#105 What Data Visualization Means for Data Literacy

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Power your data and AI transformation journey on datacamp.com. You're listening to Data Framed, a podcast by Data Camp. In this show, you'll hear all the latest trends and insights in data science. Whether you're just getting started in your data career or or you're a data leader looking to scale data driven decisions in your organization, join us for in depth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in. Hello everyone, this is Adele, Data Science educator and Evangelist at datacamp. This is week three of our Data Literacy Month special at datacamp and this week is all dedicated to data visualization and data storytelling, and there's no better person to talk about these topics with than Andy Cotgrave. Andy Cotgreave is co author of the Big Book of Dashboards and senior Data Evangelist at Salesforce in Tableau. He's the host of if Data Could Talk, co host of Chart Chat, and columnist for Information Age. He's Also on the 2021 Data IQ, top 100 most influential people in data. With over 15 years of experience in the industry, he has inspired thousands of people with technical advice and ideas on how to identify trends in visual analytics and develop their own data disclos discovery skills. Throughout the episode we speak about why data visualization skills are so important, how data visualization skills can drive organizational data literacy, best practices for visual storytelling, and much more. If you enjoyed this episode, make sure to like, make sure to comment like and subscribe to Data Framed. And make sure to check out our content for Data Literacy Month. Now onto today's episode. Andy, it's great to have you back on the show.

Fantastic to be here Adele. Thanks for having me on again.

Awesome. So I am excited for you to be joining us for Data Literacy Month to talk about data visualization, creating effective dashboards, how it connects with data literacy, and much, much more. But before maybe for the folks who haven't listened to our first episode together, do you want to share a bit about your background?

So my name is Andy Cotgreave. I'm senior data evangelist at Salesforce, having been at tableau since September 2011 and I'm co author of the Big Book of Dashboards. And I also host a video series called Chart Chat with my co authors and Amanda McCulloch who runs data vis Society. And in Chart Chat we nerd out every month about the ins and outs of charts we see in the news and across media. Yeah. So I've been in this field for over 15 years and just really am passionate about data literacy.

That's really great. And I highly recommend for the audience to check out Charchat. It's very fun. Now, before deep diving with you today on data visualization and dashboards and storytelling, I want to set the stage for today's chat by really trying to contextualize data visualization within the broader data literacy conversation. Data visualization is often called the gateway drug to more complex data skills and tasks. So can you walk us through, maybe in your own words, why data visualization is so important and why learning these skills is also really important?

Yeah, absolutely. So I'm going to attempt to explain why seeing something is important through an audio platform. So it's a bit of a challenge. But go with me listener, and go with me. Imagine you're looking at a spreadsheet of numbers, right? I don't know, sales of products across different regions, loads of numbers. If you are looking at that spreadsheet, can you see which is the highest selling region and product? Can you see which is an outlier? Can you see which is the worst performing? Well, maybe you can, but on a spreadsheet of numbers, maybe there's 100, 200 digits on the spreadsheet, it's going to take you minutes to do it and you're probably going to do it inaccurately. The power of data visualization is taking aggregated versions of those numbers or even just highlighting numbers in a table so that you can see the information you want to see in milliseconds or less. And that is what we're trying to do with data visualization. Take spreadsheets of numbers or databases full of digits and express them visually in a way that answers questions as quickly as is possible. And yeah, we're going to get much.

Deeper into that then harping on the importance of that skill or that concept of being able to understand data very quickly. In some sense, this has never been more important. Today, thinking back about the past two years, for example, if you want to look at a table of COVID 19 data spread and how it's evolving, that's not going to be. That's going to be tantamount for very horrible public safety messaging. So being able to showcase that with the chart has never been more important.

That's so true, Adele. You know, if you think we've lived through nearly three, we're getting on for. We're approaching our third year of COVID And just think about all those government press conferences we sat through, all those charts that the media and the medical professions put out in a way of communicating really complicated data in a way that educates and informs a nervous population. Datavis was fundamental to the pandemic and has hopefully inspired us all to think better about data literacy.

