Become a Creator today!Start creating today - Share your story with the world!
Start for free
00:00:00
00:00:01
From Tableau to AI: Where Data Visualization Is Headed with Andy Kirk
15 Plays3 minutes ago
My friend Andy Kirk joins the show to reflect on the changing landscape of data visualization. We discuss the evolution of tools like Tableau and Flourish, the dispersion of social media communities, and how, how AI is reshaping workflows and data visualization. Andy shares insights from his freelance experience, the challenges of teaching data preparation, and his measured take on critique and awards in the field. This episode captures a moment of introspection in data viz—where progress is evident, but big questions remain.
Keywords: data, data visualization, flourish, graphic design, how to, information design, graphic design tutorials, graphic design portfolio, graphic design course, online learning, graphic design photoshop, graphic design trends 2024, how to draw, data scientist, Federica fragapane, accurat, AccessibilityInDesign, EngagingVisuals, Inspiration, DataNarratives, VisualizationDesign, InstagramPortfolio, BehancePortfolio, mathematics, Al, machine learning
Subscribe to the PolicyViz Podcast wherever you get your podcasts.
Become a patron of the PolicyViz Podcast for as little as a buck a month
Check out Andy's website and grab his new book, Data Visualisation: A Handbook for Data Driven Design.
Follow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTube
Email: jon@policyviz.com
Recommended
Transcript
Introduction to Andy Kirk and His Book
00:00:12
Speaker
Welcome back to the Policy Viz Podcast. I'm your host, John Schwabisch, once again, for your journey around data visualization. And this time on this episode and this week, I'm joined by my good friend, Andy Kirk, who I haven't seen in many ages.
00:00:29
Speaker
It is good to see Andy, have him back on the show. We talk about, well, talk about a lot of stuff. Andy has a new book, a new edition of his books. We talk about that. We talk about the future of data visualization. We talk about client work. We talk about it AI, of course, we're going to talk about AI and how it's going to impact our field going forward.
00:00:49
Speaker
If you've never heard, got a chance to listen to me and Andy talk. I think you're going to enjoy this one. we basically just have a good time. It's always good to see
Balancing Freelancing and Market Dynamics
00:00:58
Speaker
Andy. So I'm not even going to delay any further. Here's my conversation with Andy Kirk from Visualizing Data.
00:01:03
Speaker
I'm sure you're going to enjoy it. Here you go. There he is. at that good looking guy. Oh, you mean me? yeah there's no There's no one behind you. Sorry.
00:01:15
Speaker
you've got You've got your – I didn't even know. You've got your information's beautiful trophies above you. You've got your big data viz neon sign. Well, you know what with those, right?
00:01:25
Speaker
they're the victorious trophies from 2015 and 2016. And the question is, at what point is a trophy no longer – a badge of honor does it have an expiration date on it yeah exactly you know this he's now oh god has he still got those up you know well there is there is so like no so now it's like the data viz society awards brought to you by information is beautiful or maybe it's the around i don't know so uh those are like um those have value now that's like you know the matchbox car that only there's only like six of them that's that's what you've got well there you go we'll see for now how are things
00:02:03
Speaker
How are things? Very
Impact of Social Media on Data Visualization
00:02:04
Speaker
good, mate. Thank you. Yeah, all good. um Busy, which is never something I take for granted, even after whatever, 15 years-ish, I think, freelancing. But um I still wake up every day thinking this could be the lot and the last day where I'm in demand. But I don't take that for granted, especially in the context that I do know that there's a ah general market slight slowdown, little dip in the road right now that I know that there's a few people experiencing, so I'm i'm very sort of aware of that privilege to still be still be busy and still be busy in lots of different things, which means I keep you know stimulated because they're they're all different things, but they all fit into the same central premise. So can't comp complain.
00:02:51
Speaker
Yeah. ah Yeah, you seem busy. I got your newsletter this morning. ah That's always fun to get. You've got a new book out or a new edition of of the book. Yeah. A lot of stuff going on. i want i do want to talk about the book, but um I want to – we talked a few – what is a couple months ago now on your podcast?
00:03:08
Speaker
I think maybe a couple months ago. um I wanted to get your sense of where things are in the field. Like what's the, what's the state of data is social media sort of dispersed now. So that's changed the nature, I think of, of conversation and debate.
