Welcome to the 200th episode of the PolicyViz Podcast! This week’s episode wraps up Season 7 of the podcast after which I’ll be taking a break from publishing until the fall. To celebrate–and to mix things up–in this week’s episode, Renee MacLeod from Tableau interviews me and my colleague Alice Feng about our latest report, the Do No Harm Guide: Applying Equity Awareness in Data Visualization. Alice and I spent the last few months working on this project, interviewing, writing, reading, and thinking. We hope it will become the go-to guide for people trying to take a racial equity lens to their work with data and data visualization.

Renee MacLeod is a lifelong Washingtonian, marketing leader, mother of three, and proud member of the LGBTQ community. The youngest daughter of an anti-segregationist Tennessean and a Scottish immigrant, Renee has spent her life believing the validity and the importance of diversity, access, representation, and advocating for the right to love who you love. Across her work with African American elders and their families, her role on the Board of Directors at Families of Color Seattle, and her efforts leading inclusive marketing at Tableau, Renee, MSW, centers her work at the convergence of technology, data, and community as the foundation for driving advocacy, understanding, and impact.

Alice Feng is a data visualization developer based in the Washington, DC area. She is passionate about using design to make data and information more accessible to broader audiences and recently has been exploring ways to bring more diversity, equity, and inclusion into the way we visualize data. Her work has appeared in The Parametric Press and The Pudding. Previously, Alice worked as a data viz developer at the Urban Institute where she built interactive and static data visualization features and tools communicating public policy research. Alice is currently embarking on a new adventure at Natera.

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Episode Notes

Do No Harm Guide

Tableau’s Racial Equity Data Hub

Tableau Foundation landing page for the Do No Harm Guide

Urban Institute data visualization style guide

Applying Racial Equity Awareness in Data Visualization

Code Switch, The Sum of Our Parts

White Fragility by Robin DiAngelo

Related Episodes

Episode #185 with Rhea Boyd

Episode #183 with Safiya Noble

Episode #181 with Virginia Eubanks

Episode #180 with Zach Norris

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Welcome to the 200th episode of the PolicyViz podcast. That’s right, this is episode number 200 of the podcast. I’ve been running this podcast now for six years, I’ve spoken to countless practitioners, researchers, experts in the fields of data visualization, open data and presentation skills, and I’m so happy to still be bringing you this podcast every other week. I really don’t know how to put seven seasons of a podcast into perspective. I started this podcast back in 2015. It was originally just a couple of audio shorts that I posted on my blog thinking, well, I’ll just have some conversations with some folks in the field, and then turn it into a podcast. And since then, it’s really just been continued in the various interviews that I’ve done with people from all across the field of data visualization and data communication, and research, not just in the data visualization field, but also in public health, in economics, in artificial intelligence, machine learning, you count it, I have been talking about it on this show.

Early on, I was really concerned about trying to get the number of listeners and the number of downloads up on the show. I was trying to track the downloads, trying to check where people were listening to it, what platform they were listening to the show; and after a while, I just said, you know what, I’m just going to keep doing it, I enjoy doing it, I enjoy interviewing people, I enjoy talking to people about the work that they’re doing, about the insights that they can provide to both myself and to you as a listener; and so, about two years ago, I stopped looking at the traffic, I don’t even know how many people look and download the podcast at this point, I just do it because I enjoy doing it. And I enjoy the technical challenge of bringing you high quality audio and high quality interviews, I’ve enjoyed trying to get it over onto YouTube to see how I can improve that platform and getting the podcast over there, but I’ve also enjoyed just getting to know so many different data visualization experts and practitioners. And in many ways, the show just follows my interest in the data visualization field and related fields. I started this past season back in September, really focusing on racial equity, especially as it applies to data and data visualization, obviously something that I’ve been working on a lot on recently, with two recent papers, one on racism in the field of economics, and one on how we as data communicators can do a better job communicating our data through a lens of racial equity.

