Mimi Ọnụọha is a Nigerian-American artist and researcher whose work highlights the social relationships and power dynamics behind data collection. Her multimedia practice uses print, code, installation and video to call attention to the ways in which those in the margins are differently abstracted, represented, and missed by sociotechnical systems.
Ọnụọha has been in residence at Eyebeam Center for Art & Technology, Studio XX, Data & Society Research Institute, Columbia University, and the Royal College of Art. Her exhibition and speaking credits include venues like La Gaitê Lyrique (France), FIBER Festival (Netherlands), Mao Jihong Arts Foundation (China), Le Centre Pompidou (France) and B4BEL4B Gallery (San Francisco). Her writing has appeared in Quartz, Nichons-nous Dans L’Internet, FiveThirtyEight, and K. Verlag. In 2014 she was selected to be in the inaugural class of Fulbright-National Geographic Digital Storytelling Fellows, and in 2017 she was nominated as a Technical.ly Brooklyn Artist of the Year.
In this week’s episode, Mimi and I talk about her work, her process, and the importance of finding these missing datasets.
My new video series: One Chart at a Time
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Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. I hope you are well and I hope you’ve seen my new project, the One Chart at a Time video series. If you haven’t seen it, please do check it out over on YouTube. Every day, for about 10 weeks or so, I’m going to have a separate short video with a guest speaker talking about a different chart type. And the idea is to help people understand there are more and sometimes better graphs and charts and diagrams to visualize your data. So we all know the line and bar and pie chart, but there’s lots of other chart types out there, and I’m grateful to the more than 40 people who contributed to this project. So if you haven’t seen it, please go on over to my YouTube page and check out the new One Chart at a Time video series.
But onto the podcast, because today I’m very excited to have Mimi Ọnụọha on the show. Mimi is a Nigerian American artist and researcher. She overlaps her data work with research and also with art. She uses multimedia in her art. She uses print, code, installation, video to call attention to the ways in which those people and communities on the margins are sometimes differently represented, abstracted and missed by our data and by our technology systems. And I found Mimi through her missing data project which collects data that seems like it should exist, but doesn’t which I think for many of us who work with data, we tend to download some data and we find that within the survey, within the data itself, there are missing observations within the data. So someone doesn’t answer a question in the survey, and that’s a missing value. But Mimi’s missing data project takes this a step further with data that you think should exist, and yet it doesn’t. And so, we talk about that project, we talk about a number of other projects, including one about the work of W. E. B. Du Bois, and we talk about her process and how she combines her data, her research and art in her work. So it’s a great conversation. I hope you’ll enjoy it. So here’s this week’s episode of the PolicyViz podcast with Mimi Ọnụọha.
Jon Schwabish: Hi Mimi, how are you? Welcome to the show.
Mimi Ọnụọha: Hi Jon. I’m doing as well as I can be, given the world.
JS: The world. Well, I am very grateful that you took some time out of your schedule to chat with me. I’m really excited to talk with you about your work and your art and some of your exciting projects. It’s not every day that I get to have like a real artist on the show. So that’s an exciting part for me.
MO: Oh you’re going to regret it immediately. We just love to talk about ourselves and our work.
JS: Well, you’re talking to an economist. So just be careful, because we do that too, but we do it in meaner ways. So we’ll just try to be, yeah, we’ll find the kinship in talking about ourselves. So you have a really interesting background, and your work is really interesting, because you have this overlap between all these different areas, and I want to get into that in a minute. But first, can you maybe introduce yourself, talk a little bit about your background, and what you do now, for folks who aren’t aware?
MO: Of course. So, as you said, my name is Mimi Ọnụọha, I’m an artist. A lot of my work looks at the implications of a world that is increasingly being made to fit the form of data. So as you already sort of alluded to, that requires engaging with a lot of different things. So there’s this very kind of technical question. There are a lot of different questions I ask that actually go beyond the purely technical, because they’re, while we’re still talking about data, of course, that situates us in thinking about, okay, what kind of emerging technology uses. But there are also all these questions like, what is the reason for the world being made into data, and who benefits from this and who loses; and what are other times when such transformations, seeing the world as one kind of thing, and then kind of shifting it to a different form that it needs to take for other reasons; what are other times when that has happened. So there are those questions that really, I think, nicely match these other questions about how this act is actually being done. So I say, I’m an artist, but in many ways, I am also a researcher too. Combine these things together and end up creating different works that have very different forms, sometimes take the form of installation, sometimes I’m much more participatory, sometimes I use text, I write a lot. But really, all of this is kind of answering that question of what does it mean for the world to fit the form of data.
