Valentina D’Efilippo is an award-winning designer, creative director, and author based in London. Working across formats and industries, her work takes many forms – from theatre productions and exhibitions to editorial content and digital experiences. She has co-authored “The Infographic History of the World”, and illustrated “The Brain: A user’s Guide”. Her work has been exhibited internationally and her dataviz “Poppy Field” has become part of the permanent collection of one the largest anthropological museums in the world, The Weltmuseum Wien.She also leads workshops attended by students and professionals, including a series of Masterclasses with The Guardian.

This is the last episode of the podcast until the fall. Please be on the lookout for more great episodes starting again in September. In the meantime, enjoy your summer, catch up on some rest, some reading, and, of course, old podcasts!

Episode Notes

Valentina on Twitter

Valentina’s website & portfolio

Poppy Fields project

MeToomentum project

Book: The Infographic History of the World

Blog post: Sketching the World: An Icebreaker to Working With Data

DataViz & Infographic Storytelling workshop

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Transcript

Welcome back to the PolicyViz podcast. I’m your host Jon Schwabish. I hope you and your friends and your family are all safe and well and healthy in these strange times. This my friends is the final episode of the PolicyViz podcast for the season. No, don’t worry, I will be back in the fall with more great interviews, with folks in the fields of data visualization and open data and presentation skills and technology, but I’m going to take the next few months off and rest and relax and recharge. And to finish off this season of the podcast, I’m excited to have Valentina D’Efilippo with me. Valentina is an illustrator, a designer, a teacher, and a writer, and we talk about all the sorts of things that she does keeping her busy. When you look through Valentina’s work, you don’t see a lot of line charts and bar charts and pie charts what you might consider some of your standard chart types, but instead she spends a lot of times creating new and different forms and non-standard graphs, and so we spend a lot of time talking about how she thinks about communicating data in those different ways to her audiences. We also talk about her data visualization infographics workshops, one of which is coming up very soon. We talk about some of her mapping exercises and some that I’ve actually used in my classes when I teach to kids. So I hope you’ll enjoy that conversation with Valentina, and I hope you’ll continue to support the podcast by sharing it with your friends and your social networks, I hope you’ll consider leaving a review or rating on iTunes or Stitcher or Spotify or wherever you listen to your podcasts.

Before I get to the interview with Valentina, just a couple of things as I think about the next few months and reflect on the few months behind us. It’s obviously been a very strange and difficult and challenging few months both with the COVID pandemic and here in the United States and around the world, the protests against police brutality and inequality. And as I think about my work in the field of data visualization or presentation skills, I’ve been starting to think more carefully about accessibility and diversity and inclusion and equity and how we can do a better job of communicating our data and our analysis to more people so that they can use it, so that they can make discoveries and they can improve policy in the world around us. And so I’m excited to continue that journey with you as we continue to think about ways in which we can make our work better and more accessible and more relevant to the world around us. Part of what I’ll be doing over the next couple of months is finishing up my next book Better Data Visualizations, which will hopefully walk you through many of the different types of graphs that are available to you outside of these lines and bars and pie charts, and that’s why I’m excited to talk to Valentina because she creates a lot of those non-standard graph types. So again, I hope you’re well and I hope your friends and your family are well and I hope you’re staying safe, and so I’m excited to bring this final episode of the season’s podcast to you. Here’s my interview with Valentina D’Efilippo.

Jon Schwabish: Hi Valentina. How are you?

Valentina D’Efilippo: I am great. Thanks for having me.

JS: Of course. I’m excited to have you on. It’s the last episode of the podcast for this “season”. So going out with a bang because I get to chat with you and what couldn’t be better than that!

VD: Amazing. I’m delighted. Thanks.

JS: There are a few things I want to talk to you about, but maybe we can start with you telling folks a little bit about yourself and your background and what you are doing now, and some of the work that you do.

VD: Sure. Okay, so let’s start with labels. I’m a designer, illustrator, and creative director. I’m Italian, as you can probably guess from my accent, but I’m based in London. And I guess, I’ve been working with data for more than a decade now. But yeah, very different formats and different industries. So, I guess, when you’re looking at my portfolio, you would see, like, many different ways of representing or perhaps working with data. Sometimes, it’s pretty standard, like interactive platform or editorial Commission’s. Other times, it’s a bit more unusual perhaps. So I’ve been working with theatre productions where we talked about privacy or climate change during live theatre performances or exhibitions or digital products where perhaps we don’t even visualize data but we use data as a way to create an experience. And, I guess, the common denominator of all these projects is data or working with some sort of complexity, I suppose. So yeah, of all the labels, I suppose information designers in the designer is the one that fits the bill, yeah.

