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Episode #27: Dear Data Wrap-Up
146 Plays9 years ago
For the final episode in 2015, I’m excited to welcome Giorgia Lupi and Stefanie Posavec back to the show to talk about their year of Dear Data, their analog data visualization project. Our conversation is a bit broader than just...
The post Episode #27: Dear Data Wrap-Up appeared first on PolicyViz.
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Transcript
Introduction to MICA's Program & Dear Data Project
00:00:00
Speaker
This episode of the Policy Viz podcast is brought to you by the Maryland Institute College of Art. MICA's online graduate program and information visualization trains designers and analysts to translate data into compelling visual narratives. Join expert faculty such as Andy Kirk, Marissa Peacock, and John Schwabisch to mine the data and design the story. For more information, go to mica.edu backslash MPS in Viz.
00:00:38
Speaker
Welcome back to the PolicyViz Podcast. I'm your host, John Schwabisch. This is the final episode of 2015. I hope everyone's having a great winter and holiday season. I'm very excited for guests on this last episode, Georgia Loopy and Stephanie Posovic, authors of the now famous Dear Data Project. Georgia, Stephanie, welcome back to the show.
00:01:02
Speaker
Hi, thank you, John. Thanks for coming back. We had you on a very second episode and now closing out the year as your project closes out and the year closes out. So for those of you who don't know, Dear Data, 52 hand-drawn postcards shipped across the Atlantic.
00:01:20
Speaker
Georgia and Stephanie sharing personal data, personal information as they collected it and hand-drawn very specific, very personal things about their lives and about their experiences. Lots of great coverage about the project, blog posts and websites and interviews. And so today what I'm hoping we can do is talk a little bit more broadly about how the project changed your experience, your process, and how you look at maybe the world and maybe you look at the field of data visualization. So I want to start
00:01:49
Speaker
by asking you how this project maybe changed your approach to other projects that you're working on. I can just like get started.
Creative Impact of Dear Data Project
00:02:00
Speaker
Well, John, you know, it's hard to tell because, I mean, we were already both sketching with data before and sketching has always been a part of our process on working with data.
00:02:12
Speaker
And I'd say just to introduce that we embarked in this year-long analog effort because we already have this similar analog approach to data visualization as we always tell. But I guess the way personally experimenting with so many visuals like once a week, it boosted my creativity, even if I hate the word creativity, but I don't really have a better one right now. But you know what I mean?
00:02:38
Speaker
And 52 projects like mini projects over a year makes you confident that you can pull up ideas and make them concrete and tangible very quickly, which is a very amazing benefit of doing sort of like side project and personal project when you don't really have clients looking over your shoulder. But Stephanie, we can just like talk about.
00:03:01
Speaker
Yeah, I think it changed my process in that having to do something weekly. I mean, it's quite a lot of work to do every week. And so you just had to kind of put your head down and gather the data and look for patterns and look for an interesting way of framing and presenting that data and drawing that data so quickly that I have found that it has sped up my working process much more.
00:03:28
Speaker
And then also, I think I would often draw at the beginning of a data visualization process, but it has influenced my art practice in that I've started to create responses to new commissions that are hand-drawn using data off the back of Dear Data. So this is something I was always afraid of before.
Analog Approach Benefits
00:03:49
Speaker
And then I've also been teaching this autumn a lot of data drawing workshops to get people interested
00:03:56
Speaker
You know, learning about data visualizations for this very kind of handmade, analog, lo-fi approach to understanding how you visualize data. So it's definitely influenced a lot of different facets of my professional career.
00:04:14
Speaker
I think a lot of people jump into data visualization with finding some tool, finding some computer program to start making things and to sort of have them step back, to teach them to step back and say, you can make data visualization by using crayons is, I think, helpful, right?
00:04:28
Speaker
Yeah, also, I mean, my profession, my day job is a little different from Stephanie's because, you know, with Accurate, right now, most of the time, of course, we have to work digitally. But I always like also to talk to my clients about the dear data effort because I like to say that really by removing the technology from the equation and by really hand drawing your data, we've been
00:04:50
Speaker
spending time with our data and we were forced to extend ourselves as designers and to come up with visuals that were really, really customized to the dataset that we were analyzing. Also, when you think about big data, it's all about making it meaningful, contestable, smarter, and smaller, and understandable. For sure, data is not only a matter of technology and this project,
00:05:17
Speaker
I guess it really taught me and taught us to look at data through this angle. It's mostly a matter of how every time we collect, we process, and we relate with information. And it's also a matter of how we design the ways to look at data. So this year-long, very, very analog and laborious effort, I guess it reminded these concepts very vividly to us, to me at least.
