the art of insight: in conversation with Alberto Cairo

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Our friend Alberto Cairo published a brand-new book called The Art of Insight, which I promise you is unlike any book on visualization you've read to date. I was lucky enough to chat with Alberto on a recent episode of the storytelling with data podcast. Just as he had been on his three prior guest appearances, he was inspiring, supportive, informative, inspirational, and—yes—even insightful. But this time, the conversation touched on a number of unexpected topics (philosophy, history, tabletop games, politics, power, and even a bit of data visualization). 

Here is just a sampling of excerpts from our wide-ranging discussion.

on losing, and re-discovering, enthusiasm

Alberto: I think that all of us who have worked in a field for so many years, at some point we lose a little bit of perspective. We also tend to forget that there is a whole world out there. It is very easy to assume that everything out there looks like it looks inside of our heads, and that the only way to practice a craft like visualization is the [specific] way that we practice it. 

And what I try to explain in the book is that we all fall prey to that impulse, but at the same time, when we expose ourselves to other people in the field and their work and the way that they see the world and the way that they use data visualization in the world, and for the world, we understand that this is a very diverse craft, a very diverse way of doing things. And I really appreciate that. 

So, I just wanted to build that appreciation or rebuilding and then cast my sights out there, cast a very wide net, and then talk to people and get inspired again.

on different “dialects” of data visualization

Alberto: There are really no rules of data visualization. There's just reason in data visualization. That's a very pragmatist approach, in my opinion. That's something that I describe in detail in the first part of the book.

Mike: Yes, for instance: “If visualization is a language, many dialects are possible.” There are many different ways of visualizing.

Alberto: I use terms from linguistics in a very loose way. I know that linguists would probably scold me on, oh, this is not really a language. But I hope that people will be a little bit forbearing with the way that I use certain terms. It's like, there is a grammar of graphics. There's a book titled The Grammar of Graphics. So if there is a grammar, well, I assume that this is sort of like a language. And the language can be practiced in different ways.

It's not only that there are different dialects in data visualization, so to speak. But also that data visualization can have a multiplicity of purposes. Data visualization can be essayistic, or explanatory, or narrated, which is the type of data visualization that I produce. Data visualization can be exploratory, right? We can create dashboards and other types of graphics to analyze data. But data visualization can also be poetic and artistic. And why not? That's still data visualization because it uses the grammar and the syntax of data visualization.

on the value, and limits, of artificial intelligence

Alberto: [When it comes to AI,] we should be as concerned as we are about any other technology that facilitates the work and lowers the barrier of entry to anybody in the field…which means that, in my case, is not that much. 

It will be something that will extend our capacities. It may lower the barrier of entry to the fields. I think that it is wonderful, for example, that today I can avoid writing long lines of R code to generate my charts, and I can describe the R code that I need in natural language, and then the artificial intelligence text generator will spit out decent code that I can then tweak, right? And then I can add the variables that I need, et cetera, in order to generate a chart, for example. I think that that is wonderful.

Now, are there dangers in these types of technologies? I believe, obviously, yes, that there are. But discussing what those dangers may be, I think it is way above my pay grade. I cannot make any predictions about that. 

But I'm not fearful. I'm not afraid. 

I think that there's always going to be a role for people making decisions. I think that companies will understand that you need people to make these types of choices. No matter how rich and detailed your algorithms are, you still need the people to make the final call about a particular insight that you extract from data.

And if they don't understand this? Well, good luck to you, because you are going to run into a lot of problems.

I’d encourage you to download and listen to the entire episode, or to read the transcript if you prefer. It’s episode 72, “Alberto Cairo and the art of insight,” on the storytelling with data podcast, available from our website or wherever you get your podcasts.

coming this January!

Do you want to learn to create and communicate a powerful data story? Join our upcoming 8-week online course: plan, create, and deliver your data story. Data storytellers Amy and Simon will guide you through the world of storytelling with data, teaching a repeatable process to plan in helpful ways, distill critical components, create effective materials (graphs, slides, and presentations), and communicate it all in a way that gets your audience’s attention and drives action. Learn more and register today.

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introducing Amy (and a chat about change)

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If you’ve been following the happenings here at storytelling with data, you may be aware that we’ve been taking steps to expand our team.

Today, I am excited to introduce you to our newest data storyteller, Amy Esselman.

Amy has had a successful career discovering and telling the stories hidden in data to enable better business decisions. From data analyst, to doctoral student, to consultant, to adjunct professor teaching business analytics (using storytelling with data as a course textbook!)—Amy has seen, learned, taught, and experienced a great deal when it comes to how data can be analyzed, visualized, and communicated.

I had the pleasure of getting to know Amy through the interview process. One conversation theme in particular that stood out to me was focused on changing people’s minds and behaviors. Amy has done some fascinating research on this topic and also has great real life experience to share. During her very first week at storytelling with data, she was bold enough to accept my offer to record a podcast on this topic! 

So with that brief introduction, I invite you to listen to our conversation to learn more about Amy and the challenges of change.

I must say, the expansion of the team is one change that I am pretty stoked about. We’ll have some additional news on that front to share in the new year.

In the meantime, please join me in warmly welcoming Amy to the storytelling with data team!

the inverted pyramid and the art of the interview

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inverted triangle and the art of the interview
 

A recent storytelling with data podcast featured Cole sitting down with SWD advisor Randy Knaflic to talk about a critical part of the hiring process: interviewing. Randy shared tons of great tips and stories from his experience hiring talent for companies like Google, SpaceX, and even storytelling with data. What follows is a brief excerpt from the podcast, in which Randy explains his “inverted pyramid’ approach to constructing interview questions, for the sake of making the entire experience beneficial and positive for both the interviewer and the candidate alike

Audio of the entire episode is available to stream and download from your favorite player:

RANDY

How do we construct questions that are going to go about testing and getting evidence for the different attributes of candidates that we want to test for, whether it’s general cognitive ability, role-related knowledge, leadership, or values fit?  This is where the concept of the inverted pyramid can help us craft better questions as well as provide a better experience for the candidate.