00:03:24
Speaker
Like how are you feeling these days about data is sort of generally, I think, I think we're at a very interesting junction. and And I guess that kind of,
00:03:37
Speaker
slightly caveated phrase means that I don't quite know if this junction going forward is is all going to be rosy, you know, the golden path still ahead, or if we've just reached a point of, I don't think it's saturation or or like full maturity, but there's certainly something going on whereby maybe a lot of the things that we were having discourse around and ah wondering about when will this be solved in the early two thousand and ten maybe a lot of that stuff has just been boxed off.
Diversity and Challenges in Data Visualization
00:04:10
Speaker
yeah know So, you know, things like the the maturity of certain tools, you know, we've talked about use of things like flourishing data wrapper in that podcast that you mentioned.
00:04:19
Speaker
yeah And, you know, those tools are alongside all the others, you know, the the the power behind the tableau of the world. They've really given people such a powerful means to create more things that they wish to create.
00:04:36
Speaker
And with, you know, pretty good practices as the underpinning of how they're, you know, organized, albeit, you know, still lots of room for creativity and and modifying.
00:04:47
Speaker
But it does feel to me that um there is, you know, there's there's ah there's an excellence in the data viz field in terms of the diversity, the the talent, the the energy that's still there.
00:05:03
Speaker
I think the fragmentation through social media is a big issue. It feels like we've got a partial view. We've got a partial jigsaw puzzle. And it's hard to know ah where the patterns are. It's hard know where all these things join together.
00:05:18
Speaker
It feels like we've got a bunch of kind of archipelagos of people. You know, there's a little kind of emerging community on Blue Sky. Then you've still got some and Twitter people. You've still got the kind of LinkedIn. But it's bit more, here's what I've done. Here's what I've achieved.
00:05:31
Speaker
yeah um Congrats to me sort of thing. um the The Instagram, the cool stuff, but ah eight it's hard to sort of piece all that together into a cohesive picture of what's going on right now and and who's in there. But it still feels, I mean, it's easy for me and us to say because we're inside it.
00:05:49
Speaker
It still feels just from the anecdotes of working with people who are in normal organizations doing this, you know not part of the field, but doing data viz, that there's still a huge way to go.
00:06:03
Speaker
before anyone can say, yeah, we've we've
Future Directions in Data Visualization Practices
00:06:05
Speaker
nailed that. We've mastered data viz as a practice internally. But perhaps with all these tools and with all these references, all these books that we've all written, maybe there's a sense now that they don't need as much help from outside, yeah that maybe people inside can solve these things. um But then you know the question that we always ask is, so what's its what's what's the difference it's making? What's what's the effect it's having? and That, to a certain degree, beyond the public stuff, news media, for example, that is the stuff that always has and will remain elusive because it's behind the four walls of corporate undertakings.
00:06:45
Speaker
Yeah. Yeah, i I agree with all that, and I and i wonder where the data viz field... Again, there's so many different parts of the database field. So it's sort of hard to say like the database field, right? Cause there's researchers, there's a practice, there's the designers, there's yeah computer science.
00:07:03
Speaker
Um, I, I just wonder where all these different groups see things where people see things going, right. Do we need,
00:07:14
Speaker
you know, are there, are there advancements in tools yet to make? Is it, should we be focusing maybe on the, on the practitioner or freelancer trainer side? Should we be focusing more on the data prep side? I mean, I don't know about you, but that's kind of the part I've generally sort of avoided, like kind of like you've got some data, let's talk about visualizing it.
00:07:31
Speaker
There's a whole boatload of stuff that goes on behind it. Absolutely. Right. I mean, I think part of the reason why, and know, we would, we were chatting before, but part the reason why that is either hard to do or easy to not do is that first of all, every context is unique in the treatment that you need to give the data, the way that you need to transform it, prepare it, configure it, ready for its usage, sorry.
00:08:00
Speaker
And therefore to train people to do that is his hard and you know it had to narrow down two or three reliable case studies that will you know give people most situation is a way to cope. um So generally talk quite general terms about data collecting and transformation. And I know that in my mind, when I'm saying all this stuff across, let's say 30
Intent and Message in Data Visualization
00:08:25
Speaker
slides of a course, that that is something that needs 300 slides. But it's almost like, oh, well, don't worry about it. Let's just get the cool stuff.