So now that we understand the importance of data visualization, why is such an important skill to master as part of a data literacy journey, whether an organization or an individual? I think it's really great now to talk about really what makes an effective data visualization. I'd love to dig into the details with you, and your book provides a lot of great inspiration for that. So in your book, the Great Book of Dashboards, you lay out really well the foundational elements of effective data visualizations. You're also someone who really borrows from the world of design thinking, the world of design visual imagery, to improve data visualizations and communicating with data. So maybe to first start off, can you discuss the different ways design thinking improves data visualization?

Yeah, absolutely. I think my biggest inspiration here was a book called the Design of Everyday Things by Don Norman. Seminal book on engineering design and user experience, but really applies to data visualization, too. Something I hadn't appreciated when I first got into this field. The big summary that Don Norman says is designers make pleasurable experiences. And you might think, what? How does that apply to a chart? Well, imagine a boring bar chart. Bar charts aren't boring, obviously, but imagine a boring bar chart if you can. How do I bring design theory into that in order to make the user get the maximum out of that bar chart? Well, I can add a correct title. I can add a title which asks or answers the question that the bar chart reveals. I can ensure that the data points that I want people to see are highlighted in such a way that they see the longest bar or one particular bar. I could do that by softening the way the axes are formatted, making them a bit light gray or something. So you can use annotation layers, and that's just on a single bar chart. Now advance that into maybe more complex charts. You might put on social media or Dashboards, you communicate with your organization there. You've got to create this pleasurable experience in some way that matches the medium, be it social media or your business intelligence server internally. And it's still got to get the right information to the audience in the shortest amount of time possible. And again, Don Norman talks about how we process any designed object, whether it's a remote control, a kettle, or a chart, a dashboard. We look at it, we have a visceral response, we make a judgment based on its appearance, we have a behavioral response. Can this chart actually answer the question we came to it with? And then we will reflect on, did it look good? Could I answer my question? If the answer is yes, then you did a good job. And bringing design theories, laws or theories and rules from the world of design is really taught me how to get those three levels of processing right in charts and dashboards that we can build.

And in some sense, nailing those levels of processing really enables you, as someone who's creating a data visualization, to get action from your stakeholders, from your audience. Because otherwise, if you don't nail that, the objective of your data visualization is not necessarily going to be achieved with this convincing a stakeholder, enabling an action.

Yeah, and the key word there is the objective of the visualization. You know, a common mistake I see all the time is, oh, they want to see sales data. Okay, well, just press the button. I've got a pie chart of sales data. You know, what do they want to know about sales data? What are you trying to communicate? And if you haven't thought about the objective of the visualization, then it won't be a successful design.

So, expanding on the notions that you initially laid out here in the book, and the different thought leadership you create as well, through blog posts, through your channel, your podcast appearances, you always talk about the different elements of an effective data visualization. So can you walk us through, maybe in more detail, what makes a great data visualization great, and what are the different elements of such a data visualization?

My two word answer is it depends. And so that's generally my response. How should I visualize this job? It depends. Okay, now that's a little facetious and perhaps not very helpful for people just starting out on their journey. So if you'll allow me more than two words a day, I'll carry on.

I'll definitely do.

Okay, good. So I think in order to measure the success of a visualization, you've got to be asking. It goes back to what we just mentioned. What are you trying to Achieve. And I think something I'm honing in on is a model that there are four tensions or levers that you're trying to pull and push and pull, push and pull. Whenever you build a visualization and when you think about those four tensions, you are then able and whether you've implemented them successfully, you're able to judge whether your visualization is great. So the first three would be, is your objective to show a large amount of detail or just provide the gist of the information? The second one would be, is this chart going to be for fun or is it serious? Right. That's a valid conversation. And the third tension would be, are you trying to show something where people can explore the data or are you trying to explain a story you've already found? The fourth tension is, is your visualization formatted honestly, or is it formatted in a deceptive way? So that last one, we always need to keep our formatting honest. People do create deceptive charts, sometimes deliberately, sometimes accidentally, but we do have to. Obviously, we should all be leaning towards honesty. But the other thing about those tensions are really important. So imagine I'm doing a presentation to the board and I know a slide is only going to be on screen for about two seconds and I'm going to show it and then move on. In that case, you have very little time to convey very little information. So a successful chart there is super simple with an in your face message. Contrast that with a business user who's got time to explore the data on a business dashboard that you've built. That Dashboard could have 10 different charts on it and some of them could have loads of data points, and there could be interactive actions which create a story in a flow. In that case, it could be really complicated, it's obviously going to be quite serious, and it's a very much an exploratory experience. So both those scenarios are generating success, but the way to know if you failed is if you take that complicated dashboard and put it in a PowerPoint for two seconds and say, as you can see, the dashboard shows sales are going up and then you move on. It's no, I can't. So that great dashboard could be appalling if used in the wrong place. So that's why it depends, because you've got all these little levers you have to push and pull and eventually you can judge success based on what you're trying to achieve.

