Serena Roberts is co-founder and Chief Get-shit-done Officer at Moxy Analytics. She specializes in data visualization, data literacy and community-building in service of enabling data culture. Serena holds almost two decades of experience working in data and analytics in a variety of industries, and technical and non-technical roles. Since 2016, she’s led the Twin Cities Tableau User Group as well as She Talks Data; a Minneapolis-based non-profit focused on improving gender equality in data-related fields. She’s been recognized 6x as a Tableau Ambassador for her commitment to building and contributing to the datafam.
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Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. It is episode three of season 10. I hope you’ve been joining the last couple episodes of the show, I am excited to bring you this week’s episode with Moxy Analytics co-founder Serena Roberts. We talk about all the work that they do. I found out about Moxy Analytics through my friend Kevin Flerlage. Kevin and his brother Ken were hired by Moxy several months ago, I think, kind of, maybe springtime, February-March-April, somewhere in there, and started poking around the Moxy site and found a lot of great stuff, and so reached out to Serena and her co-founder Laura to come on the show and talk about their work, talk about data culture which of course is an important aspect of data visualization because you can’t quite get to data visualization unless you have some kind of data culture and hopefully good data culture. And so, Serena and I sat down and talked about their work, talked about how they engage with clients, talked about how they try to help clients develop a better data culture. So I hope you’ll enjoy this conversation on this week’s episode of the podcast with Moxy Analytics co-founder Serena Roberts.
Jon Schwabish: Hi Serena. Welcome to the show. Good to see you, meet you, I don’t know, we’re trying to figure out whether we’ve met in person before.
Serena Roberts: I think, but I can’t place it.
JS: Well, good to see you again.
SR: You too.
JS: Thanks for coming on the show, excited to have one of the two founders, we can talk about Laura later if we want, so she can tune in.
JS: Do you want to maybe start, talk a little bit about Moxy Analytics and what you guys do and the work that you’re doing now, because when I look through the website, there’s like, I don’t know but maybe six different kind of, I mean, all interrelated but, like, six content areas, so we can just start there and then we can chat some more.
SR: Yeah. So my favorite thing to say about what we do is that we do data stuff for money. So like, we are a consulting firm in the data space, and our website, or the way that we organize our thoughts and our philosophy and things that we do is really around data culture enablement. And so, in order to successfully have this thriving data culture, you need a number of different things, right? It can’t just be, like, really awesome data visualizations; it can’t just be, you’re really good at managing your data; it can’t just be that you’ve got the best of the best tool sets; it can’t just be that you’ve got really smart people; it is how all of those things, your strategy and back to the business value, how all of those things work in concert together towards this utopia of a data-driven culture. So yeah, we do data stuff for money, Jon.
JS: It’s pretty good.
JS: I mean, if you’re going to do something for money, might as well be data stuff.
SR: Yeah, I actually feel when we were in Vegas for the Tableau conference – this year, wasn’t it? Yes. And so, I made buttons and it said, well, if you weren’t looking closely at it, it’s like we do stuff for money, then I had a little, like…
JS: Little, okay.
SR: Little data in there. So yeah, I want to say, at least one of the twins’ wives was like, yeah, really, I am going to go with that in Vegas.
JS: So let’s talk about who’s on the team, so for those who don’t know, when you reference the twins, who you’re talking about, because I think probably most folks in the Tableau community know who you’re talking about, but maybe not everybody. So what is the team like these days?
SR: Yeah, so we’re a team of six small but mighty, so it’s myself and my business partner who totally bailed on me, she was supposed to do this podcast with me; her name is Laura Madsen, and she and I go way back together. We have two other gals Anna and Ruby, and then, our most recent hires are the twins, the Flerlage twins, Kevin and Ken Flerlage. And I am going to show you, I got this little bobble head guy…
JS: You got a Ken bobble head, that is pretty good.
SR: Well, is the Ken and one’s a Kevin.
JS: I don’t know it’s Ken and it’s Kevin, I don’t know.
SR: Yeah, you guess, you could judge on that one. Yeah, so we are a small but mighty team of six right now, which is exponential for us, because when we started 2023, we’re still just Laura and I, so yeah…
JS: Yeah, that’s really fast.
SR: Ruby and then the twins, and so, it’s been a little bit of a rocket ship ride, and we’re just trying to hold on to our seats and still do the right smart things and not screw it up.