00:08:34
Speaker
I also think though, that it's something that we you can be complacent about in your own abilities to get from A to B with just the the the built-in muscle memory that you've developed developed over years of how to quickly get a table in Excel into shape, how to reconfigure it for Tableau, long and thin, or into Flourish with all the unique sort of data sheet layouts you need each chart type to be plugged into, that you kind of forget that not everyone does know.
00:09:05
Speaker
necessarily how to quickly copy and paste a column and stick it at the bottom then do the repeat and do v lookups and even just write a little bit of vba code to automate a loop and and so there is a curse of knowledge there which i do feel guilty of that i need to address that in my materials but i also your point i do think that that is perhaps where the next focus has been you know we We have for many years talked about the little viz, certainly myself included, about the minute details.
00:09:38
Speaker
ah maybe at this point in time, not just in data viz, but in the world as it is right now, that stuff needs to be just put aside for now.
AI in Data Preparation
00:09:48
Speaker
Let's care about some more important stuff, which is why are we doing this?
00:09:52
Speaker
What's the thing you're trying to tell people or let them understand? and And maybe the bigger chunk of all this hidden iceberg, which is the heavy lifting of the data side. Yeah. It is interesting. I use, I'm not a Python coder, but I've been using chat GPT to like write code to like scrape out of like, I, you know, like pulling up to do hockey is just kind of for fun.
00:10:14
Speaker
And I do wonder whether AI changes that calculus for people, right? You don't necessarily know, need to know. Well, from my own experience, you know, I'll say, Hey, I want to scrape this table off of hockey reference.com on this page. No.
00:10:30
Speaker
And you know I'll give it a prompt. Oh, OK, here's the code. Copy, paste it, run it. I'll i'll say, even at the beginning, i was like, how do I even run Python code? it tells me how to write it. The exact same thing, yeah. Right? Run it. OK, here's an error. Oh, I got this error. OK, this is how you fix it. Oh, OK, now it runs.
00:10:44
Speaker
um You know, people can do that, obviously, with VBA. They can do that with all these tools. And um the funny thing about ChatGPT is you'll say, fix this, it'll fix it, and then it'll explain to you all the changes that it made. And it'll be like, I don't i don't actually i don't actually care. I'm not trying to learn Python. I'm just trying to accomplish a task. yeah yeah And to your point, a lot of the little viz is more aesthetic, design, subjective,
00:11:12
Speaker
human, right? Where the AI can't really answer some of those questions. Yeah. It's also the place where the subjectivity exists, right? It's where the rules don't have a ah touch point because it is so open in those bits of our world for there to be several right answers.
00:11:31
Speaker
Yeah. the The thing is that when you've got that chat GPT intervention is... are you able to validate it with your own eyeballs?
00:11:44
Speaker
So you can look back at that original hockey table that you've scraped and you can do like 10 quick checks. If that's looking right, if that's looking right, I'm i'm pretty sure. And don't get me wrong, humans will make mistakes even if they do it by hand. So it's not that you know we're flawless, but when you then introduce such assistance by some other tool to to do this en masse where there could be more jeopardy of it getting, you know,
00:12:11
Speaker
Let's imagine there's ah there's a merge cell in a table. you know there ah yeah These are always happening. um And it doesn't handle that correctly. And it's worn out. And then everything's worn out. do you have the scope to be able to check those things? So that's where there's still that, I guess, that hesitancy. But it shouldn't mean that we shouldn't use these
AI's Role in Data Processing and Critique
00:12:27
Speaker
tools. And I've done exactly the same with Python. Never learned it.
00:12:30
Speaker
But now I feel that I've got a route into it without having to learn it. yeah But um but it it is the logical place for AI to live. Yeah, and I think there's a difference between messing around.