Yeah, and I love that you use the word lever, because the way I imagine this, when you're breaking it down, is that you have kind of this panel with different knobs that you can evaluate the different tensions and depending on the inputs that you have. Right. Is this an audience that only has five minutes to listen to your data story or presentation? What's the medium by which you're sharing it determines the level of where you need to put the knobs?

Yeah, absolutely. And I'll give you an example. Hans Rosling, he was Swedish physician who exploded onto the TED Talk scene back in 2006 showing this amazing chart of the health of nations, right? And basically his TED talk was him talking about a scatter plot. And I do this exercise in presentations. I put on a slide, 150 dots on it and lots of different colors. And I asked my audience, is this too complicated for a presentation? People are like, yeah, it is, it is. And then we play the Hans Rosin video where he presents the Same chart, this 150 dot multiple color chart with two measures animations. And it's mind blowing. The difference being Hans Rosling takes the time to explain what each axes mean. He focuses on one dot. He tells you what one dot means and then he explains what the context is within the greater picture. And then he narrates the chart and sort of animates through time. So what he's done there is go, does my audience need the detail or the gist? And what he's realized is he wants to push the lever so that they do get detailed. The audience does get detailed because it's a complicated chart. But he realizes in order to achieve showing the detail in the presentation, he has to commit 3, 4, 5, 6 minutes while the chart is and telling people what they're seeing. So that I think is an example of how somebody can use these levers to achieve something pretty powerful.

Maybe giving another example here, flipping the levers. Can you think of an example where the lever is more on the gist side of the things and it's bit serious? Or can you give us maybe another example where it's more just so another.

Of my favorite examples was a chart originally made back in 2012 by Simon Scott. And this was a chart showing conflict related deaths in Iraq from 2003 to 2011. So not a happy data set right now I'm going to try and explain this. The chart that Simon published was a simple bar chart. Bars were pointing down and they peaked in the center of the bar. And then he colored them a deep blood red. The appearance as you're looking at this bar chart with a sort of inverted triangle looked like a smear of blood dripping down the screen. And the title was Iraq's Bloody Toll. So what Scar did was use orientation, color and title to create this visceral response to think deeply about the human tragedy of what happened in Iraq in that period. Now what I realized you could do is if you flip the bars the other way up, change it blue, you actually begin to see that the number of deaths month by month is decreasing. So in that case, you could actually change the title and say deaths are on the decline and try to tell the story of hope instead of focus on the tragedy. And this is where the lever is really applying on your design. You're using those design levers to actually change the message in a story completely, just with color and orientation.

That's an amazing example, and definitely we've referenced it in earlier examples where you showed it to us, especially on a webinar that you attended with datacamp, and I highly recommend the audience to check it out. There's one section in your book that I love which covers something called preattentive attributes and data visualization. And it touches upon a lot of the notions that he discusses here. I think these provide a great framework to think about how a data visualization is perceived and how to best construct one. Can you walk us through maybe what are pre attentive attributes and how they impact a data visualization's impact on the audience?