JS: Right. Can you talk about maybe a couple of projects where they bring together the data and the research into one piece? I will say your project, In Absentia, which has W. Du Bois’s work is probably of particular interest to some of the listeners of this show. But I mean, just going through your portfolio, there’s an amazing amount of content here, and I’m just wondering if you could talk about a project and help people understand how you combine these various aspects of your thinking.
MO: Oh absolutely. Okay, let me see. I can just talk about In Absentia.
JS: Okay, yeah.
MO: I think that is an interesting one, because it’s sort of one that I’m still working through. So In Absentia really consists of this set of razor graph prints. Razor graph is a way of printing things that I really, really quite like, because it is very manual, but also you have to – it’s not like you push a button and you print, you have to constantly be loading paper and putting in different ink rolls. And so it feels like this very mechanical process that also is quite manual. I say this because it’s related to the work. But really, to understand In Absentia, you sort of have to know a bit of background, and the piece is concerned with this event that happened in the early 20th century. So early 1900s, and it’s when W. B. Du Bois gets this sort of, not quite commission, I’m speaking in such art terms, he has this sort of arrangement with the Bureau of Labor Statistics, where he’s going to go down to a rural part of Alabama, I believe, Lowndes County. He’s going to go down with his team of researchers, and he’s going to collect all of this data that is going to talk about what the conditions of life are for rural black citizens at that time. And part of the reason why he’s doing this is because in the census that had happened, pretty close to that time, most of these people had not been included. And as well he was doing this because he felt, he knew Lowndes County was really a part of this very – there was like a white power regime that really ruled the county at this time. Keep in mind, we’re talking just like 50 or so, 50-60 years after the end of the Civil War.
So he goes down there, and he’s thinking, okay, maybe if I can collect this data, what I can do is present this vision of black people in America that actually first is tied to the conditions that people are really facing, and hopefully by doing this really study, kind of social science work, sociological study, by doing this expansive study, I can really bring out some kind of data that will tell us a story that pushes back against a lot of the paternalistic or racist or condescending kind of attitudes that he was seeing in white dominant culture at that time. So he does this, he goes there for months, and he is working and he’s wearing his finest sociological hat. He goes with his team of a dozen researchers. They go, I think they end up talking to 21,000 homes of people, and ask all sorts of questions, they create all of these different charts. Maybe you’ve seen W. B. Du Bois’s charts, there’s a whole book on this. But they create all these charts, all these tables, it looks very modern. They do all this work. They’re there for such a long time, they finally do this. W. B. Du Bois, he sends it off to the bureau which remember, he had already been in conversation with them, he already had this working relationship where he would collect this data, and then they would publish it and kind of disseminate it. So he sends this thing off to them, and then he waits for it to be published, and he waits and he waits, and he waits, and nothing happens. And so, finally he sent them a letter. He had been communicating with them by sending letters. He sends them a letter, he’s asking, when is this going to be published. And they write back to him, and they tell him that it’s not going to be published at all. And also, that this document that he had created, is gone. They’re like, it’s destroyed, sorry, it doesn’t exist. And there’s some contention about why this is the case. Their reasoning is that they say that he didn’t, like, technically there was something wrong with the data. W. B. Du Bois is like, we were going back and forth about the data, what are you talking about; this was fine, it was fine all along, it’s that you didn’t like the results. And so, this is the claim that he makes, like, they didn’t like the result, that’s why this thing my final sociological work disappears. So I tell this whole story, and this really gets at your question where you were like, how do you combine all these things.
JS: Yeah, no, it does. It absolutely does, yeah.