00:12:43
Speaker
i want to make a line chart of Alexander Ovechkin's goals and, you know, doing an analysis that's going to affect, you know, people's livelihoods or how they get their benefit right like yeah there's there's a triviality the topic yeah yeah exactly like you know who cares if i get the ovechkin thing wrong because oh yeah espn's gonna do it right so um or or even just like i'm doing it kind of as a hobby whereas you've worked for you worked with arsenal as i recall yeah arsenal right so like your work with them like
00:13:15
Speaker
it's a little bit different when you're like working with the team because they're making decisions yeah based on your analysis. And that's, yeah that's a different story. um I wonder what your take is right now on um critique in the field, because when we were sort of coming in and the data, of the the Twitter data viz was all a rage, like, you know, there was always a lot of conversation about what is the right sort of level and temperature and attitude around critique and um don't know has the fragmentation of of social media has that changed your calculus that you do you think of it differently now a little bit i think partly because the fragmentation means that the communities that are popping up are little bit smaller a little bit more intimate a bit more personal and at this point uh still friendly
00:14:09
Speaker
And still convivial and still kind of constructive. I think when things become so huge, I mean, not to pick on this, but, you know, think about Reddit when everything's so huge, so voluminous in traffic of people and so largely anonymous, you know, the kind of, the gloves are off a little bit more so than when you've got, you know, a person that you're interacting with. But in terms of the actual nature of the critique, I think it's, I do think we've come a long way in letting things go.
00:14:42
Speaker
Even if we don't necessarily subscribe to the choices that we witness, we've probably, we've done enough laps of this world to know that there's probably a reason why they've done that. Yeah. And rather than me say, Oh, I hate that red that you've picked.
00:14:56
Speaker
We can probably think, you know what? They've probably been given that as a limited palette to pick from. There's probably a design criteria. They've probably tested it with a certain print, whatever it may be. So I think when when you have been around the block a few times, you are able to sort of just step off the gas a little bit and think, you know what, I'm sure if i understood the real context of that thing, that that would that would be fine.
00:15:20
Speaker
That would be the least worst solution rather the best. So I think there's just almost a, certainly from my perspective, there's there's a little bit less of, let's pounce on this thing and and start to yeah point out the ways it could be improved. But I think that last point there is is important, is that And it's always stood the same way, which is if you're going to have a go at something in terms of it's not good or the ways that it's it's not showing the best practices, you've got to back up with why and what you do differently.
00:15:49
Speaker
And the what you do differently therefore needs to be cognizant of what the context was. I mean, you know, 17 years into this field and we're still saying it depends and and for good reason because it does. You don't know what people have been encountering.
00:16:03
Speaker
Yeah. but But back to the tools then, it becomes a lot easier to take someone's design. I haven't done this, but I assume it's easier now to drop it into one of these, you know, like mid journey or AI image tools and say, hey, change all the red color to blue.
00:16:19
Speaker
Yeah. And so in some ways the critique, i don't know if that's a good thing or a bad thing for critique because it doesn't require you to think It's hard necessarily. Like what are the constraints when you build that thing, that dashboard or that infographic, you can just sort of use a tool um to do it quickly. but But presumably it it changes the calculus a little bit Well, it does. and And if you've got also access you know to to the to the data sources, I mean, ah you know increasingly people do publish the data that they've used for.
00:16:53
Speaker
It's not universal by any means, but you know there's there's a good fraction of works that do come with the data set to say, you know, almost like check my workings, you know, here's the here's the source material and almost like have a go, you know, see what you can do. But um it's interesting thing, because like going back to AI, I do think one of the, i don't know if it exists yet, but one of the great things, if it did exist, would be that if you could take a chart, even just as a PNG of a chart, is there a way to scrape the data from that, the values of that data, please through the encodings reversed into a,
00:17:26
Speaker
yeah I mean, that would be a that would be a super cool thing
AI Tools and Data Extraction
00:17:29
Speaker
to have. And, um, you know, I'm sure it's achievable. It'd be even more fraud for accuracy, but as a starter, as a starter, I mean, there's this, there's, there is that tool plot digitizer, which I've never gotten to. know have done gun Right.
00:17:43
Speaker
But yeah, you, you know, even just to mock up, mock something up, just like, Hey, I would like to try this as a different chart or, you know, just, just out of, out of curiosity.