So before we get to this week’s episode, and it’s sort of a special episode, I want to just thank everyone who’s been listening to the show, downloading the show, reaching out to me with guests, both specific people and general ideas for topics, I want to thank all the folks who’ve helped me pull this podcast together, the sound editors, the folks who helped me with transcription, the folks who have made suggestions on different technical ideas of recording equipment and platforms, all the different support that I have required to make this show what it is, each and every other week, bring it to you for the last six years. So I’m very excited to bring you the 200th episode of the podcast. And for this 200th episode, I am going to switch things around a little bit. So instead of me interviewing someone on the show, I’ve actually invited two other folks to interview me on the podcast. So as you may know, a couple of weeks ago, my Urban Institute colleague Alice Feng and I released a report, the Do No Harm Guide, a view about racial equity in data visualization. We focus on many aspects of how we as data communicators can take a better perspective on racial equity as it applies to data and data visualization. For example, the words that we use in our graphs, the way in which we order racial and ethnic groups in our graphs.

So for this week’s episode of the show, I invited Renee MacLeod who is from Tableau to help us talk about the Do No Harm Guide, and interview Alice and I about the report. So Renee works at Tableau. She has three daughters, and she is on the board of directors at Families of Color Seattle. She has led inclusive marketing efforts at Tableau, and she centers her work on the convergence of technology, data and community for driving advocacy, understanding and impact.

Alice, my former colleague at the Urban Institute, she is now on a new adventure at Natera. She is passionate about using data and making data and information accessible to broader audiences, and she has been exploring ways to bring diversity, equity inclusion into her daily data visualization work.

So I hope you will enjoy this turnaround on the PolicyViz podcast on this week’s episode of the show, where we’re going to talk about the Do No Harm Guide, so we’ll get right to it. Here is our discussion with Renee, Alice and myself.

Renee MacLeod: Hey there, nice to meet everybody. I am Renee MacLeod, and I am a part of the marketing department over at Tableau. And my role is around kind of building out our inclusive marketing office, and what that means is, how do we make sure that folks are taking an inclusive lens against how they approach the work. So it’s not just about DEI anchored topics or content or events, it’s really about how do we make sure that end to end, we as an organization, are proactively thinking about inclusion in everything from how we think about who our customers are, how we think about our strategy, who we work with, all the way through to the content that we kind of put out there in the world. And that is one of the things that actually resonated so much with me about the Do No Harm Guide and your work, Jon and Alice, like, to be able to – the way that you’re thinking about how this lens can fold into research, and the visualization of data and how you’re working with communities, that just resonated so much with what we’re trying to accomplish in our work. So I was really excited to get to be a part of this conversation and get to work with you.

Jon Schwabish: Yeah, thanks Renee. I guess I’ll start, it was a long project. And I think we learned a lot, not just about the topic and the message of the report, but also how to do this kind of work, I mean, I don’t want to speak for Alice, but I don’t think either of us – I mean, I’m certainly a quantitative person. So doing all these interviews was definitely a new kind of experience, and then we talk a lot in the paper about quantitative people should be doing more qualitative work. And so, we’re trying to live by what we were talking about a little bit, but it was a challenge in a lot of different ways.

RM: And it feels new, right? I feel like there are elements that have been touched on from different perspectives. But this kind of cohesive guidance, I think, is really, really interesting and helpful. And I was wondering, was there a particular catalyst or point in time, where you realized that there was a need to address these topics, or there was a need to kind of take this approach?

Alice Feng: Yeah, I think it’s been on our backburner for a while, the original impetus for this work was from a planned update we wanted to do to the Urban Institute’s data visualization style guide. And as part of that update, we wanted to expand beyond just giving design specifications or chart guidance, we wanted to make it a more complete and holistic document. And so, we wanted to touch on other topics that go beyond just design of charts, but things like accessibility, and this idea of how to integrate DEI into the way we visualize data was another topic that was brought up. But we embarked on this update a long, long time ago. I mean, when Jon said, we’ve been working on this for a very long time, he was not exaggerating. Literally before the pandemic, we started drafting this update. And so, we had this idea, we were going to add DEI as a new section to the style guide. But I think our efforts were a bit slow in the beginning, just because this area was so new and undefined. We didn’t see a lot of other people who have already been tackling these sorts of topics. So I think, yeah, we kind of let it lie dormant for a while until last summer, last May, when all the racial justice protests were happening, we realized that, oh, we can’t just continue to sit on our hands here, we really have to do something, this is our opportunity to contribute to this broader dialogue, this entire country’s been having in a field that we are both very comfortable with. And so, we, I think that was kind of the catalyst for us to really start moving on this. And there were already a lot of other people thinking about this, maybe we’ll be the first ones to sort of start up, plant flag in the ground and at least come out with our own thoughts and ideas about this topic. And from there, I think it just kind of snowballed. We got a great response to the original short paper that we published last year. And then, yeah, Tableau [inaudible 00:09:32] you could probably talk more exactly how we got connected with Tableau on this. But thanks to the support from Tableau, we were given opportunity to really build on that initial short paper that we created and had a chance to really work on this really great journey. We got to talk to so many more people and it definitely broadened my own horizons in terms of thinking about DEI and DataViz.

