ripping the rug out, because a second people, I think comfortable in meetings is when they start to tune out and think of other things.
[00:40:36] So I would just add if I could just. Yeah. You know, the beginning the book, Chapter 2 is about visual perception theory of things. And some people thought I was just doing that to show that I was serious and I did all my research. I think new important chapters and people understand how people see charts and things because those things are if you understand how people are looking at them, you can control the narrative in that situation. You understand what happens when you show them this color or that if you show them something very busy or show them something very plain and simple to understand how people are taking this. And it's really the first step in understanding how to craft the narratives that are going to, like you said, rip the rug out, keep them engaged, keep them coming back to get some songs.
[00:41:15] All right. Love it. So this is probably if they're all my favorite section, honestly. But one of my favorite messages that really came through was sort of an awakening that I've had as someone responsible for teaching others in this space. You have a lot to say on how we critique the work of others. And there's a lot of dogma in this field about, like you said, what's right and what's wrong. And this sucks. And I I liked your article on how negative feedback is rarely productive and their sites like WTS Ivies and they almost offer up people who worked on something, they produce something and then it serves them off for like a public lynching. So I'd love to get your thoughts on this.
[00:42:03] Yeah, I very put off by a lot of what goes on and especially on Twitter, too.
[00:42:07] You know, we're just the public shaming charts. Everybody's trying. Nobody's brought up to learn how to do good database.
[00:42:15] Information is information.
[00:42:18] Design is very new and they're using the tools they've been given, which are not particularly good often. But right now, I'm saying this spreadsheet programs and others insist they're not built to do this well. So I think some of that is is really frustrating to me because of two things. One is it's just not nice. Right. We should be nice people. But two is there's no such thing as the right choice. Right. Right. Is like I could show you can show me the chance that this one's much better. So let's say, OK, in a situation, I could see that. But there's a lot of situations where that's a terrible choice. Then it's all about context. Right. And so I think this sort of public kind of criticism just isn't valuable. So I do my best.
[00:43:01] I can't say I'm I'm upset to no one, not as a publicly to say this is terrible. Yeah, no, this is bad.
[00:43:09] I say what I find challenging about it. I say what I think I would change if I were to do it or I say this part didn't make much sense to me as sort of a classic design create, which is about making the product better, not making the person who made it feel bad. And in the book, I took great pains. In the upcoming book, I take great pains to never that now.
[00:43:32] I really tried to resist, do and don't.
[00:43:36] I try not to say do this and don't do that. Especially don't. What I say is try this or this may be a better approach or I found that this works because that's where a lot of this comes, is that people were brought up to say you never use charges. It's just as I said, you know, I had a friend of mine who truncated an access and somebody to do them said.
[00:44:00] Your truncated laughs this is a thought crime, and I thought, well, then, so this is.
[00:44:07] So I think this is a really important subject as visual communication becomes more and more common, as the Internet becomes more and more violent, some places like this sort of reasonable discourse really has to be has to be it has to be anybody who's participating in this world should really be thinking about how their insults come across and how their critiques come across.
[00:44:32] Just because you can make a better one does mean the person didn't try. And then maybe yours isn't always the better one.
[00:44:37] So absolutely. I think your viewpoint on this is so important and some albeit a bit rare in terms of creating a community, a global community of collaborative visualizes and teachers that want to help each other level up our games because we're all going to benefit from that. And I loved a framework that you had in the book about instead of saying, well, this sucks because of X, Y, Z. But making it more internal and subjective and listing out, OK, what do I like? When do I dislike and what would I change? That's right.
[00:45:15] These things and that's classic design. And the key for me to doing that. I do that all the time. And once you start doing this, I'm a bit of a nerd.
[00:45:24] You know, we like notes here.
[00:45:25] Sometimes you realize I don't have a better answer than what they you know, yeah, I got like this, but I don't know how I do it, you know?
[00:45:32] But the key for that step process to me is to react to not think too much. You know, look at and say, okay, what do I like about it? And as you do that and you just sort of react to it, you find yourself and you and I always say good things about it's never just say this is terrible, period. You know, there's always something redeemable. There's always something irretrievable. But to react instead of thinking it through, because you start seeing it through, you start digging out the Democrats. And that's not you don't like us said we don't need statistics. You don't read charts academically. We respond to a picture and we try to form a negative out of it. So I think that's very effective. You know, there's other sort of techniques like that. But the key for me is the form. You're doing it in small groups, not on Twitter.