JS: Right. So I know Ken and Kevin’s background, but what’s the background of the rest of the team – and I always find it interesting how people come to, I mean, I know you’re not just doing data visualization, but how they come to sort of the data space – so what is that small but mighty team? We’re assuming it’s going to be a variety of backgrounds.
SR: It is a variety. So Anna, she’s a geophysicist turned data pro. So yes, she’s phenomenal. We have a nurse, a former nurse, like, direct patient care turned data pro. And so, Laura, she actually spent most of her career in the BI analytics space. She went to college for applied psychology at UW Stout. And so, she’s probably got the most logical path, although, for some people, you might think like applied psychology, how does that – like, it’s statistics stated, right?
SR: So to me that’s a logical path. I went to school for entrepreneurship, so until I actually started Moxy, I really wasn’t using my degree that much, and I completely fell into the world of data analytics. I was like 20 – how old was I, almost like 24, something like that, single mom, just needed a job, and I started at this very small boutique consulting firm, it’s called Lancet Data Sciences, amazing crew of people back then. And so, I started out in sales and marketing, and I was employee number 18. And when you’re employee number 18 at a company you do [inaudible 00:07:59].
SR: Pretty sure I cleaned the toilet once, but it needed to happen. Yeah, so you just kind of did whatever you needed to do, and literally before that, I didn’t even know that this was the thing, that this was a field that existed. And so, I spent a decade there, and in that decade, became somewhat of an expert. Right? Like, for a long time, I didn’t feel like I belonged in this space and that was probably just me fighting with my imposter syndrome, but yeah. So that’s my start into data analytics – from that consulting firm, I spent some time on the client side, as we in consulting like to say it. I worked in the financial sector for a little while, I worked in education; and then, finally, just kind of got sick of like grind and the bullshit, and decided to try and create what I couldn’t find out in the corporate world for myself. And then I [inaudible 00:09:05] just to be able to extend that out to other people to enjoy as well.
JS: So I want to ask some questions about the folks that you work with, but I want to come back – you mentioned this kind of imposter syndrome, and I think we all have that either from time to time or people have it, it’s like just kind of part of their identity. And at least within your experience, within this kind of data space, what helped you sort of overcome that and say, yeah, I actually do know what I’m doing and I can go that next step, which is a really courageous step to start your own business, maybe like the most courageous step, right, is like, not just to say I know what I’m doing, and I can do this job that I’m in working for someone, but, like, I’m courageous enough, or I can go out on my own and do this, like, owning my own business – so, if you were to think about your younger self with someone out there listening to this, your younger self, like, what were the keys for your success?
SR: So I think, like, I’ve always had a pretty high level of risk tolerance, like, just that is my general nature. If I had a superpower, it would be being able to pick things up quickly, right? I’m the type of person who, if I don’t know how to do something, I will go watch a YouTube video; I mean, I don’t know what people did before that, but I will go watch a YouTube video on how to, like, tile a bathroom. And then, I’ll be like, I’m an expert now. Right? And I’m probably not – I’m probably not an expert. But I have enough confidence in my ability, and also a high enough level of risk tolerance to be like, if I totally mess this up, and need to retitle my bathroom, like, worst case scenario, I’m okay with that. So I think that was just kind of like base level for all of the things that I’ve done in my life that have worked out well, it’s just like being okay with taking that level of risk. When I did make that leap to start Moxy, it was actually predicated on a conversation that I had with my husband, like, I work in a job that was like 50-60 hours a week, it wasn’t what I had signed up for, I wasn’t being the kind of mom or wife that I had wanted to be. And my husband, I love him, okay, I’ll just preface it, I do love my husband, and he loves me.
JS: We’ll just have a pause here in the podcast and just let that all stick in for everybody, okay.
SR: So he came to me and he’s like, you know, I knew that you were kind of a bitch when I married you, like, that was one of the things, that was a plus, right, needed that in my life, but this is next level, like, you need to do something about this. And so, it was like, the conversation between he and I about, well, we have health insurance through my job [inaudible 00:12:17] than you do at this point, and how can we make this happen, I feel like I couldn’t, and we didn’t have any other traces, and I was stuck there. And so, we really were just kind of like, what is the worst thing that could happen; if I just put my job tomorrow without anything lined up, without any knowing what I’m going to do, what would be the worst thing that would happen. And he’s like, well, we’d probably have to sell our house and everything we own and buy an RV and like, yeah.
JS: Live as nomads.