JS: Yeah, it’s also the case that there’s so much research at Urban going on about different groups, you know, we have a justice group that’s working on criminal justice. We have a health group that was working on issues, especially during the pandemic, obviously around COVID, and how that was affecting different communities, especially communities of color. And I don’t think we were really giving, as an organization, giving a very strategic thought about how we were – certainly we’re trying to use people first language, but that’s kind of as far as we went. And I think, in the teams that were doing data visualization, specifically, I can say for myself, like, it wasn’t really something that I took a conscious thought to. So could we, not so much, set rules, but, at least, place a marker down that says, at the end, think about this, just pause, take a breath and think about this, and then, maybe the words that you’re using are correct, and maybe the order of the variables in the bar chart or table are correct. And that can be fine, but there’s probably a better way, and we need to be thinking about that. And I don’t think – I know I wasn’t really taking a hard look at that before. Alice and I really started diving into this work.

RM: I think that’s one of those things that stands out to me too what you said just a moment ago about pausing, right? And I feel like, sometimes we get caught in our brain tracks, and we keep doing the same thing, and it is now best practice, and we don’t kind of reconsider how do we kind of influence a change to influence a change, like, how do we take that moment. And I feel like, that’s one of the really kind of lovely things about this as a tool, as a resource for folks to kind of help people bridge that gap. Alice, when you were talking a moment ago, you talked about kind of how initially this started as a style guide, and then it kind of evolved into this Do No Harm Guide. Did the process of creating it, did the process you were going through evolve with that, or, what did that process look like to where you were able to bring this into fruition?

AF: Yeah, I would say, definitely, the process evolved a lot between the style guide and that initial short paper that Jon and I wrote last year. I mean, our process was very, I guess, internal, for lack of a better word. I mean, it’s really just he and I kind of thinking, brainstorming, getting down our own thoughts, but we realized too, if we really wanted to expand the scope of this work, and bring in all the other issues surrounding DEI and DataViz that go beyond just what does your chart look like. We would have to talk to other people, engage with other people working in this field, and I think that’s one of the biggest changes in our approach between initial work and then this Do No Harm Guide, was, as Jon mentioned, going out there and conducting interviews, doing more qualitative type work, really reaching out to a variety of folks, people in academia, people in journalism, people in the private sector, really trying to get a variety of viewpoints and experiences as a guide about [inaudible 00:13:09]. And yeah, just talking with them, learning from them, they connected us with more people with more resources, and definitely, I think, made for a much stronger piece of work than if it was just what Jon and I could think of, and were aware of.

RM: Right on. I’m just curious about the folks that you interviewed, as you were kind of going through, this kind of sounds almost like a snowball, like it evolved and grew – did you find that you were connecting and having conversations with people from different fields? What did that look like, was it researchers, was it community members, what did that look like?

JS: Yeah, it was interesting. So I think we had in the first, in the short paper that back to the style guide, like, that paper starts with, in redeveloping our Urban Institute style guide, I mean, it really starts with like, that’s the anchor, whereas the new piece is anchored on the issue of taking a more inclusive look to data and DataViz. So it is just like a total sea change. I mean, we had a few people that we cited in the original paper that we reached out to, and then, especially in late 2020, we were connected with other people, and then there were people just like doing more work. So I saw the director of the UCLA, I think their name is like, center of public health communication, I can’t exactly remember what, but Ninez Ponce runs that group. And I saw Ninez talk at some event, some virtual event, and I was like, wow, they are clearly doing work that is aligned with the work that Alice and I are doing. So you sort of take a shot and hope that they’ll get back to you, I sent Ninez an email and she was like, yeah, we’ll definitely talk, like, here’s the six other people on our team that would also like to talk with you. And then, we talked with them for 90 minutes, and they said, you should talk to, you know, here’s seven other people you should talk to. And I’d say, we had to, at some point, constrain ourselves, because we could have spent hours upon hours talking to people and never getting to the point of like actually trying to circle around with what are we going to say, what is the point of this document.