00:05:42
Speaker
And when you talk about the actual working with the data, do you find that you're, that you're more careful with data now, you know, sort of out in your, in, in other projects you're working on, you're more careful with data, less careful with data. Do you think maybe a little bit more about the people who are providing us with all the data that we see all the time? Is that, is that now sort of more part of your process and your thought process as you work with
Experimentation with Personal Data
00:06:05
Speaker
data?
00:06:05
Speaker
I think for me, generally, it has given me an insight into this other side of data visualization, because the way that I work normally is I work with a researcher, I work with someone who is finding all of that data, and then I always work on the design side. So it has given me an insight into that. But I think I'm probably less careful with data in that
00:06:29
Speaker
Well, because it's personal data. And so I am just testing things out. I know I'm not going to break the data. I know it's not actually critical data. So I feel like it's a really good space to learn and to figure out how to do things because I know that it isn't critical. So I found it a space for learning, and that's actually been really useful. So it's a place for me to make mistakes while knowing that my mistakes actually
00:06:59
Speaker
won't hurt anyone. So that's how I found it useful. Yeah, it's interesting. Well, another thing that I'd like to say is, I feel like a lot of people think that their data change our whole perception and approach to our work. And I mean, don't get me wrong, it has been an exciting and very, very helpful, as we are just like saying,
00:07:20
Speaker
year-long research lab in a way. But I'd like to add that we cooked up and made your data exactly because we were already valuing the process of collecting, cleaning, processing, and publishing data, I think. What I'd say to your question is we learned that spending time with your data and having to count and visualize data manually
00:07:41
Speaker
reinforce the idea that it is a necessary practice to get to know your data, to understand your data at a deeper level. And shortcuts and easy to use tools to pull in data and get a simple charge in return are amazing. It's just different. The real process of spending time with data this way. And just to conclude that, talking about personal data gathering,
00:08:06
Speaker
You know, we live in this moment where personal data apps, tracking apps are proliferating. We have a lot of apps that can detect and aggregate and visualize data for us. But we believe, and I believe that specifically to personal data, to gain real meaning from these data sets,
00:08:25
Speaker
Anyone who's interested in their data should really focus and engage in sense-making, interpretation of those numbers according to their personal stories, behavior, and again, spend a little time with their data without expecting that an app can tell them how to interpret the data, if you know what I mean.
Data Collection as a Hobby
00:08:43
Speaker
And I'll just add one final thing on here. Just thinking about this idea about people who collect and publish data. I think that when you're going back to by hand, I always sort of say that what we were doing is very similar to when you go to a baseball game and you score a baseball game. And so people are gathering data on the game.
00:09:04
Speaker
But they're kind of doing it to get into the game to like, you know, you're doing it increases your enjoyment of the game, but you don't generally like reflect upon it later. I mean, that's, that's not really the whole point of keeping score. So this is just another way. Yeah. Like Georgia says spending time with your data, but also like.
00:09:21
Speaker
Yeah, keeping score enjoying data gathering for data's sake, like for data gathering sake, which is something that like, even non data specialists do, you know, at baseball games, at cricket games, it's a good thing. So
00:09:36
Speaker
No, and that's a great point, and it's certainly the case, you know, I've got my Fitbit on right now, but the wearables and those sorts of things, in some ways, it's sort of done, well, it is, it's done for you, right? It tells you how many steps. The project that you've just completed, I guess, is more about this, you know, actually having to do the work to collect the data, which I think sets it, which is one of the reasons why it really sets it apart from, you know, what it's called.
00:10:03
Speaker
I think that piece of it, actually taking the time to sit down and collect it yourself, sets it really apart from the rest of this movement. I would say it's that joy of keeping score, that joy of collecting, which people collect so many different things. They collect stamps, they collect coins, they birdwatch, and this is just another type of collecting, really.
00:10:25
Speaker
Yeah, and also it's more really towards your story and towards what you want to know about your data. Because as we've been doing for 52 weeks, every week we would have a main question that is the topic of the week and the angle that we wanted to address the question. But then what we did that no apps can do is every time we added contextual details.