MO: And so, this story, I learned about the story, and I was just absolutely fascinated by it. A lot of my work, I said, I’m interested in what it means to turn the world into data, and a lot of the way that I think about that is by looking at patterns of absence or removal, when it is that things are seemingly missing, when it is that they’re actively removed. And this was just such a wild example to me. And it is also a really powerful example, because it ends up being something that Du Bois kind of harkens back to later on in his career. And so, what I did was I created this whole exhibition – the exhibition is also called In Absentia, so it’s a bit confusing, it refers to different things. But I created this whole exhibition that was really thinking about this moment and thinking about the different absences in the moment. And so, on this one level, one of the things I created was this sort of artistic publication, which was meant to take the place of this missing report, this report that disappeared. And so there’s that, but then I also have this set of prints, which I mentioned earlier, which are sort of like, they’re in the style of Du Bois’s infographics. They’re communicating something about data, but they’re really more about the conditions of a world where you need those sorts of infographics than they are about the data itself. And it’s a bit hard to see, if you go on my website, you can kind of get an idea of what I mean by this, where there’s kind of playing with that format of infographic to communicate what made it so that this kind of removal could happen. And the thing that I really, at the end of the day, I’m just – the whole exhibition is kind of revolving, and that work is revolving a lot around is this question of, well, what is the real absence here. Maybe this absence is not just the idea of this removal of the document, but also in the bureau’s thought that if they could remove this, that it was enough to make all of the suffering that really Du Bois was writing about, that he was really reporting on, to make it just irrelevant. And then I think about Du Bois, and I think him wanting to take this suffering and this, what people were dealing with in the ways in which they were responding, him trying to catalogue this and make this fit the form of data as a way to appeal to something to some form of justice or morality, and what that even meant, because doesn’t that then suggest that those things must take that form for them to matter.
So these are all the kind of questions that I was kind of holding and dealing with. And the end result, of course, as I said, mostly are these prints, which I think are lovely, I’m biased. But [inaudible 00:11:10] these prints, there’s sort of this, they kind of, I think, as with a lot of my work, these artifacts are sort of like the tip of the iceberg to thinking about all of these kind of complicated and interconnected issues.
JS: Yeah, it’s really interesting, your work has this thread of the issue of missing data or missing observations. And I want to talk about your missing data project in a little bit, but how do you draw that line to how we use technology today, and how certain groups are either being taken advantage of or not represented in different data – I recently had Safiya Noble on the show, talking about the oppression of algorithms, I’m just curious how you think about maybe broadly about modern forms of data and how data, yeah, again, is not capturing people, is maybe, for lack of a better word, punishing different communities, different groups – and how then do you think about weaving that all together in the artwork that you do and the writing that you do? I know it’s sort of a broad question, but do you have this, like, interesting thread that runs through a lot of your work?
MO: I think it’s that, it kind of comes to this point that you made a bit earlier on, which is that all of these things are so connected in my head. It’s so clear, you know, to think about data, and the things that it kind of capture, to think really about what is the need, why does it need to be able to capture this, I think that Du Bois’ story, it’s so fascinating, because it took place in the 20th century, but it is extremely relevant to right now. I think that that sense that he had, which is justified and which I’m grateful for, is still one, you know, it’s the same kind of issue that people are grappling with today. And I think that in many ways, the reason why I like to start from this place of absence removal, the missing, is because the story is not of one thing, it’s of an entire system. And the thing about looking at what isn’t there is that it, by default, forces you to think about, okay, what is and what led to that, why are things that way. And I think that this is part of why I like taking this perspective of being an artist, even though a lot of the work that I do really could be called research or sometimes is a bit more ethnographic or takes journalistic, like, it takes different forms, but I find a lot of space in saying, no, actually, I’m looking at this from an artistic point of view, because I think that a lot of what the hardest creative work, the hardest thing to push back against is the narrative of what these things can do versus what they are doing, and seeing the distance in between that.
JS: Right. So then let’s turn to your library of missing datasets project, because this was what first caught my eye. I read about you somewhere, and was like, whoa, that’s incredible project. So can you talk about that project a little bit, and then maybe we’ll try to not to be pun so much, but we’ll try to fill in the gaps on the various questions I have about that broadly.
MO: Oh, that pun was intentional, don’t pretend.
JS: All right, you caught me, I am [inaudible 00:14:11]
MO: You were like smiling as you said it.