You have to, again, I come back to this qualitative-quantitative thing, just because, for me personally, there’s a lot of parts of this project that have been personal in a lot of ways in both professionally and personally; but on the professional side, the concept of doing this qualitative work, like, when you do quantitative work, for me, at least, you download some data, you analyze the data, you run your thing, you write your paper, and it kind of wrapped up in a little bow. And the qualitative work is there’s just all these threads that just reach out, and like just fascinated by how qualitative folks end up like, how do you cut the thread, and say, okay, I’ve reached the point where I can write this up, and I think Alice and I, at some point, just said, we need to start writing and stop talking to everybody, because the other thing is, to the latter part of your question, like, all the different sectors that we talked to, I mean, it’s so interesting to talk to a data journalist, and then talk to someone in public health, and then talk to a sociologist, and then talk to someone building technical tools, I mean, you’re learning all these different things, all these different perspectives. And that’s terrific. It’s kind of like being a graduate student again. You’re getting paid to learn. That’s amazing. But at some point, you have to rein it in and say, okay, we’re going to write, we’re going to start writing, and I think that was part of our challenge was reining ourselves in to say, okay, we need to sit down and wrap this phase of the work-up and start pulling it together.

AF: Yeah, I will say the other thing, I was really blown away by just the response we got when we reached out to people, asking if they would be willing to take the time to talk to us. I think when Jon and I started drafting up a list, our expectations were like, maybe less than half of people would say yes or even respond, and yet the vast, actually the vast majority of people were very willing to talk to us. And there were many people we had no personal connection to, we’d never really reached out to before, and they were just kind enough, generous enough to give us their time. And I think that also just speaks to, yeah, just desire, enthusiasm to talk more about this really important topic. So that was, I think, also something that made it really exciting, but also, yeah, a little bit hard to like cut off, because everyone just kept saying, yes.

RM: Yes, and also, you should talk.

JS: Yes, and also you should talk.

RM: Yeah. Well, and that’s one of the things that has been, in my experience as well, and another thing that resonates, is this, there are these learnings and perspectives that when you kind of broaden the lens in the field of who you’re speaking with, that carry into what we’re doing, like, I feel like what you have created here is I understand it’s on a specific topic, but the tenants that you speak of, and one of the things that has kind of really stood out to me was how you speak about empathy. And these are the things that I think play into different areas where folks are trying to apply a similar lens. I’ve learned so much from both of you that I can bring into my own work, even outside of the piece where we’re talking about, visualization or some of the other elements. Could you talk to me a little bit about empathy and the role that that kind of played in this work for you?

JS: Yeah, that’s a big – if you want to start Alice?

AF: [inaudible 00:18:48]

JS: I think there’s, in the database field specifically, there is this concept of how do we get our reader or our user to be empathetic with the content that we are presenting. And it’s just a constant challenge when you’re communicating data, like, how do you get people to feel GDP and unemployment, but then COVID, mortality, infant mortality, like, how do you get people to put themselves in the perspective of the people that you’re talking about or the data points. Right? You make a bar chart and you’re aggregating deaths from COVID, and it’s just this abstract shape, and how do you get people to relate to that data, how do you get them to feel that so that they want to do something about it, and they want to – and policies can be changed and society can be improved.

And so, I think that’s just an underlying challenge in the DataViz field, and I think we wanted to see if we could take that another, I don’t know if it’s a bigger step or just in a slightly different direction, when it comes to, it’s not just about that abstract shape, it’s about who are you representing, who are you talking about in the visualization that you’re producing. And I won’t say it was a common, it’s certainly a common through line in the project, in the report, and I would say also, that it was pretty common in all of our conversations that empathy or some variation on this idea of empathy would come up. And I’ll say one more thing and then let Alice fix everything I said that’s wrong, but the conversation we had with the UCLA team was really interesting. They do a lot of work in Southern California with Native Hawaiians and Asian Pacific Islanders. And one of the parts of that conversation was, they bring a lot of their products about the API community to the community groups that they’re in touch with. And one of the members of that team told us we’ve built these relationships over time, and one of the things that we have to do, especially for the Native Hawaiian community is to go to them, we can’t have them – just the way that that culture and that society and that community works, we go to them, we don’t have them, they don’t come to us.