JS: I find that when I do this podcast, people come to the field of data visualization or information visualization from all different ways, you know, there’s no like single path. So you are a designer by training and by background, how did you get into this, especially because you started doing the data part 10 years ago, so even, especially back then, it wasn’t sort of a standard path, so how did you end up going down this path of working with data and combining it with your design training?

VD: Yeah, interesting. So it’s been a journey. So I graduated industrial design at Polytechnic of Turin, and then I came to London and I did a Master’s in Visual Communication. And, I guess, like, from the beginning, my first steps in [inaudible 00:05:43] design were really just experimentation with topics and subject matter that I was interested in, and because of the background that I had there was quite analytical and perhaps more engineering, I was deconstructing everything, I had to put my hands on while I studied visual communication and graphic design. So for instance, at the beginning I did the construction of the [inaudible 00:06:05]. My thesis in my postgraduate was visual analysis of stereotypes and how those gender stereotypes specifically are portrayed in the literature for kids. So I did a recollection of many symbols and colors and activities and emotions in which females and males were portrayed. And unfortunately, this was like 2005 and 2006, it was pre [inaudible 00:06:36] the amount of bad stereotypes in which men and women specifically were described in this literature. And yeah, as you were saying, like, probably at that time there wasn’t really a thing called data visualization. Those projects started to fall into the bucket of information design in my program and later on I kind of understood that, yeah, since the beginning, my first step into visual communication were always kind of like driven towards visualizing complexity, making sense of complexity, breaking it down and then piecing it back together to explain my day inside, so what I learned to others. And then really the project that cemented my practice came years later. So the first job that I landed after university was actually in advertising, in digital advertising. So I worked as an art director for a number of years. And then in 2012, I got an email from HarperCollins, which is obviously a big publisher, and they got in touch saying, we saw your experimentation with data, like those projects that I just mentioned, visualizing gender stereotypes, and we would like to discuss with you that you’re putting together a book about the history of the world through data and infographic storytelling. And I was like, oh my god, this is amazing. But at the same time it was like, really, do have the skillset to do that. But anyhow, it was an amazing opportunity. It was an arranged marriage as you would say. So I was paired with the brilliant James Ball who at the time used to work for The Guardian as a data journalist. And together, yeah, we put our brains together, and we created a 100 infographics from scratch, narrating the evolution of the world and the evolution of mankind and this is the Infographic History of the World, the book that came out in 2013. Yeah, I guess, it was like a very ambitious project, very hard, certainly a daunting brief, but absolutely amazing, an amazing opportunity – an amazing learning experience more than anything is by doing that you actually learn how to do it.

JS: So when you finished the book, is that when you decided just to start doing information visualization and teaching and workshops full time?

VD: Yeah, I guess, again, it wasn’t quite a decision, it was just, I guess, one thing just led to another thing. So the book came out and there was the business card, it just opened a lot of opportunities. So I started to receive more and more briefs and commissions that were labeled as infographic projects or DataViz projects. Then the Guardian got in touch saying we’re putting together these master classes and we would like to expand our curriculum and include infographic storytelling, would you like to teach. So one thing kind of led to the next thing, it’s life.

JS: As it were, as in life, yeah. I want to talk about the workshops in a minute, but I also wanted to talk about your, I guess, that’s your style or your approach to data visualization, because when I scroll through your portfolio, I don’t see – I mean, obviously, I see lots of different types of work as you already mentioned, but I also don’t see a lot of line charts and bar charts and pie charts and area charts. There’s a lot of – you have a lot of complex data and there’s a lot of different forms going on with your work, and so I’m just curious about that aspect of your work. Are you anti-bar chart or anti-line chart or is it more that creative side of the brain, sort of takes over – so I don’t really know how to formulate a precise question but it’s more of an observation I think I’ve made about your work over the last couple of years.

VD: Yeah. No, I guess, like in the portfolio, there is a clear curation of the type of work that I would like to work on.

JS: Yeah, you are right.