JS: I’ve been working on it for days.
MO: Sure. So the project you’re talking about, it’s kind of this umbrella project around missing data, and it started from this sort of observation I had many years ago, maybe in 2015, something like that. I don’t know, if I’ve said it. I also work as a programmer, and have done a lot. I do a lot of things around data, so I deal with data a lot of different ways, and I had the sort of observation or just realization that for all of these spaces where there would be loads and loads of data being collected, there would be these kind of curious blank spots where nothing would exist, where all of a sudden nothing was collected. And that kind of juxtaposition was so interesting and so jarring to me, that I started just thinking a lot more about it, and creating work and writing about it. And I say creating work and writing because those are sort of the ways that I like to think through something. And so, what I first started doing was just trying to collect this list of all of these datasets that I could not find, for various reasons, sometimes not because it didn’t exist, but because I couldn’t get access to it, or maybe because in many cases, it just really didn’t exist. But I started collecting all these datasets, but then I kind of ended up taking a step back. As I said, a lot of the work I do is not just about the thing, it’s about, okay, how does this thing make sense within a larger system. So instead of just thinking about things that weren’t being collected, I started thinking, what are the reasons why things aren’t collected. And so, I came up, to begin with, with this list of about four reasons, and I’ll kind of go, if it’s okay, I’ll go through some of them.
MO: And then [inaudible 00:15:47] example. And so, the first one was this reason that often the groups who have an incentive to collect something won’t have the resources to, and vice versa, the groups that have the resources, won’t have the incentive. And this one, this was really the first thing that got me thinking about this, which was, this is 2015 – I was thinking about civilians killed by the police. And that was a moment where there was a lot of conversation happening around that; and it, at the time, that was a missing dataset; and now it no longer is; now, actually, people have collected it, or lots of different organizations and groups, working sometimes in concert, have collected it. But at the time, it was, you know, I was like, wow, we have so much data around justice, around policing, around crime, nothing about civilians, citizens, just people, just civilians, I’d say who are killed by the police. And so I was thinking about why that was, and again, this sort of difference between resources and incentive emerged as really a primary reason. So that’s one reason. There’s a lot more to say on that, but we don’t have all day.
The second reason was that sometimes the burden of collection isn’t perceived to be worth the return of actually having the data. And this came up for me, I was really looking a lot at data around sexual harassment and sexual assault. I was having so many different conversations with various groups and people talking about how so many people are disincentivized to come forward and talk about this, because of what they’ll be put through in having to talk about this. And that actually, they’re like, you know what, it’s not worth it, I’d rather just not have this data exist in the world than have to come forward about this. Then there’s this third reason, which is, I think, one that I quite like, which is just that some things resist metrification, that this, again, this comes back to this idea that not everything can be quantified. And one of my favorite examples that seems like it wouldn’t fit this is actually cash. When you compare cash to credit card transactions, so wonderful for a world where you need data, everything is tracked, it’s fantastic. But there’s this kind of anonymity to cash. There’s like shadowy nature to it. I had all these conversations with statisticians and we’re talking about this problem of needing to figure out how much US cash is outside of the US, and where it is, and virtually impossible to track. You can model it but difficult to track.
MO: And then finally, the final reason was that there is sometimes an advantage to some form of data not existing. And in some way, that really speaks to all of these reasons. If something doesn’t exist, there’s an advantage for some group in it not existing often. But specifically, what I mean is for the group that is situationally disadvantaged, the group that doesn’t have as much power in a situation, sometimes intentionally will say, actually, no, we don’t want this to exist, because we know how it could be used against us. And the example that I used, I think at the time, was around sanctuary cities. In the pre – this was, remember, 2015, there’s this question of, when all these different cities were coming up with municipal ID cards, there were some who would actually, you know, I should rewind the point of a lot of municipal ID cards just to make sure that undocumented people can have some form of identification, while also providing lots and lots of benefits to people who do have documentation as well. And there were some cities who would say, okay, to collect the information we need for this, we are not going to save it, because we’re worried that whoever – this is again 2015 – whoever comes into power, might try to get that information and use it in a way that would be disadvantageous to undocumented people, which was true. And so, that kind of, that as well.