And just that like very, I don’t want to say it’s simple because it’s not simple, but that insight was really striking to me, because it is something that you have to do from the researcher or the data producer perspective position, and it’s a physical thing you have to do. But it’s not sitting behind your computer, it’s not picking the colors for your bar chart, it’s something you physically have to do, and that to me was one of the bigger insights for me of this whole work that there’s more to everything that we’re doing that it’s more than just using better words, it’s more than just picking colors that make sense and not using icons that are racist or stereotypical, it’s more than that. And that’s, I think, how the project went from what, as Alice mentioned earlier, that we thought we just talked to a few people and we [inaudible 00:22:12] a 15-page thing to grow into this 50-page report and talking to however many, you know, a couple of dozen people.

AF: Yeah, and I think Jon you captured it perfectly. It’s just empathy, I think needs to be baked into the entire project and process, it can’t just be retrofitted onto the DataViz portion. You can’t just make your charts with people first language and the right colors. If your entire research process, if the data you’re using is flawed, biased, racist, what have you, following the principles in our guide will fix those sorts of issues, so you really need to have empathy in the way that you engage your communities, the way you approach your research, the way your understanding of your data, and also just who it is that’s doing the research, who are you, who’s your team, who’s your organization, do you also reflect and embrace that spirit of DEI. I think all these pieces are really important to have final communication products that are empathetic.

I guess the other thing, I would also add is that, yeah, as Jon mentioned, in the DataViz field, there’s definitely some debate over whether or not DataViz can even really achieve any empathy. There’s some people who I think feel strongly about charts and graphs, they just can’t, inherently they can. But I would argue, even if you have that perspective, DataViz can definitely be still used in wrong, in harmful sorts of ways. We have that example of the redlining map from the Home Owners’ Loan Corporation, and that’s definitely a very explicitly racist data visualization that has had harmful impacts, that’s been perpetuated across generations. So even if you don’t necessarily agree that DataViz can achieve empathy, if you aren’t still thoughtful about how you make your charts and how you get your data, you can still end up with horrible impacts. I think that’s [inaudible 00:24:00]

RM: I’m so glad that you are kind of speaking to this, like, as you’re speaking to this idea of they’re the end game things that you can do that will visually do A, B and C, and kind of help you do these things. But if, from the beginning, from the development of your strategy, how you’re choosing to engage, like, if you’re not bringing that lens in early, it can influence all throughout. And I feel like, that’s one of the things that has stood out for me, when I was reading through the guide is that it takes things that may initially feel abstract and provides practical guidance. And I think that that is one of the big missing pieces, and from the beginning, like, it has you think foundationally. And so, I think that’s one of the things that stands out to me, and that I am incorporating into my own work.

JS: That’s awesome. That’s what we want to hear. I mean, it is – I think we definitely struggled with some of the sections, like, we write about having a diverse workforce and diverse teams and how that impacts the analysis. And there are whole libraries and whole sets of literature on that particular topic, but we felt it was important, especially when it comes to the data part, like, it’s not just about having diverse teams, because having diverse teams is good in some sort of, you know, I don’t even know what the woke…

RM: Right.

JS: Like in the derogatory, use of the word woke. But that having those diverse teams makes the final product better.

RM: Absolutely.

JS: And thinking about these different groups makes the work ultimately better. I’ll tell one story, so this is sort of a little off topic. But one of the things that we considered early on is, like, should we write about accessibility. So should we write about people who have challenges because of vision impairments or physical impairments, and we said, again, we need to cut this off at some point, otherwise we’ll never get it done. But we were doing similar work along those lines, and we ended up talking to our blog team at Urban about we should have our researchers write all text for the images that appear in their blog posts, and we sort of had this back and forth, well, should the researcher do it, they don’t really know how to write all text, it is a skill, even though they’re content producers, well, maybe the blog team should write it because they’re the writers and they know how to write it. And Alice made this really fantastic point in this meeting that the researchers really should write the all text, because it forces them to recognize that there are these other needs, and these groups of people that maybe the researcher doesn’t necessarily think about immediately. And I think that that insight was one of those other things that sort of came out of this report, and again, sort of, goes back to empathy, I mean, at its core, that’s about empathy. But it’s about getting everybody, in this case, everybody in the organization to think about these other groups and to have empathy as opposed to saying, there’s this group of people in the communications department or the DataViz department, and that’s their job to think about that. And the researchers or the marketing folks or the PR or whoever it is, will just do their work, and these other people will think about it. And that’s where we tried, I think, kind of tried to bridge the gap a little bit.