Context and Vulnerability in Data
00:10:48
Speaker
And so, for example, if the topic is
00:10:50
Speaker
my complaints is not only what I complain about, but it's everything that is around that. So what was the situation? Was it really needed? Who I was complaining to? And what was the reaction of the other person? So all of this kind of data that you can only only track if you are active. And really active in finding out what you want to know about yourself by how you set up the questions.
00:11:12
Speaker
Yeah, that's really interesting. So let me take that point a little bit further. So a lot of the things that you've put into these cards and posted online, they're all very personal pieces of information. How many times you swear, how many times you smile, how many times you laugh.
00:11:28
Speaker
Have you found that that has changed your approach to honesty, how you approach both personally and professionally, how you interact with people, how you interact with colleagues? So how has the project, I guess, changed your interaction with other people, especially when it comes to being more or maybe less honest with people?
00:11:48
Speaker
I guess we both agreed that it did change our idea of honesty. And we realize how being honest in general helps make real connection with people because we all are human beings. We have flaws, habits, negative feelings, strange routines, but this is how a whole person looks like. And also,
00:12:16
Speaker
honestly starting to share those vulnerable side of yours in this sort of objective and quantitative way from from one hand helps not being afraid of doing it and also like from the other hands really is what people like the most that was resonate with people and that's what you know it makes you human and vulnerable but but not
00:12:38
Speaker
not necessary it's not necessarily a negative things i really really believe that is a positive things to make real connection to people that just you know i think it could. With everyone because we everyone are like that yeah.
00:12:51
Speaker
And I guess I think, I don't know, I think what it just reminded me is, like, why people find data visualizations compelling and, and why they're drawn to them. And it is because, I mean, it is because they have this semblance of honesty and truth. I mean, like,
00:13:10
Speaker
that is the ultimate goal, isn't it? And I think that that's, yeah, people like honesty and truth is why people are interested in the field we work in, in the first place. But that honesty and truth, it's like one thing when you are, you know, whatever, you're looking at global warming, it's another thing when you are publishing your own experience for the world to view, right?
00:13:34
Speaker
Yeah, I mean, I think one thing that I've realized, well, I mean, of course we all know, but, you know, that a data set is never, never really objective, because I would be very honest in the data that I personally collected, you know, like, for example, like data on physical contact with my husband, I
00:13:54
Speaker
I was very honest in like what I collected and so like for me personally for like my personal insights about a week or about my life like I was very honest I made sure I was very honest and open but that's not to say that I was very as explicit
00:14:12
Speaker
in how I categorize those honest moments on a postcard. You know what I mean? It did make me more honest. I think Georgia knows more about me and my life for the last year than a lot of my friends in London know. But that's not to say that I was honest in my data collecting for myself, but it's all about how you categorize it. You don't want to give too many things away.
00:14:41
Speaker
I'm sure. Okay. So, um, so I want to talk, so I want to turn a little bit and talk about, so we've talked about honesty. I want to talk about curiosity
Curiosity and Analog Methods in Data Visualization
00:14:51
Speaker
a little bit. I mean, clearly one of the things that I think drew people to the project was, was this visual expression of curiosity. And I'm curious whether you feel that the field of data visualization is
00:15:07
Speaker
Does it have enough curiosity? Does it need to have more curiosity? What about the other fields that are sort of related to data visualization like open data and big data? Just sort of thinking more broadly about the field itself. Where do you sort of think the field is? Is the field too much in the software and not enough into the analog world? Not thinking hard enough about the data, not being curious enough and thinking about
00:15:30
Speaker
different ways to present data as opposed to just, you know, let's stop arguing about whether a pie chart is a good chart type, but, you know, where is the field headed and is it, you know, curious enough and honest enough?
00:15:42
Speaker
That's an interesting question. Just to narrow it down a little bit, I'm curious about what you exactly mean with curiosity. Do you mean explorations of new way to present data? Or do you also generally mean something about the increasingly more popular interest outside the design data world to the data world? Just to frame the question. Yeah, just to frame a little bit. So I guess, so I don't really know.
00:16:10
Speaker
I mean, so I think this project, the Dear Data Project is unique in lots of different ways. And one of the ways that it's unique is the things that we've been talking about. That's your personal data. It's presented in an analog way. And that, I think, is a sense of curiosity in your personal experiences that is unique in what people are
00:16:31
Speaker
often presenting in the field of data visualization. And the work that both of you do I think is one of the reasons why the project is so great because the stuff that Accurate does is sort of very exploratory visualizations and the art that Stephanie does is sort of a different end of the spectrum of the field.