Yeah, I fell in love with data visualization back many, many years ago, and it was part of learning about my cognitive science that really turned me onto it. And basically our millions of years of evolution, of hunter gathering and trying to spot tigers moving through the grass in the savannah and avoid the red dangerous, poisonous berries enabled our visual system to we could process the natural environment around us before we consciously think about it. And that is a gift of evolution. Wow. So avoiding tigers and finding barriers enables us to be better data analysts. Yes, it's true, because think about a bar chart. A bar chart has rectangles that are different lengths. So length is an example of a preattentive attribute. So now what actually is it that our brain looks at the different lengths of those bars and actually identifies which is the longest and which is the shortest before we even look at the bar chart consciously. So we've already got a head start to the data before we actually think, what am I actually looking at? Or area. You can make circle charts. A big circle we can pre attentively see as bigger than a smaller circle or colors or hues. So if you're looking for a red dot Amongst some gray dots, you're going to see the red dots pre attentively. These pre attentive attributes are the atoms from which we build chart length, position, color, hue, size, angle. There's loads of them. And it's probably beyond the scope of this podcast now, but we process some of them better than others, which is why pie charts are very good, because we don't really do angles and areas very accurately, but our brain can super accurately see differences in length of bars, for example. So, yeah, preattentive attributes, once you understand those, it unlocks so much.

Yeah, I couldn't agree more. And you can actually leverage them to your advantage while delivering a presentation, for example, to guide the audience's attention by using these pretentive attributes. A great example would be, you know, if you want to point the audience's attention to one bar in your bar chart, you can elect to make everything gray and just highlight color on that bar chart mid presentation.

It's so easy to misuse color in visualization or any communication medium because every tool available to us today can use an infinite amount of colors. But the most powerful visualizations and dashboards are the ones that use gray and one color and then really, really powerful.

Okay, that's awesome. So, of course, given the book is called the Big Book of Dashboards, I'd love to actually deep dive with you on dashboarding, as dashboards are one of the most effective ways to share insights with data visualizations within any organization today. You know, many organizations are leveraging tools like Tableau, Qlik, Power, Bi to do these dashboards. So can you walk us through maybe how dashboards extend the power of data visualization and what you have found are the best practices for creating effective dashboards?

Well, I think first we have to. We have a semantic challenge. I want to ask you, Adele, how would you define a dashboard?

So I would define a dashboard as a collection of data visualizations aimed at answering a specific set of questions around a specific set of data within an organization. How does that definition.

It's great, right? Have you thought deeply about that in the past or is that your first stab?

That's kind of my first stab at it, but I'm not a data visualization expert as you, Andy.

Oh, I'm sure you are. I'm sure you are. Okay, so a dashboard can be defined in a gazillion different ways. In our book, the Big Book of Dashboards, our definition is only 15 words long and it's super vague. Because what we realized is as we were looking at all the dashboards that we could find across industries is that there were so many different variations. So one example, you said a collection of charts in your example. But we've got some great dashboards that we think are dashboards. They're just a single visualization. So every time we try to extend our dashboard definition, we could just find more and more caveats to be like, oh, well, this dashboard doesn't fit the definitions what we're going to do. So in the end we just collapse the definition to something pretty vague about it's an artifact used to monitor a system and to facilitate understanding. I think it was something like that. The reason I don't really know what our definition was is that I don't really care about the semantics. Right. You or the audience. We are trying to collect information and present it to a user in such a way that they can make decisions, check a process, or understand more about whatever it is they're looking at. Right. And if you want to call that a dashboard, great. Ultimately, a dashboard is a word referring to a piece of wood on a stagecoach, anyway. So it's a word taken from somewhere else. So now we've got a definition. Well, now I've maybe destroyed the definition of dashboards. Who knows? I don't know. How do you best create one? Well, you have to go to your audience and really understand very deeply what it is they want to see. If they say, oh, I'd like to monitor sales, oh, great. Why would you like to monitor sales? I mean, it might be, are we on target? Is our quota on target this quarter? Why do you want to measure that? And they'll come up with a different answer. And believe me, when you go to users, if you ask why four or five times, you know, this is classic business MBA process. You'll get to the root cause of what they want. Once you know what they want, you're like, well, how do you want to see it? Are you going to be interacting with this thing? Do you want this thing delivered in an email? Are you going to be looking at on a cell phone or on a screen in a call center? Each of those will determine a different delivery mechanism and a different style. So the summary of this, the best practice for developing effective dashboards is go and speak to the user and understand what they want, why they want it and how they want it, and then create a really basic prototype. And then they'll go, no, that's not what I wanted at all. And then from there you can iterate until you get the right answer of what they need.