00:17:53
Speaker
Um, so So your last comment makes me ah think about one other thing I want to ask you about. So let's go back to the Information is Beautiful Awards. You've got all of your trophies lined up behind you.
00:18:05
Speaker
And we've got um ah the DataViz Society ah has their conference outlier coming up in June. ah they have their they have They now run the awards. And I'm curious, and I know we've talked about this in the past.
00:18:17
Speaker
I'm curious now how you feel or think about DataViz Awards. And I'll caveat that because of the point you just made about the data yeah that, you know, you don't know what people have done with the data before yeah making the visualization. So, so that's sort of the, yeah. Yeah. moona like It's a great point. I'm i'm just trying to think, I think from memories from like Francis Gagnon as well has been pushing this button and as well, which is if you're judging these things just on the aesthetics, then kind of make that, but make that the,
00:18:53
Speaker
the goal of the awards. If it's about the, the the work itself, that it needs to be a, a more sort of fundamental judgment of, you know, is it faithful to the data? Is it disguising things? But again, I think the awards to me, I've always learned more on the side of, um, not getting too wound up by the notion or the nature of awards for very subjective, um,
00:19:22
Speaker
products or or works. and And I think really, although yeah you know you you never want to be like not winning ah or like left out the long list into the shortlist, I think it's probably best for most people's buying sets to probably just chill a little bit and just say, well, and for those who win, it's a really nice thing and yeah let them have it.
00:19:44
Speaker
For those who are judging, it's a pain because yeah you're rattling through all these different works, you're limiting time. you start off very thorough with the first five and then you get into the 15 to 20. It's like, Oh God, I've i'm seen enough now. And, and I, and I think when, you know, to be fair, the, the awards that you mentioned that they do go to a really great efforts to get a load of eyeballs.
00:20:09
Speaker
Look at these, not everyone looking at everything, but certainly a a nice way to just cushion the idea. It's not just one person looking at piece. So again, with that language of it's the,
00:20:22
Speaker
it's the least worst way to to do this stuff. And yeah if you don't win,
Writing and Updating Andy's Book
00:20:27
Speaker
don't worry about it. If you do win, stick it on a top, a top shelf on ah for about 10 years. And then I've asked questions about whether it needs to be dusted off and put away. All right. Sage advice from, from captain Kirk. um So we've talked about, um we've talked a little bit about tools. We've talked about, you know, lots of different aspects of sort of,
00:20:49
Speaker
today's data is creation process. um The one thing we haven't talked about is doing it with it. This is a very, I'm going to right up front, by the way, this is a very good segue that I'm doing right now. um We haven't talked about, we haven't talked about creating data visualizations within a team or within an organization. And that's what your book sort of tries to help people do. Yeah. What a bridge that It's pretty good.
00:21:13
Speaker
Right. ah It just came to me too. I had not. let it Yeah. So, Third edition. Third edition, right? Third edition. That was one of it, actually. Imagine imagine imagine that. and You've got it to hold up. Look at that. Now, folks listening, of course, can't see that, but they can imagine it because they all have it with that lovely, colorful swirl. Circular thing. Yeah. um So what is what is the third?
00:21:40
Speaker
what is okay so two-part question. ah What does a third edition do? And is it, is it, um, have you left things out from the first edition that people should be like, i need to get all three.
00:21:52
Speaker
Like, is this now, now, now are you just trying to like just gain revenue or like, yeah, what's the, what's the third edition doing? Well, what level of cynicalness is it? thats That's the thrust of the question.
00:22:06
Speaker
Um, you know what? So I was asked quite a few marches, I think it may be, might be three years ago. i was asked to, um, to agree to do a third edition.
00:22:16
Speaker
And listen, this is all in the context of a a niche marketplace, but that the publishers, Sage, have always been really nice. i' really enjoy working with them. they They asked me that because within their parameters, it's seen as a good seller. I'd never say best seller, but it's worthy of a refresh from their perspective.
00:22:35
Speaker
e When I was asked though, I was thinking, well, I don't really know what is new to warrant a third edition. But I had that as well with the second edition. um Now, the second edition in that case is often a little bit of a, well, let's say that the first book was the first draft.
00:22:54
Speaker
Can we do it properly this time? Can eradicate some of the they know and the inefficient language, and which I definitely look back and think, Jesus, I was chewing up some of those words there.