00:16:52
Speaker
So I guess my question is really on, maybe the question is really on, are people getting hung up a little bit too much on arguing about the standard visualization types and not thinking more broadly about different visualization types, different data types, collecting different data types, or maybe the field is doing a great job of that. I'm just curious on your perspective on sort of the state of the field as we close out 2015. So maybe it's more just like,
00:17:21
Speaker
best hits of 2015 sort of thing? Oh, well, I mean, I think just responding to your idea of whether people are
Embracing Imperfection
00:17:29
Speaker
arguing about like, what's the right chart? I mean, like, obviously, like, this is really important for, you know, it's important to do things properly and to do things well, but I do feel that being so black and white about what's right and wrong can in many ways hinder creativity because it makes people afraid to experiment and to be imperfect and to make like mistakes and to explore and to move further. So,
00:17:59
Speaker
I think yeah there is this thing that I have realized is that like imperfection is a sign of extending the edges of a field and so it's like with our year-long project we've made a lot of mistakes and we know that not all of the ways we presented the data are probably the best ways but I you know in maybe our data sets sets that we gathered were as
00:18:22
Speaker
perfect as they could have been because we were gathering laughter or something incredibly difficult to gather. But we're still kind of moving the edges of our space a little bit further out. Even if it's not right, we're getting nearer and nearer to making something that's new and different and quote, right. So I think like imperfection is a sign of exploration. And so like there needs to be kind of
00:18:48
Speaker
an embrace of that instead of always arguing about this very black and white sort of way of doing things.
00:18:56
Speaker
Yeah, I guess I really agree with Stephanie. I also think that there is room and space for everyone, and I hate to answer with it depends, but I guess it does depend on the goal, the scope at hand, the kind of data that you're working with, and yeah, the scope of the project you're working with every time. But I think that what we, just like taking it a little back to why we've been doing your data,
00:19:26
Speaker
We really think that despite the number of tools available that can help practically everybody visualizing their data and despite the growing number of people in the field, it is still a very exciting time for being a data visualization designer because exactly because projects and opportunity get more and more complex and challenging and because of the field of data visualization is growing, we really I guess that we have the
00:19:53
Speaker
We need to still find new languages. We need to still explore, also through these very radical projects, how to convey those knowledge and to inspire feelings at the same time with data, also being, you know,
00:20:09
Speaker
faithful to scientific accuracy but at the same time allowing space for exceptions and for experiments like this one that as Stephanie was saying can help just like pushing forward a little bit what can be done with visualization.
00:20:27
Speaker
Just to conclude, we have been forced over a year to find 52 new visual languages. That was also our idea, like having to do a new visual with data every week. And of course, you didn't want to repeat yourself, as though every postcard wanted to be a little different from the postcard.
00:20:46
Speaker
that you've been doing before because you wanted to form a very various collection of 52 posters and that was exactly what helped us really finding new languages and maybe some of those strange data visualization that have been hand drawn can become something that can be, I don't know, a new way for visualizing a specific thing.
00:21:06
Speaker
So I don't know if we answered your question. No, I know. I think so. I guess I would also sort of extend the question to ask whether you feel that the discussion in the field is a little too curt, where people, you know, it's a lot of comments on Twitter that, you know, it's only so many characters with an image that says this thing is terrible, but then there's no sort of elaboration on that. And whether you think
00:21:31
Speaker
There's a place or there are ways in which people could improve the conversation. I think from my perspective over the last year or two, I've seen a real shift in the conversation. It's trying, I think, in general to be a bit more constructive. But I'm curious, I mean,
00:21:47
Speaker
Like Stephanie, like you said, you know, you're putting out a lot of different types of data you're creating. Georgia, as you mentioned, creating non-standard types of visualizations. So I'm wondering, do you feel that creating deer data in the way that you did with hand-drawn on postcards got you away from what might be the regular sort of barrage of critique because it wasn't created in like Tableau or Excel?
Non-Standard Visualizations
00:22:10
Speaker
Oh, yeah, I mean, definitely. I think the first few postcards we were worrying about, what will the community say? How will this be redesigned on Twitter and will people not like it? And I don't know, I think like that's just not... That is a very restrictive way of thinking, to have that hanging over your head. But it was really nice that as we kept going, we...