RM: Yeah, and that seems like another example of that pause, right? And when you’re talking about, I feel like there’s this third piece, like, when we’re talking about kind of the process being more inclusive, leading to a more, like a better outcome too, I feel, as someone who gets to experience being only in different rooms, it also, I feel like, make spaces more inclusive for the people doing the work. Right? If you’re not always the one that has to raise your hand to flag the thing, because it’s attached to your identity or somehow connected or for whatever the reason is, and so, that’s the other thing that I think is really great. And because the way that you have painted this picture with this guide, I feel like, it helps kind of bring in that context there.

We were talking about the practical application a little bit, and we were talking about having a moment to pause, and so, I was wondering, can we talk a little bit about why it’s important to – it’s important to have best practices even while we’re kind of breaking old practices – can you talk a little bit more about that? I feel like your example a moment ago kind of touches on this, but if we could go down that road further, I’d be into it.

JS: You want to start? You want me to start?

AF: You can go first Jon.

JS: I mean, I think the whole guide is intended to be practical. Right? It’s intended to, and it’s not intended to be a set of rules. I think we were pretty conscious about that upfront. I mean, I will say from my perspective, it’s like, I have to say middle aged at this point, it feels very middle aged – middle aged white guy, like, perhaps, I’m not the right person to be writing about this. And so, we definitely didn’t want to set rules. We definitely didn’t want to say this is the right way or the wrong way, because, like you said earlier Renee, there’s not a lot out there on these particular topics. So how can we just – I don’t want to even say kick off the discussion, because these discussions [inaudible 00:29:54] have, but how can we sort of have this maybe cornerstone document that can really have more of the literature written around this, and maybe we can find best practices. I mean, I think, for me, one of the most tangible things that we talked about was finding a phrase to replace the word other or a word or a phrase to replace the word other in our work. Again, I download any dataset that I use in my research, there’s going to be a category for other, and there are lots of technical reasons that that happens, there’s survey reasons, there’s practical reasons why survey instruments don’t just list 500 categories in every survey. I mean, there are reasons for it, but as the communicator, are there better ways, and can we provide, in this particular guide, can we provide the reader with some practical alternatives.

And I think we, I mean, just interestingly, like, stumbling upon people trying different things, we have a list I think of seven or eight alternatives. And one of those options was like someone sent it to me in the chat window of a talk I was giving, like, hey, what about nail this idea, like, okay, yeah, that works, sure, like, whoever has a good idea, let’s put it in the hopper, and let’s see what works. And I think there’s, in DataViz, one of the things that I like about the DataViz community is that, for the most part, people are kind of like, there’s not really rules, we’re just kind of trying to figure out what are the best ways forward, and there is research, obviously, but a lot of it is on the practice, like, let’s try some things, and let’s see what works and let’s see what doesn’t work, and we’ll see what we think kind of works the best.

AF: Yeah, and I think the other thing is that I hope that our guide also offers sort of a variety of effortful, some of these changes might be, like, I think there are some steps that are pretty manageable, in terms of how much change we’re asking you to make, things like choosing a better color palette or alternatives to [inaudible 00:32:06] using people first language. Those are changes I think are within a scope that most people would feel comfortable embracing, or, some of the other changes we asked were, like, how do you go about doing organizational change, or, how do you go about doing qualitative research, if that’s not at all your background, or, how do you know address data that might be [inaudible 00:32:24] those things might feel like really big, kind of daunting topics, things to try to embrace in your own practice. So I hope guide kind of offers like a spectrum of things that are easily achievable, and things that are maybe a bit more ambitious, so people are overwhelmed, but can definitely read the guide and come away with some things, and concrete actions that they can hopefully themselves and their teams actually implement in their work.

RM: In this piece, I mean, it’s a little bit attached to what we were just talking about in this, the idea of there being, like we got to focus in on scope, like, where do we kind of hold the line, and – so stick with me, there’s a couple of ideas in my head right now – So there’s that piece, but then there’s also this idea, this piece of some of the idea of evolving understanding, like even, I’m hearing conversations about evolving from the word inclusion, because it implies being someone brought in versus belonging which is like you are already here and you are supposed to be here. And when you think about the way forward, can you talk a little bit about how you see this guide potentially evolving or other kind of conversations that you hope grow from this point?