That's really great. It's definitely complicated, but it's also wonderfully simple and accessible, which is what's so nice about dashboarding and data visualization in general. So the book contains a lot of examples of dashboards from different industries and different use cases, and you showcase brilliantly why these dashboards are effective. Can you walk us maybe through the different type of dashboards that you've encountered and maybe expanding onto that, what makes each of those dashboards effective?

Yeah, I think a big question you have to ask is, should the dashboard be interactive or not? And I'll focus on that for this answer. So if something's going to be interactive, then you've got to start asking, well, do my users understand how to use this dashboard? How do I make sure they literally know how to use this platform? You know, for Tableau Server or Tableau Cloud, for example, they need to know what the URL is and then once they get there, what is it they're looking at? Even when you put filters on a dashboard, how do the users even see them right now? This sounds so ridiculous, Adele, that I could say, well, it's on the screen. Surely they'll see them. Well, we've done a bunch of eye tracking studies on dashboards, a lot of which were taken from the big book of dashboards. And I could have designed a dashboard with filters on it that we allow people to look at the dashboard themselves and they literally don't even look at the filters. So then at the end of the exercise, you ask them, well, why didn't you interact with this stuff? And they go, I didn't see the filters. And the inside, I might be screaming, but they're on the screen. They're literally in front of you. But understanding that people look at screens in a way that you might not predict or might not be as you'd hope, so you've got to ensure they can see the things to interact with in the first place. So interactive or not is important. We have a great example in the book from Arsenal Football Club, one of the Premier League teams in England, and they have this static chart based on a player's performance, which is delivered to the player after each match, but just before the training session. And it's a static dashboard delivered to their cell phones so that they can show that to their teammates and have a laugh or have some serious insight, but basically analyze their own performance. And they're really fundamentally different types of dashboards because one's interactive. This is an example that isn't. But the really lead to the question is like, well, is this dashboard effective? It goes back to the previous question, are we thinking about how the user gets it and what we're trying to achieve? So yeah, I think interactivity or static is a big decision to make. Another one is what is the form of delivery? Is it going to be primarily used on a big screen, Is it going to be primarily used on a cell phone or is it just going to get delivered in an email? Again, they require completely different form factors that you have to take into when you're designing your dashboard.

That's really great. And maybe deep diving here a bit more. What strikes me from your answer is there are really two main considerations people need to have when thinking about the dashboards they need to create. One, is the audience, right? What is the audience expected to achieve with this? And then secondly, what is the format and the user experience of consumption of dashboard in an organization? What are the different types of audience is in a nutshell basically of Personas that may expect to interact with the dashboard someone is developing and what is often the ideal way of presenting that information?

The types of audiences. It's difficult to try and summarize that as a catch all. But one example I often see is your executives. Right, let's think about, let's go back. I mentioned sales earlier. Let's think about sales. An executive, CEO or head of revenue, they want to see what is the aggregated roll up revenue this quarter compared to last quarter, compared to target and compared to this time last year. Right. They want to see this thing rolled up to a very high level of aggregation. And they'll have KPIs which will show whether they're on target. And then you know, some slightly disaggregated right there, maybe by region or by product area, something like that. That dashboard is useless for the account executive who is actually trying to use data to target an account. So an account executive, completely different experience. You know, maybe they have five or six accounts and one of the accounts is a leading car retailer or something. Right. The executive's dashboard is utterly useless to this person. They have to be able to take the same data set and say, well, maybe they can use data to tailor an account plan to target that account. You know, what have the users in that organization been doing? Have they been looking at our website? Have they been doing training courses? What have we sold to them previously? Are there any opportunities that have been one or closed recently? And so it's the same data set, but the dashboard the account executive needs is completely different to the dashboard, the exact needs and the reason I use this example is sometimes we see in organizations, the executives go, we have done business intelligence, we are successful because I have an executive dashboard. Hey team, everybody use the executive dashboard because it reveals a complete misunderstanding of data culture and data literacy. To think that, hey, the dashboard you've got is great. What about all the people below you in the organization? You've got to think for those people too. So that's one example that's really great.