If I was interviewing, for example, a recruiter—somebody who has to find talent in the marketplace—one responsibility of a recruiter is to use different methodologies to search and find people who meet the profile to ultimately hire them. And there's techniques to do that. 

Let's say a question that I have, trying to test that role-related knowledge, is, "How would you go about finding this type of technology or expertise in a search engine using Boolean logic or on LinkedIn? How would you go about finding that?" That might be my pointed question where I feel like, if this person can tell me this, I'm going to be in a great position to understand if they have the appropriate role-related knowledge.

Now think of an inverted triangle. What we're doing by asking that question is, we’re going right to the point. By doing this, we've actually missed that whole journey that starts at the top more broadly, where we can be getting additional information, additional insights, additional context, and a better understanding of the candidate. 

So in a situation like that, I like to find ways in which I could keep it very broad to begin with. So for example: "You know, Cole, what roles did you like hiring for in your previous company? What were some of the positions you enjoyed working on?" This might seem like an easier question, but it's very broad and it lets the conversation start to unfold where I can then get to the next level of conversation. 

COLE

Well it's nice to start with something easy or something that the person is going to feel comfortable talking about, because then that gets them comfortable. 

RANDY

Yeah. It's not that you're not going right for the, "How would you come up with an algorithm to solve this problem?" Going right for that will take us nowhere. So from that first open-ended question, we can move towards, "All right, let's do a little role play. Imagine I'm a hiring manager, who's looking to hire people with these particular technical skills.  How would you qualify that role or work with me on that? Let's just do a little role play. Let's talk through it." 

Okay. Now we've gotten a little more detailed on that. And now I would say to the recruiter, "You're going to go to the market, or you're going to go to LinkedIn or Google and do some searching. So talk to me about how you organize a strategy for doing that and managing your time."

And then, ultimately, I could come with this pointed question of, "All right. Let me see what Boolean logic you come up [with] to identify this sort of technology." 

So I've taken this triangle of starting broad and getting focused down to one question that's focused on the attribute of role-related knowledge, and I've gotten so much more insight by doing that.

Find the full episode of “The Art of the Interview” and all of our podcast episodes here.

tapestry conference

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At the end of November, I had the pleasure of attending the Tapestry Conference in Miami. I don’t attend a ton of conferences and this is actually the only one that exists where I’ve (two years in row!) been present for every single session (both physically and consciously) and found something useful or inspiring in each one. If you’re reading these words with slight envy for not having been there—I can’t recreate the great break-time chit chat with attendees, but I can share the presentations (huge thanks to organizers for making these available): here are the videos.

In particular, I’d recommend the keynotes. Mona Chalabi opened the conference with an entertaining session discussing a number of her hand-drawn graphs (a quick scroll through her Instagram will give you a sense of your work if you aren’t familiar; unfortunately her talk isn’t being shared). She described wanting to feel something about the data and marrying the subject and the visualization so that if you see the visual, even without labels someone can get some sense of what it is about. She also worked in good reminders on significant digits (too many conveys false sense of precision), designing with visual impairments in mind (using alt text or sound, like in this work), and how important the simple question “do you get it?” posed to people unfamiliar with your topic can help point out issues or help you to identify improvements.

Matt Kay’s keynote on Uncertainty (“A Biased Tour of the Uncertainty Visualization Zoo”) was fantastic—he made the point that it isn’t necessarily true that people aren’t good at understanding uncertainty (a claim often made) and that there are intuitive ways to communicate uncertainty that we should be using. I like the onus this puts on the designer of the information. Matt illustrated several specific methods—icon arrays, quantile dot plots, and animating—for better communicating uncertainty. I also learned a new term: subitizing, which describes how we can see a small number of something, for example three circles, and we recognize (without counting) that there are three. This is both useful to be aware of when designing graphs and also simply a word that I will enjoy adding to my vocabulary.

Elijah Meeks delivered the closing keynote on the “Third Wave of Data Visualization.” He describes wave one as Tufte-inspired with the goal of clarity and the second wave of systems following Wilkinson’s The Grammar of Graphics, leading into the third wave of today. Rather than tell you more about it, I encourage you to listen to Elijah tell you about it directly (plus more!) in Episode 12 of the storytelling with data podcast.

In addition to the keynotes, there were eight short stories (roughly 15 minutes each, standout ones for me were Jonni Walker’s and Alex Wein’s) and a number of short talks (about 5 minutes each). You can hear Jon Schwabish and me chat about more of the sessions in our Tapestry roundup. I highly recommend watching the videos of the Tapestry presentations.

Big thanks to organizers, speakers, and attendees for combining to make this an awesome event (and extra thanks to the organizers for recording and making the content widely available!).

it depends...

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“It depends.” These two simple words can answer a number of questions raised when it comes to visualizing data and communicating with it effectively. In this session, Cole discusses 10 common data visualization questions where the answer is “it depends” and discusses what it depends on and the critical thought process required for success. Cole also answers reader questions on considerations between lower and upper case in data visualization related text and Excel resources.

    Listening time: 45:00. Links mentioned during the podcast:

    Subscribe in your favorite podcast platform to be updated when new episodes are available. If you like what you hear, please rate or review the SWD podcast. You can find past episodes on the podcast page, including sessions focusing on how I've built storytelling with data, feedback in data visualization and discussion of what is story?