VD: So there is a filter that I apply, I clearly do lots of bar charts and conventional charts in the day to day but I suppose, like, whenever I can, I try to push it, I try to kind of like find a way to balance form and function, obviously depending on the audience and brief, the kind of like purpose of the visualization. I try to combine the informative aspects of what we’re trying to do when we’re creating a data visualization, like, a simple bar chart to the more creative aspects as you were just saying to create something that perhaps is more compelling, maybe more aesthetically pleasing or they can perhaps resonate more with an audience from a semiotic point of view. What I mean is how can we actually bring the narrative behind the data behind these numbers to life through the use of color, use of visual metaphors, novel forms, different aesthetics that perhaps we borrow from other fields. I guess, like, a lot of times we tend to think that a chart is like a standard chart, like a bar chart, is easily understood because anybody can read a bar chart. But is it true? Can everybody read a bar chart? And also are we really bringing to life the stories of these bar charts by representing bananas, let’s say, in number of deaths in the same conventional way? So, I guess, those are the questions that I keep posing whenever I’m approaching a new brief, and sometimes a bar chart is the most appropriate way to go about, other times I might just experiment with something else.

JS: And do you find, when you have these experiments and you end up on a form that you like, but it’s not a bar chart, it’s not a line chart, it’s something different, do you find that you have to spend – well, actually, I was going to ask you about how you spend time explaining to the reader or the user how to read the graph, but actually I want to back up – how do you explain to the client that this is maybe a better, you know, maybe they’re coming to you and saying, oh I’m – maybe they’re expecting a dashboard or maybe they’re coming to you because they don’t want a standard dashboard – but do you have to explain to them why this form that looks, you know, doesn’t look like anything they’ve seen before, it’s actually a way that they should go, this is a better way?

VD: I guess, as I was saying before, it really depends on the brief. So depending on the audience and the type of communication, the type of design work we’re doing, I might need to just stick to whatever is conventional. So let’s say, I’m designing a trader platform. It’s not that I’m going to be redesigning the way that the trader does the work. And that will leverage the way the visual cortex has been trained for years and years. So I’m not going to enforce like a new novel way of creating the dashboard. On the other hand though, whenever I’ve got a brief that allows me to be more creative, I suppose, the reason they’re much selling work to the client to kind of like convince them that there are different ways, because usually it’s kind of like a process, we go from the insights, the data, what it’s telling us, discussing the stories that those insights communicate, and then it’s a journey to getting into the forms and the shapes and how those can be communicated. So as long as you kind of like always reference the numbers in the stories, then the forms actually comes, yeah, it’s a normal evolution, I suppose. It’s not something that you are trying to inform, let’s say – oh I really want to do – I am saying something stupid but, like, I really want to do a flower, and then you actually look at the data, it doesn’t make any sense to create a flower out of this data, because the flower doesn’t connect to the story, and it’s probably not the most appropriate way to represent the data shapes either in terms of pre-attentive processing. But if you’re looking at the data and you look at the story and then you can find a connection with a flower, then why not? Does it make sense?

JS: It does make sense. It’s really interesting how, especially your comment about including the data on there somehow makes it, or not somehow, but it makes it easier for people to read and understand it, because they can read the numbers right there.

VD: Yeah. And, I guess, like, now that I said, I talked about this metaphor of the flower, I guess, I can briefly just discuss these poppy fields perhaps is one of the most popular visualization, it’s simply a scatterplot. So we’re looking at numbers, and the stories. What we’re looking at is the last century’s war from the 1900s until the present day where the war took place and the toll in terms of the cost, the human cost, the number of lives that each war claimed as well as I think we had the geography but quite broadly speaking just a continent, so the starting point is obviously the data, we’re looking at the data and what type of shape is best suited to actually represent all these different variable, because we’ve got a magnitude, number of deaths, we’ve got time, so some sort of timeline, and we’ve got geographies to kind of like [inaudible 00:17:02] these wars. I guess, after short exploration, scatterplot seems like evident to be like a good way to go, where perhaps we’ve got a bubble chart on a scatterplot. And the bubbles are representing the number of deaths that each war claimed, and then in terms of timeline where do you place these bubbles, at the beginning, at the end. Seems intuitive to put it at the end, because that’s when you count the number of deaths, right, the end of the war is when you actually see the cost of the war. So you [inaudible 00:17:36] the bubble there. And then what do you put on the Y axis? On the X we’ve got time, on the Y I’m thinking duration to see how long the war was, because on the timeline I just have the end points. So I want to see when it started, when it ends, so I get duration on the Y axis. So this is kind of like initial exploration, I see what the numbers are telling me; and by visualizing just in this simple bubble chart and a scatterplot, I can see some interesting outliers, like two big bubbles at the beginning of the century the Great War, and then in the middle of the century the Second World War. And then I see right at the end of the timeline, a small bubble but really tall in terms of the wide positioning [inaudible 00:18:28] it’s been lasting for six decades, Palestinian – Israel and Palestine. And that’s pretty much the first investigation. Now that I’ve got like an idea what the skeleton of the visualization looks like, I’m kind of thinking, okay, now instead of just babbles, what can I do. And that’s when I come up with the idea of, like, it can be a poppy field, it can be like a field commemoration. So what if I dress those bubble with flower. And then if you think about the flower, then I’ve got a new element that is actually the stem, so I can anchor the flower to the timeline in the moment where the war started, and then make it grow horizontally as well as vertically to indicate the passing of time. So I’ve got timeline both for the duration vertically as well as the duration horizontally on the time on the X axis. And yeah, those poppies can change slightly variation of rate to kind of group them together in terms of geography, and that’s basically the process of getting from something that is quite standard like a scatterplot into something that is perhaps a bit more novel that is a pop field.