So I started focusing on those reasons, and taking a step back looking at those patterns, and then this sort of snowballed into a much larger project which has involved working with different groups who are missing some kind of data and thinking with them about whether that should be filled, whether it shouldn’t, what the ramifications of that are. And then, the sort of art project, which are these libraries of missing datasets, which are these different cabinets that are always themed around either a location or a theme or a topic, and in them will be these filing folders, and each of them is titled with the name of a missing dataset, but all of them are empty. And it’s, I think, a really – I found it to be just a very grounded way of thinking about these topics, which I think is what I’m always searching for, something that feels like evocative and make sense, because so many things when we talk about data feel abstract and in the [inaudible 00:20:05]. And actually, these are very real issues for so many of us.
MO: Yes. So anyway, that’s my long, long overview…
JS: No, it’s fascinating. It’s a great story. It’s a great project. I mean, I’m always struck by just as another example, like the United States’ inability to count the number of people in prisons and jails, it strikes me as fascinating that we can’t get a handle on that. I mean, especially in prisons, where people are there for some longer period of time, it would seem that you could just literally walk down and count people. But we don’t have good counts of people in prisons and jails, which just kind of blows my mind in some ways.
MO: I think, looking at this, it really does. I [inaudible 00:20:50] how there’s, again, the narratives versus what things are like on the ground. This idea of just like a kind of easy, you know, like an easy way of just collecting the data, you look at it, and you’re like, oh, this is so fragmented. Different states have different ways of doing this. There’s this kind of tension between the federal, state, and municipal level, like, this is a jagged project, it’s very difficult, it’s not smooth, it’s not seamless at all.
JS: Yeah. You mentioned that you work with some organizations to maybe help them identify missing datasets and whether they should go out and collect those datasets. Can you talk a little bit more about what that process is like – a group reach reaches out to you or you’re connected with a group, and then do you have a conversation about, yes, this dataset would be great, it doesn’t yet exist, can we go get it and should we go get it?
MO: So that is a good, really interesting question, I think, again, very biased, I think all of it, all of it is interesting. So this is something I don’t do so much anymore, I pretty actively try not to. But especially in those early days of working on this project, where I really was, as I said, thinking through it, and talking with many people, it was the thing that kind of emerged without my seeking it out, which was just that some – it’s happened really in two cases. There’s one, in fact, I’ll kind of go over both of them, because they’re interesting and very different. There’s one case, which is super public, I ended up meeting with these Broadway performers, who were very cool and had been doing this huge kind of data collection project on their own. I don’t know if they were classifying it as that at the time, but it is absolutely what they were doing. And what it was about was how there’s no data for the racial demographics of Broadway performers. And there is a lot of data about the demographics of audience members, because this is what makes the theatres money, knowing the demographics of the [inaudible 00:22:40].
JS: Yeah, right.
MO: But, in fact, there was none of this data about the performers themselves, and this was a group of Asian American performers who were really feeling like they were not getting cast at the same rates of some of their other counterparts. And so they were like, actually, why don’t we just collect some data on this, why don’t we see, because every time we try to bring this up and say that we don’t get cast, people say, oh, what are you talking about, who knows, what about this show and this…
JS: Yeah, right.
MO: And so, they thought, yeah, okay, well, let’s get some numbers, let’s do this. And so, they had already been doing this for a long time. They were doing it for years, and then I just kind of got connected to them. And at the time, they had been talking a lot about this missing dataset idea, and it just was a framework that really worked so perfectly with what they were doing. And so, I joined on at the very end and helped them do a little bit of the analysis that they were trying to and do some visualization. And then I also wrote an article about it for courts, so that it could kind of get a bit wider. And it was a really just very lovely, very neat project, in the sense that, as I said, they were the ones collecting it, they managed to collect very completely this kind of data because they were in the space themselves. So the people they were collecting it on, they knew those people, they can actually reach out, it’s very rare.
JS: Yeah, you actually talk to them, right.