And I'm excited to expand on the data literacy component here and maybe on the user experience. Before we move on to the next question. What are things that you wouldn't expect need to be considerations as part of a dashboard decision design process? That would be from a user experience perspective. So, for example, one thing that came to mind, I was reading an article about this recently, is just how important load times are for a dashboard are to be able to be consumed. Right. And this is not an interactivity decision, not an aesthetic decision. You could have one of the most well designed dashboards of all time, but if it takes more than five to seven seconds to load, it could really hurt the amount of times it's actually used.

Yeah. And I think we might have a question later about if DataViz is the gateway drug. What else do you need to consider? And this is where these things have to be considered. I know in Tableau, for example, if you build a tableau dashboard and keep it bare bones, super simple, and you've got a well formatted data set and a big nicely resourced server, the load time will be really fast. If you get carried away as a designer and start thinking, I'm going to bring in loads of bells and whistles and background images and do all really.

Bespoke calculations and add a lot of interactivity, for example.

Yeah, yeah. Then you're actually then beginning to put more of a burden on the server and that dreaded load time begins to increase. And this is one of the challenges, certainly something. When I used to be a data analyst, when I was a customer of tableau before 2011, I used to get carried away with building elaborate dashboards that were really intricate, but they took forever to load. So you have to sometimes recognize that the enthusiastic designer that is inside you trying to build these wonderful experiences has to be balanced with the need to create something that actually doesn't lose people when they're trying to load it. It's a great question, important thing to think about.

Yeah, that's awesome. So of course the other side of things here, beyond user experience and beyond design is the ability to create a narrative. Right. Communicating data insights and data storytelling. It's extremely important when crafting data visualizations and dashboards. So can you walk us through maybe how to effectively embed a narrative within a dashboard and how to convey that insight to a consumer.

Guys, I've been resurrecting a talk I did back in 2014 about a dashboard I built which was very much inspired by Dilbert. What Dilbert? Now think about a Dilbert comic strip. The weekday strip is a three panel comic strip. Panel one is an introduction to the joke. Panel two is the joke, and the third panel is like some sort of epilogue which hopefully adds and builds on the joke itself. That's an amazing story structure. Introduce the plot, complete the plot, write an epilogue. Right. I built dashboards that follow that story structure. Three panel dashboard oriented horizontally. Introduce the data, deliver the punchline, and create an epilogue. Right. Now that was for a particularly bespoke situation, but the reason I use this example is because dashboards are made of panels containing charts that are in some sort of a grid like structure. And they should be in some sort of a grid like structure. And where do we see that? We see that in comic books. Right. And what do we do with comic books in the western world? We read them from the top left to the right. Well, we read them from the top left to the bottom right in a sort of a linear structure. So if you want to form a narrative in a dashboard, a really good starting point is to think like a comic strip. So the top left, again, I'm talking about western left to right reading cultures here. The top left contains the super summary, and then from there you can lead the user left to right and down into more detail. So whatever's in the bottom right is kind of the most granular level detail, which is where you've got the deepest level of context. Yeah, that's not a universal way of designing dashboards. They're a mini way of doing things. But that's one example. Go and read some comics, basically, I.

Highly recommend as well. They're incredible mediums to convey storytelling through visuals. So of course now we covered kind of the main skills when it comes to creating effective dashboards, visualizations and narrative. But I think given that it's Data Literacy Month, I'd also be remiss not to talk about the connection between data literacy and data visualization, both from an individual perspective and from an organizational perspective. You know, I said at the beginning of our episode together as you alluded to as well earlier, data visualization is often called the gateway drug to more complex data tasks. Right. So can you walk us through maybe from your perspective, how consuming data visualizations and learning about data visualization enables better data literacy within organizations and more thoughtful conversations around data?