JS: Right. It’s interesting the way you describe it as going, in some way, step by step and just letting the data inform how you evolve the form of the piece.

VD: Yeah, absolutely. I think the starting point is always the data, I always need to see the numbers and what they look like, and that usually happens in a very raw way in Excel or Google spreadsheets, sometimes in RAWGraphs, sometimes in Tableau, but I just need to see the numbers and the kind of insights. And then I apply all the visual communication, the graphic design, semiotics later on. I guess, it was only one case where the visual metaphor actually unlocked the data puzzle, it was in MeToomentum – it’s funny that I’m referencing to projects that both use flower as a metaphor. So not all my projects are flowers. But anytime the starting point is always the data, and then I go into visualizing the data and then get into the visual metaphor. But in this specific case, for MeToomentum, that is a visual analysis of the Me Too movement of the first six months, I really got stuck at the beginning, like, with a million data points, we’ve got so many tweets related to the Me Too hashtag, and we had a very multidimensional dataset, we have the geography, we had obviously the time in which the tweet was shared, the person who shared it, the number of followers, also the number of likes, the number of comments, and obviously the [inaudible 00:21:40] data that is contained in the content of the tweet. So doing some sort of semantic analysis you could find meanings and frequency of words and all of that. And that was like, wow, okay, where do we start to piece it all together. And more than anything, it wasn’t even just the complexity of the data, it was actually the complexity of the subject matter and looking through the datasets was really hardcore, reading through these tweets and the stories was like really challenging, really, really hard. At that point I actually felt a kind of heaviness of working with the data and irresponsibility as well of like, am I going to paint anything meaningful, how can I actually do justice to these voices. And then kind of moving forward into the direction of like painting and thinking, okay, it’s just going to be an expression of what this dataset, it is actually just a drop in the ocean, because obviously the movement has been massive, and we had all the limitations that come with scraping the Twitter API and so forth, thinking, well, what if I just paint an image, what could this image be. And I thought, what are these voices, these voices are amazingly powerful, but on their own, until now, they’ve been incredibly fragile. And that kind of brought me to think about the dandelion as a visual metaphor for something that is regarded as something beautiful as well as fragile like a female voice, like, you just blow it and it disappears, but it’s also amazingly strong because dandelion is actually not a flower, it’s a weed, like, you blow it and all these seeds can just grow anywhere, and it’s really fertile, it can grow pretty much everywhere. So you like to see in field, but you don’t want to have it in your garden, kind of thing.

And also if you think about the dandelion is this symbol that is used in popular culture, many times symbolizes hope or the hope for change when you blow at you, say, most of the time, did you make a wish. So they are kind of like semiotics dictated and the shape of the data analysis. So in that case, in this specific project, I was like, okay, what if I could paint this data with a dandelion, what attributes do I have in the form of the dandelion. So I’ve got the seeds, they could have different length, they could have different size, and then I started to kind of plot the data into the visual metaphor if it makes sense. But there was the only case where I actually went reversed.

JS: But it sounds like you had this connection with the data in such a way that the form sort of informed how you were going to do the work.

VD: Yeah.