[00:46:22] And to be humble are to realize that, you know, you don't have all the answers. And you may not. You may. Like I said, sometimes I say I just I wouldn't do it that way, but I can't think of a better way to do know so humble about it and just realize, well, we're all trying. And sometimes we do well, sometimes you don't do don't do well. And I've you know, I've made my fair share of really terrible charts.
[00:46:42] So no, I don't. I mean, I think there's some classic communication skills in here as well. Taking a pause before reacting is just a tool for life that I'm far from mastering myself, but I'm getting there. But I think taking a pause to reflect and also staying curious. This is a big growth area for me as well. It's like before I assume I know why someone did what they did. How can I stay curious and say what made you go about it that way? Tell me more about that. And I think wars will end when we do that. So, yeah. So I want to ask you, you know, you on the forefront of seeing what's happening in the world of data is what gets you most excited about the future of information sharing.
[00:47:35] It's not in a lot of people's and it's going to be like holograms of VR is going to help that at all.
[00:47:41] I'm excited by two things. One is that it's becoming it's sort of overcoming its democratising moment where it just became exploded and became popular and now is becoming part of how we communicate.
[00:47:54] So it's sort of exciting to see it embedded into everyday life. It's it's a TV. Plotz It was like, you know, you see it in TV plots, you see it in the news being done much better than it used to. I just think when you look at a situation like the boys in Thailand, some in the amount of good data is that was produced to help us understand that situation. It's incredible. It's really hard and I think that's really interesting. I think about worry about, you know, talk a lot about this idea of data manipulation as a propaganda tool.
[00:48:27] I think we have to be on the guard for that. Yes. I mean, we have to sort of improve everybody's database literacy.
[00:48:34] And I think the tools are slowly, surely getting to a place where what I call, you know, default output is a lot better than it used to be. This give the follow up work to a place where it's not feeling almost random in place.
[00:48:51] It makes it much easier to get to that next step, you know. So I think I think that's interesting.
[00:48:56] And then finally, I would just say. Interest in some of what's going on in terms of data science and visualization in industries in general, like how the sports industry is Méliès.
[00:49:11] Oh, when you get a fervor about something like that, you're going to get the best minds creating.
[00:49:18] So there's some great stuff going on and database and basketball and other sports. And then in like agriculture database is making a huge difference in how we're mapping farmland and how we fertilize that farmland and things like that. Databases in engineering. The example I use my presentation on is Tesla is using data visualization to re-engineer cars based on how we drive them, not how they're supposed to be driven. Right. So they can see how hard on the brakes and how are we at the gas. It may not be the way the car is engineered to take that kind of stress. And so they're using data to sort of discover behavior, which I think is just really interesting. So that that sort of excites me as well. But, you know, I also just love the bar chart.
[00:50:05] It's like homemade apple pie. Yeah, not a pie chart.
[00:50:10] I love those things, too, especially around creating greater data literacy. I'm really trying to inspire both sides of the coin of saying it's not really just the responsibility of the presenters and visualizer as our audiences could show up by empowering themselves and becoming more literate as well. And for the visualizes to really take more responsibility for the ethics behind what it is they're doing. So those are great. And you actually inspired a one last question. Is there some sort of resource that you could recommend where people can break out of their bar charts and see some of the visualizations that you're talking about that are you really making a splash in?
[00:50:55] First this. Yes. OK.
[00:50:59] There are some amazing stuff in there.
[00:51:00] So, yeah. So the first thing I would suggest people do and I know Twitter is nuts, but if you subscribe to just the hashtag databases. Yeah, you can subscribe to database data visualization, sort of all the derivatives of it. You'll see a lot of stuff live on, a lot of good stuff, a lot of bad stuff, a lot of interesting stuff. Some of it is on the very high end of the sort of computer visualization world, which is sort of 3D modeling and all. That's interesting to me. I'm not sort of I wouldn't say I'm the best person for that book. But you will just be inspired by what you see there for sure. The upshot on The New York Times is really great. OK, they do a similarly good job with their visualization efforts. And for the most part, I don't think, you know, anybody's perfect. So I use it a lot to mine to say how would I have done this different, that kind of thing. In general, the media, the large media organizations, the Times, the economists, The Washington Post are starting to get good at integrating databases into their storytelling.