00:23:05
Speaker
Yeah. but um But the third edition was interesting because actually when when you look at it from a distance, it is five, six years since the second edition. And I felt that in that five or six years, I've continued to evolve in my lens of what data vis means to me.
00:23:21
Speaker
yeah um And you always want to update things with new examples and new reference points so to to reflect the the new work and the new people doing the new work.
00:23:32
Speaker
So there is that part of it, definitely. but What I found actually interesting when I got into it, um, last January, when I had two months left to do it was the, every page I was looking at was thing. I was seeing opportunities to either rephrase stuff, remove stuff or add stuff that just felt new ways to express things.
00:23:55
Speaker
and And the way that I've kind of concluded all this is that it, to me, it's the same house. If the book is a house, it's the same house. the same rooms, those rooms still save the same purpose, same purpose. So the kitchen still exists as the kitchen, but I've ripped out a lot of the units and the appliances. I've changed the wallpaper, changed the flooring.
00:24:18
Speaker
Um, I've got rid of the shed so that there is one bit I've got rid of. So we got rid of the shed that wasn't serving purpose anymore. um Um, but yeah, the the whole thing has just been almost, you know, re-engineered reconstructive, but it feels to me like, um,
00:24:33
Speaker
you know, a bit of el leap forward in terms of how I think about the field and how I want to express the field. I would say that the biggest, you know, I never want anyone to spend more money on books from me if you've already got the books or one of the books.
00:24:46
Speaker
There are some completists out there who I love dearly, but I would suggest that if you've got the first version, sorry, the first edition, there should be enough clear water to warrant possibly third edition if you've got a desire to kind of grow your bookshelf.
00:25:04
Speaker
um I would always say buy someone else's book first if you feel there's something missing. ah Maybe the second edition to the third edition to an outside lens isn't as big a gap as I see it.
00:25:16
Speaker
um But, you know, I never want to prejudge how how people want to spend their hard-earned money on yeah um my products. But it's to me, it's it's more just those arriving into the field now.
00:25:29
Speaker
Those for whom this is the first book that they buy with me, I feel confident that, yes, this edition is the latest version of me and my brain. right and For others, it's up to them if they want to decide to jump onto a new edition. But it's an interesting challenge. You've done a second edition, right?
00:25:46
Speaker
I have not. Oh, you've just done different books. Yeah, and I've talked about ah doing a second edition of the of the Excel book, um but that's a different game. That's because the the technology and the tools have changed, and Excel itself has has changed in lot different ways.
00:26:02
Speaker
Right, yeah. ah But ah yeah, I haven't done it. But I'm i'm curious, aside from ah ripping out the shed, what are what's like, in your mind, what's the, and aside from, i don't know, some of the but changes in language and and exposition,
00:26:20
Speaker
what's like the biggest, yeah. What's the biggest renovation you did? Yeah. The biggest renovation is on the biggest chapter, which is the centerpiece chapter, um, about charts.
00:26:31
Speaker
So that act, that's so that chart chapter has always acted as the, almost in some respects, the kind standalone section where, although you can go cover to cover in most cases, people just referring to that as the gallery of chart options.
00:26:47
Speaker
but In any given day, they might just flick through and think, oh, it could be that, could be that. ah yeah In the past, I had, I don't know, something like 40 plus, maybe 48 charts um organized by these five families that i tend to organize things into.
00:27:02
Speaker
and But they were always different examples of each chart in terms of ah might have asked someone to give me permission to use their Sankey diagram. And then somebody else gave me permission to use their choropleth map. And they were all very isolated examples that looked different in terms of style, they'd have been
Illustrating Charts with Consistent Data Sets
00:27:21
Speaker
different topics. But in this edition, what I've done, it kind of came to my mind about three days before the chapter was due, felt the need for greater flow from one page to the next, from one chart to next.
00:27:34
Speaker
I felt the need not just to make them all look the same in terms of style and and color palette and be made by me in the same tool, but also be about the same thing.
00:27:45
Speaker
And so what I found was first of all, flourish to me. Um, think by default, the three version flourish enabled me to make about 37 of the eventual 40 charts that I included.