SR: Yeah, be nomad and vagabonds. And we’re both just kind of like, you know what, that actually doesn’t sound that terrible. And now Jon it’s actually almost like a bucket list thing…
JS: Right, to do that now, right, just like, yeah, all right, that’s good.
SR: Yeah. So we had that worst case scenario, but like, let’s do it, if that’s the worst that’s going to happen to us, then I’m all in. And plus we’ve got Laura, and not doing it alone, and that’s a huge, huge thing; she actually quit her job before, like, almost a full year before I did, and was doing her own thing by herself before we started Moxy together. So I’m not sure that I would have done it all by myself like that.
JS: Right. But to have someone you can lean on and trust and…
SR: Just another, I mean, like, so much of this stuff is like trial and error. Right? You don’t know what’s going to work, and what’s not going to work. And so, just to have that second person too, I guess, is a crazy idea, like, is it the kind of crazy idea or like a bad crazy idea.
SR: And so, I’m going to pick you up and say, hey, that didn’t work, but dust yourself off, and we’re going to try something else.
SR: I understand you got that…
JS: Yeah-no, I think it did, I mean, I think part of it is the ability or the willingness to just learn new things, which I think is definitely part of this data world, DataViz world, like, learn new tools, learn whatever tool it is, pick those up, and then, to try something, and to have the courage to go out and recognize that maybe the worst case scenario is not so terrible. And of course, you had your spouse to lean on, and other people may or may not, but they might have their parents or a sibling, or another spouse or friend to lean on. So, I mean, I think it’s everybody’s experience, just a little different. So you mentioned at the start that you work with lots of clients on their data culture, and I know on the website, it talks a lot about data literacy. What are some of the big challenges or, I guess, challenge is the way to phrase it, what are the big challenges you see when a client calls you to help them on that – I think primarily my interest would be on the data culture because it’s such an interesting part of this world, as you mentioned, it’s how people evolve and improve and develop that culture within their organization. So what does that sort of engagement look like with Moxy?
SR: Yeah, well, so we almost never get clients that come to us and say, hey, I want to hire you to help me build a data culture. Right? It’s usually something else. Sometimes it is like, oh, we need to do a literacy program, or a champion program or whatever you want to call it. But if you like, if you peel back the layers of that onion, what people are trying to do when they build a data literacy program, when they try to build these like really great dashboards, when they invest in tools like Snowflake and Alteryx and Tableau and all these things, they’re trying to create an enterprise wide culture of people who know how to access leverage, rewrite, understand, speak data, and can use that to make good business decisions and inform good business decisions, and like that to me is data culture, and it can’t be the thing that you directly focus on. Right? Like, I’m not going to come and, I’m going to build your data culture today. Right?
SR: There’s so many pieces of it, and it’s such an ambiguous thing to sort of measure that, like, it’s almost never the direct goal. Right? And when I write a contract, it’s never like, I’m going to do a data culture. It’s all these things that that are in service of that. So when it comes to the things that I see are getting in the way of people meeting that goal or succeeding these efforts, what comes to mind first is governance. Laura would be great to have here because she’s our resident queen of data governance and data management. Because, on the one hand, yes, you need and want people to be using data as this asset that you’ve invested in, but if you don’t have the guardrails and the education in place, those people are going to do some really stupid stuff with it. It’s like driving, like, think about all you have to do to go through to get your, even just your provisional license these days, or at least in Minnesota, that’s how they do it. And then, we have roads, and those roads are maintained, and there’s white lines and yellow lines, and there’s guardrails to try and make it so the people on the road are driving safely. It’s a great analogy because, well, if nobody drove on the roads, we wouldn’t need cars, and we wouldn’t need roads. We want people to be using that infrastructure because it supports a lot of the commerce that our society lives on these days, but without all those, all those guardrails, it would be chaos. So we see some organizations will invest in data literacy programs or self-service initiatives where they’re like, we just want everybody to have access to all this stuff. And then, they don’t put any kind of governance or guardrails around it.
JS: So in that case, it just kind of becomes like what, a free for all and there’s no…?
SR: Yeah, you’ll have people summing averages and not connecting the dots between the thing that they’re building and the business decisions and business value. It’s just like here’s a chart and build something cool.