[00:52:08] Oh, lie did the Times just the other day, if anybody's interested, did a very brief visual explainer on the trade war.
[00:52:18] Probably two hundred and fifty three hundred words total, but it was one of those scroll triggered things where you just scroll. It gives you a caption and the picture changes. Yeah. Beautiful. I think he's one of nearly perfect execution on data visualization as an explainer. So 5:38 voxel, all those guys on the sort of big media blitz are doing it very well. I happen to be a huge fan.
[00:52:44] Of historical visualisations, well, and I can't recommend enough books about 19 twelve's cult classic methods for presenting facts but demanding rule of Brinton. And what you find when you look at this is that a lot of things you think are new visualization sort of approaches are not new.
[00:53:07] They're all doing incredible stuff that the railroad companies especially, but a lot of the large industrials who had the resources to do it, they were doing credible visualization around the turn of the last century. So I like going backwards. Get inspiration sometimes do. I think that's sort of an interesting way to look at how people used to handle situations. I just my wife was just in Washington, D.C. and got me a gift make show. Do you? Yes. Please say it's on my wall. So I'm going to walk over here.
[00:53:34] Some guy in the government a long time ago made a visualization of cocktail recipes. Wow. It's old, but cross-hatching and sort of approach is just incredible. Yes. Like a key down here. What the different mixes mean and whatever. So I take a lot of inspiration from things like that. I think it's really, really cool to see how people used to solve problems before they had computers to do it.
[00:54:02] Right. Messing everything up.
[00:54:04] Those are just some of them. But then I also look at district design. I look at presentations that people give anything that I think can inspire me in terms of how do people set context and other situations. You know, when you design a room, how do you use the context for like which furniture pieces are put together, that kind of thing? I just I like the design thinking approach, which is all about understanding the needs, understanding the context and then delivering something that helps achieve that.
[00:54:34] All right. It's beautifully said. And I, too, have caught some amazing New York Times visuals, especially the interactive ones are particularly fascinating. And you also brought up something interesting around the scrolling aspect. So, you know, we're becoming a scrolling generation. That's becoming the new storytelling paradigm for the Web. And yet, you know, we're still confined to one single view and dashboards and things. And I think it's gonna be really interesting how we see scrolling become a tool in our storytelling capabilities.
[00:55:09] I agree. I think it's when it's done well, it's it's seamless and it makes sense. It feels right. And I don't know how else to describe it.
[00:55:18] And it's the Times piece I talk about. It just feels right. It's giving you what, one of the. I've Niccola. Why is that? I think one of the reasons is it gives you the information you need at the moment. You need it. So you think it's static? It's just sort of place kind of close to where it goes and maybe somebody looks at it before they shoot or after. But this is sort of putting it in the perfect context.
[00:55:38] I'm going to trigger the view you need to see as you're reading the words that we thought about. Right. Exactly. So that's really powerful.
[00:55:45] Awesome. Oh, Scott. Unfortunately, our time has run out. I could we could go all day. But what's your next big thing coming up?
[00:55:55] Yeah. So this is pretty exciting. I think deeply be excited. Good chance. Workbook. Yay! So. Very excited. So here's the idea behind this crossword.
[00:56:09] Maybe with with my son or a crossword book.
[00:56:14] You can do the crossword theanswers in the bar. We've done something similar here with the charts. I wouldn't say the answer is in the back. I say the discussion is in the back. So what we'll do is we'll provide you with challenges where we provide a chart or part of a chart and we ask you to go ahead and solve the challenge we give you. You know, how would you make this clear to use the color in this chart to make it still work? They have fewer colors. And we give you some sketch space in there that we are recommending a paper handy, too. And then in the back, as my discussion of how I attack the challenge and I often in that discussion say it is not the only way. This is just how I did it. I'd love to hear how you did it. And then a couple of the cases, I think my discussion arrives at a very unsatisfactory place where I say I don't think I solve this very well. Look forward to your solutions to these challenges. And the book is sort of divided up by challenge type. So there's a chapter on color.