00:27:57
Speaker
ah had to strip back to 40. There was, there was a page length, sorry, ah a book page length, uh, issue where I thought, right, 40, um, managed to get, make the other three, um, uh, with a little bit of a hack and a workaround, but, um,
00:28:12
Speaker
But I ah also did it on the same topic. it was all about Nobel laureates. There's a data set out there that gave me a really nice, neat data set, thousand records. Beautiful. ah We had dates and categories and relationships and locations and quantification stuff. And it just felt the perfect all rounder that I could then just quickly pull together some ideas for 40 pieces of analyses for each of the 40 different chart types. So that, yeah when you're reading the book chapter, you're not having to remind you, okay, so this thing's now about COVID and this chart's now about sport and this is about politics.
00:28:51
Speaker
It's all about the same thing. So it should enable you not just to sort of follow the chapter through with a ah lot lot more efficiency and kind of gracefulness between pages, but also learn the language. This is the crucial thing to me.
00:29:05
Speaker
Learn the language of the questions that these charts answer because they're the roles. And if you start to interchange the language of, oh well, he's he's used Nobel Laureate prize winner it there, but that could be a department.
00:29:19
Speaker
It could be um sports team. It could be political party. You could interchange these objects in the language of the questions been answered that, you know, gives readers that more,
00:29:31
Speaker
um that almost kind of, ah that kind of flexibility to be able to say okay, well, that was about Nobel laureates, but I can see now how that language applies to my work place.
00:29:42
Speaker
analysis case study just by changing some of these objects.
Getting Featured in Andy's Newsletter
00:29:46
Speaker
Because to me, and you know, it's not new unique to me but why by any means, but to me, language is so important in data fits that the thing you try to show, the thing you try to tell, the thing you try to answer comes as before, in a sense, the chart you you're picking. And so, yeah and and actually going back to chat GPT and the likes, that to me is potentially where there is a real role for these um tools to be able to give people a sense of, if I want to show X statement yeah of and and analysis visually, which are the best charts or which are the charts I could come to using.
00:30:27
Speaker
yeah So I think that is, and it's ah it's a little bit like the old basis of how Tableau is. Is it the kind of, yeah. thisql have Yeah. And the show me tab. yeah yeah that That's kind of underpaying of a lot of this stuff, you know the the written constructs of what analysis youre showing. so So that, to your question, that is the biggest change.
00:30:45
Speaker
It was the most amount of work to to put into it, but it's the thing that I'm kind of most satisfied that I had the idea and sort of saw it through and didn't take the shortcut, which was,
00:30:56
Speaker
ah no Just get lots of other examples and and and stick them in there and just go again. sir it is ah It is an interesting balance, right? Like, should you go do it all yourself? Do you want to spend the year your your time making them all yourself do you want to spend your time asking people and they have their pros and cons and yeah they're both just... Licensing.
00:31:18
Speaker
yeah, the creation one is is, a little bit more fun. Although a little bit more frustrating sometimes when you're just like I just want to get these three charts done. I got to reshape my data once again. Why? Why?
00:31:32
Speaker
i mean, there must have been, there were points where thinking this sunburst chart is just not interesting. but Yeah. Right. yeah That's the thing. need to show it as an example. So, yeah. Yeah. So we're almost done. And and folks are like, ah you know, folks are listening. It's like, I got to get to work, but, um,
00:31:48
Speaker
What did you do when you got to a point, like, you know, you create a chart, you've got this data set, you have this nice kind of story. I'll call it a story. And you come to a chart type and you're like, this is just not that interesting. You're just like, it's okay because it's just, the point is to be illustrative to like, it is. yeah Yeah. That's the way that I kind of made peace with it, but also explain it in the book is that, you know, the,
00:32:11
Speaker
these 40 charts aren't revealing 40 groundbreaking pieces of analysis. Yeah. I'd there's about seven that revealed something of interest to me. It's more a showcase of the method in practice based on real data to give you a sense of if you're wanting to show this thing or answer this question, here's a method that will do the job for you.
00:32:32
Speaker
Gotcha. Okay. I have one last question for you. So when I first came in the field, you were already the, you know, one of the big people on campus. And one of my goals was to get into visualizing data's, i think at that point it was a monthly newsletter, right? It was like the it was the best stuff. It was the best of, yeah, the blog posts, yeah.