JS: That’s interesting. It’s also interesting the way you describe your process. The way you describe, it’s sort of very flowing from one state to the next. So it’s not so much like I did this, and then I did this, and then I did this. It has this – the way you describe it just a little bit more, has more of a flow to it. And I’m curious, so I know you teach a lot of workshops, you mentioned the Guardian Masterclass – is that how you teach people to create information visualizations, like, again, I don’t have a specific question, but what is your approach to teaching this skill which, as we’ve already talked about, people come from all different ways to be creative with data?

VD: Yeah, so that’s an interesting challenge, the workshops. So I run workshop with the Guardian graphic and this is an organization in the Netherlands as well as corporate trainings or workshops with university students. So I’ve got many different audiences and also the length of my workshop can vary from three-hour format to a week or perhaps a couple of weeks if I’m working with university students. And I started actually with the Guardian, that was my first commission I would say of infographics storytelling workshop. And, I guess, I took the challenge as a design challenge – how can I actually explain to other people what I do, how can I design a format that will explain that. And, I guess, as you were saying, it’s kind of like sharing with others this flaw, how do you go from the raw spreadsheet into something that is visually compelling as well as highly informative. So I created a few activities and exercises that hopefully illuminate the process, and it’s very analog. I try to keep all these workshops very tool agnostic to kind of remove the barrier of tack, and also because, let’s say, at The Guardian I might have a group of 20 people and all these 20 people are coming from different backgrounds, like, some might be data scientists who are very fluent with data in a spreadsheet, but other could be storytellers or health practitioners or even just students or retired people, they just want to kind of expand their knowledge and become better consumers of charts and data. So I tried to remove a few entry points, like, the tooling and really just focusing on kind of like decision making process that goes into creating successful database. And yeah, I guess, like the aim of this format is always like to create something that is interesting, informative, inspiring, but also highly accessible. So for anybody to be able to create something, I want to everybody be able to participate. And I do create a number of activities with just pen and paper. So you are kind of familiar with the first activity, I suppose, because I spoke at information class at the conference where we met about these activities, that is mapping the world geography from memory, and then on top of that, we’re going to be mapping personal dataset. So perhaps I can talk a bit about that. These initial activities actually are an icebreaker in my formats and it’s based on an obsession of mine. So I’m a map collector, and I’ve been collecting hundreds of maps for about 11 years since 2009, when, for the first time, I visited Japan, and I saw a presentation of the world where Europe was not in the middle, and I kind of felt lost. I was like, oh my god, what’s going on, east and west are reverse, the Americas in on the wrong side. I kind of felt disoriented. So I turned to the local people and asked them to draw the world map from memory to just kind of like sketch it really, really quickly for me. And over the 15 maps that I collected, all of them presented Japan in the middle, and the geography around was somehow more detailed. And then the rest of the world was very much personal, was very much subjective to each one unique map. And that’s kind of like a fascinating thing for me, like, how we are innately able to describe a concept like the world visually, but at the same time it’s like so unique to each individual person, based on our own experience, our perhaps knowledge of geography as well as ability to draw.

So I’ve been doing that for a number of years, and then when I started to design the format of my workshop, I thought, wouldn’t it be cool to actually introduce people by drawing their own world map, because then we could see where people are from, where perhaps they’ve been. And then on top of that I thought what if then I can use that to actually plot some data. So the map itself is already a representation of information and personal data that I can walk people through, that we can share. And then on top of that, we can then map a dataset, a specific story. So to be a bit more specific with that, after we draw the world map, we think about a story that could be, maybe the trips that you’ve taken; or if you haven’t traveled extensively across the world, you might think about the food that you consume, whether it’s Japanese sushi or Chinese takeaway or Italian pizza and spaghetti or, I don’t know, Mexican tacos, anything – anything can make the story. The only, I suppose, filter of all the personal stories that you can possibly introduce yourself to the class, is it in needs to be global because we’ve started with a world map. And [inaudible 00:31:24] we started with a world map, and I think it’s quite effective in a way, because the blank canvas can be very intimidating, especially, if you’re not coming with a creative background, if you’re not used to drawing and sketching, staring at the blank canvas and thinking like, oh, now I’m going to draw this dataset, it can be like incredibly challenging and intimidating, especially when you’re in a group with strangers. So anybody can somehow articulate what the world looks like when you’re asked to do so. So it’s a nice kind of like icebreaker in the sense that everybody can actually start noting down something on paper. And then the next step is to plot this specific story that you might have chosen, like, the travels that you made or the food that you like or where your family and friends are from, whatever the story might be. And usually, in live events, so if it’s like face to face workshop, because nowadays obviously everything is online, so the sophistication of paper choices is not available. But if it was a live event, I would bring tracing paper, and I love working with tracing paper on my own work because then you don’t need to start from scratch over and over, you can just overlay a new layer on top of the map and start with a new dataset, perhaps for correlation to see two different datasets. Or if you perhaps were not happy with encoding that you’ve just done, you can just remove the tracing paper and start again, but online, yeah, we just do everything on the same sheet of paper or [inaudible 00:33:10] box if you don’t have paper laying around. And then, I guess, what is interesting in this exercise is that at the end once we have created our maps and we have plotted our data stories, we swap them around and from being the creators we become the readers. And there is plenty of learning that can actually be drawn by just doing some really rapid user testing and see how people actually enter these maps, what they find useful, how they travel perhaps back and forth between the key and the visualization, what type of titles are the most interesting, most successful, and ultimately, also like, it’s important to know the bias they will put as creators are also mirrored in the bias that we put as readers. So whenever we create our maps, obviously, we put ourselves in the middle of this creation, we see the country where we’re from and blah, blah. And when we’re reading this visualization, ultimately, what we do is overlaying our own map on top of this world maps, to find if the creator actually did include our country or did include the places that we know. And that’s actually how we read data visualization, like, there isn’t a universal way to depict a dataset, and there isn’t a universal way to interpret the chart because everybody has a unique experience, and there’s a unique understanding of the specific data. So yeah, in summary, that’s kind of like the icebreaker of the workshops.