[00:57:10] There's a chapter on clarity. There's a chunk of chart types. There's a chapter on persuasion and there's a chapter on conceptual diagrams. And then at the end, I'll give you two big challenges where you put all of those skills together.
[00:57:22] I compare it to learning the guitar and trying to what are you doing?
[00:57:26] First you've got to do chords and notes and all that stuff and then you can kind of start to do songs.
[00:57:29] So there's a couple songs and facts like that, a term where we put all these skills together. One big challenge.
[00:57:36] Well, it sounds like this workbook is going to be a crucial tool and I will make sure everybody that I know knows about it.
[00:57:45] It's going to be a lot of fun to people because, you know, people like to sort of figure out, see if I can figure this out on my own and then have the answers in the back and do an I.D. crossword.
[00:57:53] It's nice to have the answers and know. Well, that's gratification. But I think the beauty of what you what you're creating there is like we talked about before. You're not creating a situation where a person is going to take a stab at it and be like, oh, God, am I wrong? Am I wrong? And then you say, hey, this is how I went about it. What do you think? Right. And they don't have had this crushing sense of rejection.
[00:58:17] If they got it wrong. No, they will not. I think is fantastic. Which is great. I would love. Yes.
[00:58:25] So how can people keep up with you if they want to reach out?
[00:58:28] So Twitter or Matt Scarpinato, just my name all learn together.
[00:58:33] I occasionally I've been very active lately just because I've been busy with the book. But I that usually just cause charts and interesting things that, you know, I like that I see on the walls on part of that database sort of community out there. Google my name is what? Go to HP, Artaud or HP or I do a lot to the magazine.
[00:58:52] And for the Web site, the book obviously is on Amazon and all those good posts.
[00:58:58] And I do I do a lot of people sort of all around the country that's usually up to on Twitter. So if you are into charts and I happen to be in San Francisco here in San Francisco, just tweeden.
[00:59:09] Awesome. And all of the links that we've mentioned and all the ways to get in touch with Scott, including the book will be on the show notes page for this episode. So, Scott, what a thrill to have you on today. So glad it finally happened. You know, it's rare when someone's work I think has such a profound effect or can and so perfectly answers the burning questions that you you come up as you go deeper into these areas. So I know that my listeners will be so leveled up after they dove into your stuff. So thank you so much again for the time. Really an honor.
[00:59:43] I had so much fun. And it's really heartening. It's I know it's a cliche, but authors always say is. Well, how many books I sell ads about? You know, it's I move somebody. And so to hear you responded positively really makes you feel good is going to be my weekend.
[00:59:57] Oh, wonderful. It's well-deserved. Thank you so much. Appreciate it.
[01:00:06] Getting to meet true thought. Leaders like Scott is always the most rewarding element of doing the show and bringing their infinite wisdom to you. Listeners know he's not where he is just by casting a sort of fire and brimstone approach to the right and wrong way of doing things and data visualization. He's just someone who is driven by the pursuit of service and balance, which speaks right to my heart. And I hope you enjoyed meeting him as well. So to catch all of the links and resources mentioned in this episode, visit the show notes page at Lea Pica dot com slash 0 3 5. I would love if you could leave me a comment or a question for Scott because I want to hear about the challenges you face when presenting information. Remember to scroll to the bottom to sign up for a copy of the Pico protocol prescription. You're going to love it. Or you can tweet me a question for the show by including my Twitter handle, which is at Lea Pica, including the hashtag p.b. And I was tempted to leave you a quote from his book, but something he actually said to me before we recorded this interview was so much more inspiring to me. And it has to do with our role as teachers and guides to you in this challenging line of work. And what he said was, we are not here to lecture you. We are just here to armor you with tools and you're going to be OK.
[01:01:44] Man, he is good. It's so true. The greatest teachers truly are the ones who detach from the idea that what we know is better or worse than what anyone else knows. It's all about empowering you with tools to build your own experiences and empires of success.
[01:02:03] So that's it for today. Till next time. I must say.
[01:02:17] And that's around. All right. Thanks. I hope so. Great stuff. My cat agrees that my quadrio bombing my. Yes. Okay. It's not you, it's me, like.