00:22:39
Speaker
We're like, this is going to just be what it is. And we're learning about what makes a good data visualization and some are better than others. And like, I think it really, we became more free as we went through the year. And I mean, I understand that like what we're doing is kind of, is it, yeah, it's drawing, it's probably, you know, it's more of an art project. It definitely gave up, like separated us and gave us the sandbox to experiment and,
00:23:04
Speaker
But yeah, at the beginning, you're just there, you have this fear for the first few weeks where you're just like, everyone's going to talk about how bad this is. I mean, yeah, it's just not a good way. It's a shame that this is how we feel that we tiptoe out there because we're afraid of just trying something. Yeah, but I absolutely agree. I find all of the conversation within our community, if you can call it a community,
00:23:34
Speaker
interesting and exciting and everything. But I guess probably we are more interested in is also like taking them outside. And so the idea that we had with your data and then surprisingly it has been really well received is also to try to talk about data and to speak about data to people that are not necessarily data visualization designers, data scientists, and data people.
00:24:00
Speaker
That is really something that we wanted to do from the beginning. So to try to find a way again to make data be felt as more human and more approachable to even to people that are not experts. And so really just to extend the conversation even beyond our community.
00:24:21
Speaker
Yeah. No, I think that's great. Cause I think as even though lots of people sort of in the field are like, Oh, data is great. A lot of people are afraid of data and not really sure how to, how to use it to be able to demonstrate that you don't need to have a PhD in statistics to write down how many smiles you had in a week as I think is great. Um, I want to close up by talking about next steps. So, um, where, where are you going next with deer data?
Dear Data Book Announcement
00:24:49
Speaker
Well, should I announce the news? Yeah, go ahead. It's already announced everywhere, but we have gotten a book deal with Penguin Random House in the UK for their imprint particular books. We'll be making a book of the project and it'll be out in September 2016 in Georgia. We'll just tell you a little more about what we're doing in the book.
00:25:16
Speaker
Yeah, first of all, we are really the happiest to be working with a such a prestigious publisher such as Penguin. And also, we are very happy that your data is not over. So we have, you know, some more work to do on that. And what we are doing with the book is, I guess it's interesting, because we're not only we will not only be showcasing our postcards, we are finding ways to make the book more interactive for readers to inspire them to
00:25:45
Speaker
gather and draw their data but we're not going to make it how to kind of book or workbook because we think there are more interesting ways to get people compelled by data so we will you know i'm not really revealing too much but we will unfold our process and be very transparent about what we've been doing and.
00:26:04
Speaker
you know, narrating and drawing the many ways you can go about working with data in a handcrafted way. And, you know, we'd love to make a book that makes people say, oh, wow, I want to do it, rather than just I'm learning how to do it. Yeah. Yeah. That's great. That's exciting.
00:26:22
Speaker
Yeah, I mean, just like, generally speaking, as we were, you know, touching upon multiple times, also with the book, we are trying to show how data is not scary and is not necessarily big, but it's, you know, present everywhere in our lives. And, you know, you don't really need to be a statistician or a data scientist to start approaching data.
00:26:43
Speaker
Very good. Very exciting. Well, congratulations to you both. It was a great year. And so I enjoyed flipping through them and I look forward to the book coming out next year, September. Great. Yes. Well, thanks again for coming on the show and thanks to all the listeners. Thanks for listening to all the episodes this year. I appreciate all the support. Of course, if you have questions or comments, hit me up on Twitter or on the website. And I look forward to next year. I have a great slate of guests.
00:27:11
Speaker
set up to come on the show and talk about all sorts of things ranging from data visualization to text visualization to open data and big data. And we'll be talking, I'll be talking to a bunch of different authors with some very exciting books coming out in the beginning part of the year. So thanks to everyone again for listening. Have a great holiday season. And until next year, this has been the policy of this podcast.
00:27:44
Speaker
This episode of the PolicyViz podcast is brought to you by the Maryland Institute College of Art. MICA's online graduate program and information visualization trains designers and analysts to translate data into compelling visual narratives. Join expert faculty such as Andy Kirk, Marissa Peacock, and John Schwabisch to mine the data and design the story. For more information, go to mica.edu backslash M-P-S in viz.