JS: Really interesting. And when you prime people to start adding data to their maps, do you show them examples or do you say, here are some data types that you could plot, because when I’ve done this and you had inspired this exercise for me when I teach kids – and when I teach kids I have them just draw the one floor in their house, and I feel like the times when I show them a drawing of my own house and then I draw circles in each of the rooms of how much time I spent in each room, I get a bunch of kids who start drawing circles on the map. And so it’s a double edged sword because on the one hand they may not know how to add the data to the map, but on the other hand I don’t want to prime them to just be using circles.

VD: Yeah, it’s a fine balance. I found the same, and I run the same exercise with kids myself, and usually kids tend to just follow the instruction, which is fine. At the same time, with [inaudible 00:35:55] audience as well, like, you might have the audience kind of like stuck and needs a bit of prompt and help, and that’s totally fine too. But, I guess, like, I always try to suggest a few paths, like, it could be travels, it could be food, but what if it was your unique story, what could it be, and kind of like rewarding as well of like saying the most creative or the most unusual story would get [inaudible 00:36:25] in the clouds or something like that usually does prompt a bit more inspiration or the challenge at least.

JS: You have a workshop coming up, right?

VD: I do, on the 6th and the 7th of July.

JS: And it must be virtual.

VD: It’s virtual, yes. [inaudible 00:36:43] one of the many Zoom [inaudible 00:36:47] yeah.

JS: Many zoom meetings, right. So do you want to just talk about it real quick, and I’ll put a link in the show notes in case people want to [inaudible 00:36:54]

VD: Sure. So it’s a full-on deep dive into the process of infographic storytelling and there’s visualization, the activity that I just explained right now would be probably included, as many others. We’re going to be looking at conventional charts as well as the use of visual metaphors, storytelling devices, interactivity versus linear storytelling, and just a lot of visual communication and visual perception and hopefully a lot of fun. So yeah, if you want to join, for anybody listening, it would be amazing.

JS: Yeah, I’ll put the link on the notes page, and people could check it out. And I also will put links to the various projects that you talked about and, of course, your whole site and the book which is great, I have it here somewhere [inaudible 00:37:49] my bookshelf. Great. Well, it sounds great, sounds like you’re doing great. Thanks so much for chatting with me. And yeah, it’s been great chatting. Great to hear from you.

VD: Thank you so much for having me Jon. Thanks.

I hope you enjoyed that interview with Valentina and I hope you learned something and maybe can incorporate it into your own work. Take a look at Valentina’s website, her portfolio, and her classes with the Guardian, all linked on the show notes. I hope you’ll consider leaving a review of the show on iTunes or Stitcher or Spotify or wherever you listen to podcasts. I hope you’ll share it with your networks. And if you’d be so kind to support the show financially, head over to my Patreon page for just for a couple bucks a month, you can help me pay for things like transcription, sound editing, and more. I hope you will have a lovely restful healthy summer, and I look forward to connecting with you all again in the fall. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.