Well, I think when you have good data set and you're building charts, you know, your dashboards, for example, are successful when users come to you and ask more questions. Oh, I've seen the sales by region, thank you very much. Now what's happening by product. But that's a sign that they have a gone and looked at your visuals, understood it and then it's inspired a second level question and that is a sign of success. You can't always answer every single second level question. You shouldn't aim to do that because you can't anticipate all the questions users are going to have. But that's a sign people are engaging. Right. So that's on dashboards. As you start bringing data into presentations or even just into meetings where you're throwing data around on screens in an ad hoc manner, you can start querying data very, very quickly and getting answers instantly. I hear customers a lot say, one of the problems we have, Andy, is we spend 15 minutes of every meeting arguing about the data. And I've always thought, isn't that kind of the goal? And I realized it's a problem if the argument about the data is because you don't agree with where the data's come from and you don't agree about the truth of the data, obviously we've got to solve that. But I'd love to meetings to be. Let's go in. We're going to try and continue sales. How are we going to improve sales this quarter? Right. Well, let's play with the data. Let's explore which bits are underperforming or outperforming and let's have that conversation and argue about what the data is saying to come out with the decision. So I think that's a really high level of maturity where data is just the fabric of the conversation and data is easy to access, easily understood by everybody, and driving questions as they arise, which dashboards can't do. Dashboards can only answer questions you've already thought about. What are you going to do to answer today's question?

Yeah, that's really great data visualization skills. What's so nice about them as well is that they give you that confidence to be able to criticize a data artifact. Right. Because especially visualizations, they tend to present themselves as matter of fact, as ground truth. Right. Because it's beautifully visualized, it's there in your face. And having those skill set really equips you to become much more constructive and also much more critical of data.

Yeah, that's so good. And a really important part of that is empathy as well, because I'll tell you a little story. I used to run a blog called Decipheredreality.com and it analyzed data about the board game Arkham Horror. Arkham Horror, the card game, Right. It's a nerdy collectible card game where you build decks to take your characters through a scenario against Lovecraft inspired monsters. Hey, what an awesome game. Right? There's an entire website dedicated to which cards are used in which kind of decks. And I started building charts based on the data in this website. For fun, right? And because it was, I love the game anyway, so I built the charts. Here's the most common used card, here's the least common used card, blah, blah, blah. And then somebody on Reddit replied, yes, I don't think you understand. Data visualization analytics should be about this, that and the other. And you're only showing the first level of insight. I recommend you do this. Thanks for your advice. I'll think about that when I write my second book and when I appear on another serv podcast and when I do another set of keynotes and teach another 10,000 people. But what that person had failed to do, show the empathy of, well, what was I trying to achieve? I was just trying to achieve a little bit of fun looking at super basic charts. I'm going, here's the basic thing. Take the data for what it's worth and have some fun. And he had kind of gone, well, he's seen that. And then he's gone, well, I've got these other 10 questions. Damn it, he's not answering these questions. He's a failure. I only wanted the first question. Now you go and do the work if you're so bothered about it. So anyway, I can now, after three years of her, laugh about that story. But the point being that person was criticizing in a non empathetic way. And I think data literacy, mature data literacy, knows how to criticize thinking about the designer's intent and which. What's the actual goal of the visualization they're critiquing. Right. So there you go. Shared my story.

Yeah, that's really great. I appreciate the vulnerability, Andy. So given how important data visualization skills are, you know, what are the ways you recommend to people within an organization to become better at visualizing data and also consuming data visualizations.