SR: So the lack of the government piece is one, and then, I think, accessibility is probably two. And accessibility in the sense that everybody should be, not just regular, like, fully sighted able-bodied people like me, so that is important too. And when I say accessibility, I mean, being able to find stuff. I mean, like, how many Tableau server cloud environments you go to, and you’re just like, I have no idea how this stuff is organized, I don’t know where I am going to find this. And that’s not Tableau specific by any means. So it’s like just the how do you make this stuff accessible to people, you push it to them, when and where they need it, sort of like meeting them where they are kind of thing, or making it really intuitive and logical for them to go to some kind of a repository and find that.
So that’s one and two. I could probably call out a third being just people not following some of the basic design and best practices, like, all too often, you see Tableau can do some really amazing things, and then, they just go and build a grid. Right? Just like, they connect it to some CSV file, and then, they just recreate that CSV file.
JS: Yeah, right.
SR: It’s like, what!
JS: Here’s a table, I made another table.
SR: Yeah, not sure that was worth that investment.
JS: Right. This is a very broad question, but having identified those three pieces, plus I’m sure many more, I’ll hone in on this question a little bit – where do you start – so you come into an organization, and let’s just say they are in kind of that wild, wild west current state, where do you start to say, hey, we need to build in, for example, a data governance structure, we need to make things available for everybody, we need to get best practices, sort of, organizationally wide, like, where do you start in that? Because I think what I’m hearing from you is it’s not just a technology problem, and it’s not just a personnel problem, but it’s a, as you said, it’s a cultural management problem which is human beings, which is way more difficult, so I think the applied psychology degree probably comes in handy here. But yeah, so where do those conversations start?
SR: So I’d love to say that there’s like, oh, like, there’s this common path. There just really isn’t. Everybody is on such a different journey, on such different paths. I will say that one common thing that we see is that every organization comes in and they think that they are the ones that are behind the eight ball, that everybody else is doing all of this cool stuff better, faster, smarter than they are. And in my opinion, that is almost never the case. Everybody is much, much earlier on in their journey than you might think despite some cool AI predicting analytics things [inaudible 00:22:41] and somebody is doing over here.
SR: But number one thing, we’re going to meet people where they are. From a selling perspective, we do know that it is easier to sell, like, dashboard projects, simply because, I can say to you, I’ll build you a dashboard. You’re going to generally know, like, what I’m talking about, what you’re going to walk away with at the end of that engagement. If we say something like I’m going to build you a data governance framework…
JS: Yeah, it’s more amorphous, yeah, loose.
SR: Yeah, so that needs much more explanation. So we do see we’re getting our foot in the door, particularly because now we’ve got the Tableau experts on staff. We’re getting our foot in the door, like, let’s build you a dashboard, and then, that would lead to things like, hey, here’s some best practice things that you should be thinking about implementing; here’s some templates that you can use to help drive standardization across your enterprise, amongst developers; here’s some checklists that you should run through before you kick stuff up to production; oh, by the way, you should have production in that environment, right? You know, looking for opportunities where we can give that kind of best practice advice and guidance, and then, after a while it’s just like we have built that relationship where we’re seen as someone who can give that kind of advisory expertise. So when they are ready for, hey, we want to do this big thing, we do want to build a data governance program, like, we do want to have a data champion program, we do need help getting buy-in from our leadership, and the change management aspect of this difficult work that we’ve got a good footing in place. It does help that Laura literally wrote a book on data governance, so she’s already kind of like seen it herself as that expert.
JS: So this may not have an answer either, but have you found that it is a better strategy for you to work with the analyst level so that it’s kind of bottom up to have this cultural change, or is it better to work with the top C-level management style sort of like driven top down, or again, is it sort of like more of like the gray, it depends?
SR: I mean, yeah, as a consultant, every answer I give is, it depends. But we’ve done it both ways, we’ve done it both ways, and I gave a presentation a while back on how to kick certain data culture, and one of the – I think there was five, it was like five myths about kick starting the data culture. And one of them was that you need to wait for C-Suite sponsorship. And in my opinion that is just not true, right? If you have even mid-level, like, if you have an analytics function, whether it’s a center of excellence or just whatever, even if it’s a siloed analytics function inside your organization that has appetite for that kind of thing, there are some things that you can do from maybe not necessarily like ground level, but that sort of middle of the organization, there are definitely some things that you can do in a shorter period of time, on, you know, we know with a little budget to show what is possible. You can take that, you know, what you’ve done, and you use that to make a really strong case to your leadership of, like, look at what we did, this is what we spent, this is how long it took, and here’s the impact of that. You know, the top down approaches, where that is starting at the C-Suite, that has its challenges too, because that can always just feel like it’s another freaking thing that I’m being told to do, right? Edict from our high that now I will do this training and blah, blah, blah, blah, blah.