MO: Yeah. So it was like a closed system, which you so rarely get, where the people themselves make up the group, and can do a lot with that. And we were able to demonstrate that they were absolutely correct. There are all these charts that we showed about it, and one of them that was so great – not great, it’s terrible, but very useful for the point that they were trying to make is how it was like only one new Broadway show had really had any Asian American performers in it at all. And it was a show that took place in modern day Thailand, and had, like, the main characters were white. And so I just, like, the point they were making absolutely came through. And so that was one of those rare examples where you’re like, oh, this absolutely makes so much sense, we can point this out, and it allows us to now – it allowed them to be able to move forward and they still, they continue to do this. It’s called APAC is the organization that does it. They are amazing. They’re still doing it. They constantly have met with all these different theatre companies and really done a lot with that. So that’s like one example.
JS: That’s really interesting.
MO: Yeah, that’s very neat, very, almost inspirational.
JS: Yeah, absolutely.
MO: On the other hand, I had a lot of conversations with a group of people who were working in the restaurant industry and were undocumented workers, and were talking about being the sort of open secret, as they put it, of underpaid people, undocumented workers, working in the restaurant industry. And they were thinking about, like, this is a missing dataset, people don’t know how many of us there are and how little we make compared to other people in the same industry. And we sat and talked about this, and at the end of the day, we, as a group, or, for that capacity of the project, we didn’t do anything, because what they really landed on was that to do something in the short term, would basically make things much harder for them. Sorry, something that would improve – it would improve the situation in the long term, but in the short term, it was going to make things harder for them, because it would mean kind of going public, putting a name to who they were and talking about being [inaudible 00:25:51]. And they were like, it’s just not worth it. And so, this is a point where it wasn’t – it’s missing, that doesn’t mean it should be filled. It doesn’t mean it shouldn’t be filled, but it doesn’t mean that it has to be done in this particular way. There might be a different way, and maybe this is something where it’s like, okay, this has to be dealt with in a different kind of organizing capacity maybe. And so, that was a little, it’s good, it’s really instructional, because the point [inaudible 00:26:16] the point of this project is not necessarily to fill everything, it is to think about all of this wider system and all these different groups who are impacted by it, and in different ways. And really to find a way to hold all of that and find a way to think and talk about it, so that you can actually decide what you should do.
JS: Right. I’m curious from the perspective of having a dataset. So this is sort of going in maybe in the weeds a little bit. But assuming I have a dataset, and then there’s missing data inside that dataset, which is probably certainly a lot of people’s challenges, right, I have some survey from wherever and I download it, and they start analyzing, and there’s missing stuff all over the place. When you are working with some of the groups that you’ve worked with, the APAC group, I think is a good example, like, you’ve created a bunch of visualizations, you did the analysis of it, do you have techniques or strategies that you follow when you’re working, you have a dataset, but there’s missing components within that dataset?
MO: Yeah. This is one of those things that I really think is so context specific. It’s so, because this, and it – my overarching principle is that data collection is actually about a relationship, and it’s this, at its simplest, there are two kinds of nodes in this relationship. There’s the group – and I’m talking really more about civic data in a way, but there’s the group that wants to collect something and decides they’re going to collect something; and then there’s the group that makes up the collected. And in the case of that APAC example, the reason why it was so neat, is that those were the same group. So the people who were doing it were the people who were impacted by it.
MO: And so that, when you have that, it makes it so that these other issues, like, these issues of like, okay, what is this for, what is this going to do, like, those become easier to resolve and to see. They’re easier to think about. Often, that is not the case. And so, a lot of my work has been thinking about that moment of data collection, because for so many people who work in tech, you just, as you said, you kind of get a dataset, you’re not actually involved in that process of collecting data. You don’t have to think about that. And so, I like to think about this relationship because it does allow you have the chance to take a step back and think, alright, well, why are we doing this, what are the aims, why was this created, who is it for, how’s it going to benefit these people, how’s it going to harm these people, what’s my positionality in this entire thing. And I think that’s useful, because, of course, even, as we talk about, you have this, like, you have a dataset, something is missing. If you’re a statistician and you’re looking at a dataset, and there’s missing values, your answer to that is very different than if you are a parent, and you’re looking at a dataset and your child doesn’t show up in it for some kind of, you know, some score [inaudible 00:29:08].