AF: Yeah, I would say that this guide is definitely very much a living breathing document. It is very much a first iteration or first volume, it’s something we don’t – we certainly don’t claim to be experts that have, this is it, this is how you do DEI DataViz, this is just [inaudible 00:34:04]

JS: We’ve dropped all the knowledge and we can walk away, yeah.

AF: I mean, we are absolutely open to receiving feedback. We hope we’ll receive feedback, and we want to hear from other people, what other things that we missed, what other things that we [inaudible 00:34:21] other people are doing. And so, we definitely anticipate that this is a document that will continue to be hopefully updated and maintained over time. And there are definitely other topics that we know we haven’t touched on, accessibility, as I been mentioned before in this conversation, definitely is a big, another really big gap that currently exists in the field of DataViz. That’s something that absolutely needs to be addressed as well. And just, yes, society and technology continues to change and evolve. We expect our thinking will evolve, best practices will evolve, new issues will also come up that will need to be addressed. So yeah, I think that this is very much just the start of a conversation, certainly, not the end of one.

JS: Yeah. We were in the last week or two, as we’re wrapping up all the work on this, we’re in the stage of getting all the final touches on the report, doing the copy editing, getting all the blogs written, all the things that you have to do now, it used to just be you would write the report and you’d be done, now there’s this whole ecosystem around it. I’m listening to an episode of Code Switch, and they’re talking about, do we need to retire the phrase people of color. And we’ve kind of had this phrase, people of color. Now we have BIPOC, and there’s a whole part of the podcast like, how do we pronounce BIPOC. And so, I’m listening to this podcast, I’m like, oh my God, we should have included that in the report, we should be talking about – I’m like, okay, this thing is going to keep evolving, and it’s going to keep changing, and we’re going to need to kind of constantly update it and revisit it. And I think that’s the challenge, both, as data communicators, and also just society, that we need to keep revisiting. And if we can keep that empathetic approach to all of this, I think we can keep doing that, and that’s okay, and be willing to make those changes and to say, yeah, the language that we used last year is not the language that we’re going to use this year, because reasons X, Y, and Z.

RM: I think that’s what I love that you use that example, because I was actually, just this weekend, having a conversation about this with some close friends of mine, and we were talking about how we identify just a low key ladies day, like [inaudible 00:36:46] how we identify and how that evolves, and we got into this conversation, and one of the things that stood out to me is just what you called out, like, one, the evolution of language, but also, from the Code Switch perspective, the different ways that I may describe how I identify based on the context, and based on where things are out. And that’s one of the things that I think is really, that I’m so glad that we’re having these conversations, so that there can be an understanding of that. But even being aware that that is a consideration, when we’re doing this type of work, is a win. Because if you know it, you might see it, you might kind of have a moment to understand it.

JS: Yeah.

RM: Another question I would ask [inaudible 00:37:28] at a time. But one of the other elements of those evolutions and, Alice, to your point, these big rocks, when I’m approaching my work and trying to bring folks along and provide resources, and as folks grow their own lens, that’s one of the challenges, is that it’s kind of a moving piece. I was wondering if, one, if you have any thoughts on, do you see that as one of the biggest challenges, or, do you have ways that you would suggest that people think about this work to better equip the folks around them with it – are there any standouts for you?

JS: Change is the hardest part of this. I think we know that everything changes, right? That language, I mean, when it comes to data, and DataViz, the technology and the tools, all that’s changing. I think a couple of things we need to do is, one, we need to be able to have these conversations, we need to be able to talk about race, and we need to talk about ethnicity and gender and the intersections of all those different groups. And we need to be able to have those conversations openly, especially with the people that we work with. I mean, at least we start there, and we can say, what is the better path forward for us as a team or an organization, and then, it builds up to society as a whole. But if we can have those conversations openly and honestly, which is very hard to do, I mean, I don’t think, I mean, and I will say, it’s something that I reference a lot, you know, Robyn D’Angelo and her White Fragility book makes the accurate point that talking about race is not easy for white people, because, and as a white person we take for granted, we don’t have a lot of the, Renee, as you just mentioned, like, I don’t have a code switching perspective. I don’t have to think about that. And that perspective is something that we, I think, can change, obviously, again, back to the empathy, but also having people around us where we can have these conversations and especially within a group or a team, where someone can say to me, Jon, you said this thing wrong, or, you wrote down this thing that I don’t – it doesn’t have to be a personal thing. You use this term that I think would be better off if we use this other term. It’s not aggressive. It’s not personal. It’s just, you know, I think we can be approved. That I think is only makes us all better, and that, again, is I think, in a lot of ways, easier when it’s within our own teams and groups and organizations, when we are working with the people that we trust and are friendly with and interact with all the time, before you worry about saying something wrong on Twitter or Facebook, the social media machines. So I think that’s where I tend to think about starting, is that honest discussion.