Well, I guess first of all, I'd say listen to the Data Camp podcast. Get involved in Data Literacy Month and all the resources that are there for you. Right? So that's a given. But also just practice, practice, practice, practice. Data visualization and getting good at building dashboards and being able to communicate effectively with data. It is an art and a skill. It's technical, it's art related. And you know, an artist is not successful from day one. An author is not successful from day one. A coder is not successful from day one. They have tried, they've played, they've succeeded, they failed. And every single step they're taking is teaching them a little bit more. There are free tools such as Tableau Public. You can just download it or use it on the browser, connect to data and get going. And every time you build something you are learning, on top of that, there's really rich and active communities you can get involved with. One example in the tableau community is something called Back to viz Basics. Every two weeks, it's like, here's a really basic data set and the challenge is build a bar chart or build a scatter plot. It doesn't get easier than that. And so the barrier of entry is super, super low because you can learn that skill in about half an hour. But then you can connect to all the people who've done it the same task that month. And believe me, build a scatter plot with this data set will generate infinite different types of scatter plots. Right? So practice, get involved in free tools. Obviously Tableau Public isn't the only one. And then essentially, Steal like an Artist. Steal like an Artist was a great book by Austin Kleon. Oh my gosh, I've forgotten his name. But his manifesto was in order to become a good artist, or in this case, a database designer, you can go and get inspiration from expert practitioners and sort of copy their work in a way which is not plagiaristic. Right? Copy something for inspiration and with humility. So I think get involved, download something for free and be inspired by the work of others.

That's definitely the case and I really appreciate these insights and advice. And now, Andy, as we near the end of our episode today, I'd love to ask you, what are you up to next and where can audiences find you? And given your position at Tableau, what are future trends and releases in the business intelligence space that you are excited about?

So I recently got promoted, so I'm Senior data evangelist at Salesforce now and I'm excited to bring data visualization and data culture understanding to Salesforce's customers and prospects, which is a really big platform. So excited about that. At time of recording, I'm going to be going on a sabbatical soon. So at the end of that, I'm beginning to work on a new book. Can't say very much now, but you've heard me talking for an hour and you've got an idea about what I'm passionate about. So just keep your eyes peeled on that. So that's me. And then future business intelligence trends. I think what we're seeing in our area is that Tableau and other tools like Power Bi and Qlik create tools which in the hands of expert analysts can turn data into anything. What I think tableau hoped 10 years ago is that anybody can learn to use Tableau. What we've learned is that actually analysts like to use Tableau or people who are analytically inclined like to use Tableau. Right. So they put in the effort to learn the platform. However, not everybody has that inclination to spend the time learning the ins and outs of using this platform. So it's how do you bring the power of say, Tableau or any analytics platform to people who for whatever reason can't invest the time into learning that drag and drop experience? And we're doing it through things like Ask Data, which is a natural language interface to Tableau. We've been working on it for years and the latest iterations are even beginning to tempt me, you know, a 15 year veteran of using Tableau away from the native tableau interface to I'm just going to type a sentence like my in Google and then tinker with the words in that sentence to tinker with the view. So I think our goal is to try and bring the power of analytics to people who aren't analysts. You know, that's things to ask data and bringing data to the user rather than asking them to go to a destination to see data. So yeah, that's a trend I'm excited about.

It's very exciting to see the trend of conversational interfaces and all sorts of analytics tools like even you see it in open source programming languages like Python. You now have something like, like Codex and you say, hey, I need this data set, I need a function that creates that and it creates it for you as well. So this is going to be very exciting towards the democratization of data and the democratization of data insights in general.

Yeah. Adele, have you been using the AI generating artwork.

Yeah, I have. I have dall E, too. Yep.

Yeah. Yeah, sorry, it's dall E, not wall. Yeah, I keep calling it wall E, which is. Yeah, they're amazing. Right? And that kind of AI, that language interpretation, you know, that's what we're trying to bring to data as well.

That's awesome. So, Andy, it was amazing having you back on the show. Is there any final call to action before we wrap up today?

Yeah, I guess I'd say, first of all, Data Camp, Data Literacy Month. I think what you're doing is fantastic at Data Camp, so I support everything that's going on there. Second, just start your own journey. If you are thinking about starting your journey, just download a data set, try it, and see what you can find from it. And I guess if people want to follow me, I have a newsletter called the Sweet Spot, which will get renamed soon, but that's slightly to do with the book, but currently called the Sweet Spot. And I'm sure we'll put links to that in the show notes.

Awesome. Thank you so much, Andy, for coming on Data Framed.

My absolute pleasure. Thanks, Adele. I think you're doing a great job, and I hope you all enjoy the data literacy.

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