MO: It’s very different. Your positionality actually does affect so much of what it is that you do and you’re trying to do. And so I think that is really the first step, that kind of understanding. And then, of course, it depends what is the aim, what are you working on. So I think it’s very, I don’t have any huge, like, okay, this is what you do, and this is why, even in that case, I was like, oh, this is not really – I’m not the most equipped, I could help in these little situations, I could participate, but I’m not the most equipped, I’m not like just one person [inaudible 00:29:37] consulting. But I do think having a clear idea of that kind of relationship helps you think about all of the different points of contention, things that could come up around it.
JS: Yeah. The overlap of those two groups is fascinating, something I hadn’t really considered because it is generally the case that you have a group like the Census Bureau, for example, they sort of set the, more or less, kind of set the techniques and the expectations about what data is and how it should be collected and the terms that they use, and then others sort of follow those guidelines or strategies. But when you have the group that is the group being studied, which I know is not quite the right term, the group that’s being interviewed, also collecting the data, that is a unique experience, I think, or a unique way to collect and build a data setup from the ground.
MO: What I was going to say is that, I think that, yeah, understanding that kind of relationship, it just helps you then understand, thinking about the census, there’s so much controversy for so many different groups thinking about the census. And there needed to be this whole campaign to get people to actually complete the census. And I think that when you look at that, and you’re like, well, okay, who’s the group that’s doing the collecting and who makes up the collected – it absolutely explains all of the issues around this. It explains why, like, exactly why people, some people were reluctant or why some people were not. And why it was this huge, this huge kind of undertaking to try to see who should be included in the census that that even was a question. It’s because you can look at this history of what it means, and this group, the state, tries to collect on these people and how different groups have been affected by that historically. It just really to me opens up these doors then understanding these data collection processes, and then the datasets that result from them, which will then go on to be used in all sorts of different settings. It just helps, it kind of to me pins it down and makes it so that you can see, oh okay, I understand – again, it’s always this question of a larger systematic thing than [inaudible 00:31:39] it is about that one moment.
JS: Yeah, that one moment, yeah. Where is the missing data project now? Have you sort of exhausted your hands-on work with it or is it – I mean, there’s a GitHub page for it, so is it still just sort of chugging along, where does it stand now?
MO: I often describe that project as, like, a TV in the background, where you turn the volume up sometimes and turn it down. And I have definitely been working on a lot of other projects that are connected to some of those ideas, but not exactly that. I think the next step in that is that there is some stuff I really like to do, kind of playing with this, the idea of different types of missing data and kind of really diving a little bit deeper into thinking about, as I said, trying to find ways to connect between, okay, like, how is this different in different settings, depending on what you’re talking about. And so, I think that there could be some things coming up in the future with that project. But for a while, I would say, the latest iteration of it probably happened in 2019 or so. And something I do a lot in my practices, I will remake our project, but in a different way, and create a new version of it. And I think part of this is, is really thinking about, kind of, stealing that the software versioning thing, releasing [inaudible 00:32:58] and kind of applying that to the art that I make where I’m like, oh I want to explore different aspects of it. The original artifact, that cabinet, that work was, I think, from 2016. In 2019, I made an update to it that was thinking specifically around blackness and data, and I was thinking about this, again, that relationship, and what happens when you are a group where loads of data has been collected about you, but you don’t control it, and you don’t have access, you know, you don’t really set the terms for that. And so, I made one version of it that really was speaking to that datasets that were really connected to that. That was in 2019. I think the next thing to come will probably be a series of more of them that are kind of experienced together at the same time, but we’ll see. Honestly, it’s just hard to make any plans for the future.
JS: Yeah, I hear you, yeah, absolutely. Mimi, this has been great. I’m just fascinated by your work. I just am going to RSS feed on your website. Wait for the next thing to come out. Thanks so much for coming on the show. It’s been fascinating. It’s been great having you on the show. I appreciate it.
MO: Thank you so much. Thanks for letting me talk at length. Love it.
Thanks everyone for tuning into this week’s episode of the show. I hope you enjoyed that. I do hope you will check out Mimi’s work and see if you can contribute or think about or somehow work with the missing data project. There is an opportunity there I think for more of us to consider the missing data in our world.
So until next time, this has been the PolicyViz Podcast. Thanks so much for listening.
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