AF: Yeah, I think, definitely honest discussions are critical. I think I’d hope that our guide is one of those tools that, for people who are trying to bring about change in their organization – there’s always going to be a spectrum of people who are very eager and embracing of these sorts of efforts; and then there’s always going to be people who are just incredibly reluctant no matter how many brown bags, no matter how many resources you throw at them, they just aren’t going to attend, they’re not going to read it, they just don’t want to – they don’t want to be involved or think about these issues at all. So I think for the people who are the change makers within their organizations, I hope that our resource can be one of those tools that helps them bring about that change so that they can point to, as they try to convince the less change friendly people in the group that this is something we should all be thinking about, these are the practices that we should be adopting, this is the better way for us to be doing our work. So I hope we can also help people in that sense as well.

RM: Awesome. So I think you all know, I have a favorite last question.

JS: [inaudible 00:41:31] a couple of times. Now we know our [inaudible 00:41:35].

RM: So after our chat today, and the work that’s underway, if there’s one thing you want to make sure folks walk away with that really sticks with them, what would that be?

AF: I mean, gosh, there’s so many things I hope people take away from our report, but I would just, yeah, I think I would just continue to reiterate the thoughtfulness. Right? The fact is that the world is really, really complex. Issues like racism or discrimination and prejudice, those are very, very complex topics, and I think that’s part of the reason why we shy away from trying to claim we had any rules about what to do or what not to do. In fact, it’s going to really depend on your situation, the topic you’re studying, and who you’re communicating to. So just be thoughtful when you make decisions, and don’t be afraid, I think, as well, to try things. This is a really difficult topic. It’s hard for people I think. A lot of people I think have good intentions, and don’t always know how to go about doing things in the best way, and so they become afraid of even trying, but we’re all going to mess up, and that’s okay. Just keep trying. Keep learning. Keep being open to hearing feedback from others. And ultimately, I think if we keep doing that, we hopefully would bring about a better society for all of us.

JS: So I think my answer, you’ll see why Alice and I work so well together, so Alice has the aspirational thoughts, and I am going to give you the more practical thing that I hope people do. So there are a lot of practical things I think in the guide, and, for me, I’ve already talked about the word other, and we have some suggestions for that. But the other one is, when you are making your bar chart or your table or your graph, the order of the groups, like, just think about what is that order, and that’s a practical thing that everybody can do. And if you take your racial groups and you order them by sample size, or your order them by population, those are reasonable ways to order your data. If you order them alphabetically, but maybe ordering them by the purpose of the study or the magnitude of the effect, like, there’s a lot of ways that we can present our data, and I just hope that, just as that as an example, that people will think purposely about how they’re presenting their results to their reader, their user, their audience member, whatever it is, and keeping in mind that how would you feel if you were one of those data points, and how would you feel being presented always in the last column or the last row or the last bar, even if the effect was large or whatever. So this is why Alice and I make the perfect team because we get both the macro and I do more of the micro stuff.

RM: They’re hand in hand, right?

JS: Hand in hand, that’s right.

RM: That’s awesome. Well, thank you, and thank you both for letting me come and be a part of this conversation with you. I really, really dig this work and talking with you. So just congratulations. I know this was a long road.

JS: Thanks Renee. Appreciate it.

AF: Thank you for doing this, Renee.

And thanks to everyone for tuning in to this week’s episode of the PolicyViz podcast. I hope you enjoyed it, I hope you have taken time to listen to maybe some of the 200 previous episodes of the show. So this is the last episode of the show for the summer, I’ll be taking a break, but back to you again in September with more episodes. If you would like to support the show financially, head over to my Patreon page, where you as a patron not only can receive some good PolicyViz swag, but also can ask questions to future guests on the show. So that’ll be something new that you’ll be hearing in the new season of the show, I’ll be asking some questions that are provided by my patrons. So have a great summer. Thanks again for listening to the show. This has been the PolicyViz podcast. Thanks so much for listening.

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