00:32:52
Speaker
Right. And, um, so I, got I got, you know, once I, what I felt like once I got into one of those, I had sort of made it right. That was my sort of goal. So now you've got, ah now you've got your newsletter.
00:33:03
Speaker
Um, I don't think you, I'm looking at it right now. Uh, is it just, it just gave me my, my thing. So I don't, it's got like separate sections. There's a section on visuals. It's not necessarily, you don't call it best of anymore, but how do people, there's someone out there who's like, I want to get into Andy's newsletter.
00:33:19
Speaker
Oh, interesting. yeah What's their way to do it? How do they get there? The way to do it is please don't email me to say, can I be your newsletter? That's it. That's the number one thing. It's like, if you ask, you're out. Okay, I like it. I think it's a good criteria, actually. I do and i do appreciate that. But then i'm um I'm too soft to say, no, you're out. you know I'll find a way to get it.
00:33:40
Speaker
Right. It's just make your work public. I mean, ahlthough although we spoke earlier about the fragmentation of the Dataviz community, Yeah. Good work will find its way to my eyeballs.
00:33:53
Speaker
see Not because I'm tracking everything, but I track people who track things and they track things. and And I use this kind of a sort of pyramid of sort of trusted eyeballs and and curators that help me to get a center what's out there.
00:34:07
Speaker
um But yeah, the the newsletter is 50 things that I've encountered the last month. Now ah do encounter When I kind of come down to my bookmarks, there's usually about 200 things there. So I do have to go through a process of, in some respects, forming what I think is the best stuff of those 200.
00:34:24
Speaker
um But it sometimes it's just also a variety of stuff. you know You might have 20 great pieces, but they're all about, for example, this month, you might have had 20 great pieces about the wildfires in California. Well, I'm not going to show 20 pieces about that, but I might show two.
00:34:39
Speaker
and then i'll I'll move on to another topic. so It is extremely subjective and instinctive on the day, which the day was today, this morning. um It's something that i I did drop off because i found it tremendously hard work and it still remains hard work because I don't want to automate it. I don't want to outsource it. I want to kind of still do it myself because it gives it that extra edge of authenticity.
00:35:03
Speaker
What I do find is that what i do find is the There are fewer works these days from slightly more independent producers.
00:35:13
Speaker
There is still a dominance ah of the news media because their work is very public and very shared amongst the public. So, ah you know, if ah said don't email me, if you've got a great piece that isn't getting traction, but you know it's a good quality, write me an email, but don't ask me to put it in, ask me to consider.
00:35:32
Speaker
All right. This is key. This is important stuff for people who are listening. say This is, this is key. This is the stuff that, that the only get, uh, only get on when you start listening. But don't be offended or hurt if it doesn't make it. Cause it's not a reflection of the work. It might just be that month. There's, you know, your Sankey diagram is great, but there's 10 others that have had to sit through. So, you know, it's just,
00:35:52
Speaker
Well, and the other thing that you do, and I think you did this with the best of, right, the with the monthly ones, is that you write whatever, like a sentence or two on each of these things, which, like, that's taking a bunch of time. I mean, that's that's work. It is. And don't get me wrong. Sometimes I'm a bit lazy. I'll just grab the the extra excerpt from the piece. But usually it's a good articulation of what it is anyway. So it's just other people's voice. But yeah it'll be the thing that i've picked that matches why i you might have included it, sir.
00:36:22
Speaker
All right, so they can find you on visualizingdata.com with two S's. All the S's. All the S's. There's no even though. We don't need to get into the even though.
00:36:33
Speaker
No, we don't. Andy, always a pleasure. to see you. Thank you, John. Thanks so much. Good to see Likewise, mate. Thanks everyone for tuning I hope you enjoyed that conversation with the one and only Andy Kirk.
00:36:46
Speaker
Make sure you check out his book. Make sure check out his site, visualizingdata.com and make sure you sign up for his newsletter. comes out, I think once a month or so with the best of the data visualization web, definitely worth your time. So until next time, actually next week, this has been the policy of this podcast. Thanks so much for listening.