JS: Right, one more training I have to pretend to watch four hours of videos on.
SR: Yeah. So there is no way sort of one size fits all approach to it, and we really do try to come at it the right way in each…
JS: In each case, yeah. I wanted to ask about the third point you made earlier about following design best practices; probably folks on this show know that I have like a little side interest and project on DataViz style guides, and so, I was wondering how you work with your clients on promoting those best practices – do you give them readings, do you do a training, do you build style guides for them, like, what are your steps that you – or, I don’t know, steps is not the right word, cause that suggests it’s linear, which I’m guessing it’s not, but what are the tools and things that you do to sort of help people follow those best practices?
SR: All of the above. So we actually just published an eBook. It took me, embarrassing to say, it took me a year to complete this DataViz best practices eBook. It’s out on our website for free download, I think it’s 30 pages or something like that. And it’s just got like my top ten DataViz best practices, it’s really focused on actually going from good to great, although I have seen a lot of really terrible data visualizations out there. I think most people understand generally how to make an okay chart or good enough chart.
JS: Sure, you just insert thing, right?
SR: Yeah, especially when you’ve got like the little click ready, this…
JS: Right, the show me tab or whatever, right, yeah.
SR: So how to take it up a notch, from there, that’s what that eBook is really focused on. So we also build people templates, and whether we’re doing that in Tableau or Power BI, the tools can lend themselves, like, bone to pick with Tableau is that like creating those templates is harder than it is to do other applications. You can be an absolute pro at containers, and still want to kill yourself after messing around with that for a couple of hours. But yeah, helping people figure out, you know, so one of the things is like, oh, we’ve got our style guide for our brand as a company, and we want to infuse that into our style guide, into our dashboards, we want to use some brand colors in there, every bar chart needs to be colored by these colors. And it’s just like, okay, please don’t do that, just don’t do that. You can have splashes of those kinds of colors in there as long as you’re using them in some sort of logical and consistent format; but more often than not, what that does is it just confuses our attention, as you very well know. It’s like, I see this orange here, and this blue here, and it’s over here, and it’s over here, and it’s over here, and at the end of the day, if it means everything, it means nothing. And so, just as an example, we might say, okay, well, let’s just keep some of that color in the header, and that can be consistent across every dashboard; your bar charts and things like why don’t we just use a grayscale. Pick one of those colors and then use a gradient, a washed out version on a gradient, something like that.
JS: Yeah, it does seem that people, they try to take their branding styles and apply them to DataViz which either, like, generally, I have found the colors are too saturated, or their colors are red, white, and blue. Well, white doesn’t work in a, like, you know, draw a line in white, it just doesn’t work, and so, that frustration – so I know you have more to say on the thing, but I want to make sure, on the color point, just to drill in just a little bit, so like, yeah, it’s a huge one. So do you get a lot of pushback on that, like, no, our brand guidelines are red, white, and blue, and that’s all we can use, how do you work your way around that?
SR: Sometimes, but at the end of the day, we give the advice that we give – it may not be the advice or the recommendations that clients would want to hear, but that doesn’t, like, we’re not the people who are just going to tell you what you want. So, yeah, clients get to decide, it’s their money, it’s their dashboard, if they’re going to die on that hill, fine; but we did our job by saying here’s what you should do instead. Yeah, color is, in my opinion, one of the easiest ways that a dashboard can go completely sideways. Yeah, the brand colors are too saturated. And what gets me, Jon, is that 99% of these dashboards are internal. Everybody knows, like…
JS: Right, it’s in your company. It’s on your…
SR: It’s your company, it’s okay, like, just slow down a little bit, your brain does not need to be everywhere.
JS: That’s a really good point. But I do, I often run into people who are like, they lose sight of the graph or the point, because it’s not in the blue color that they expect it to be in, and you’re like, yeah, sometimes it’s just like a wireframing. I had this, yeah, so I asked, I’m working with a group that’s building a website for one of my projects, and I was like, they were showing me the wireframes, and I did this exact same thing that I tell people not to do, where I was like, are these the colors we’re going to use, because those aren’t the… They’re like, no, no, these are the wireframes, it’s all gray for a reason. I’m like, right, right, right. I think people kind of get stuck on some of these things that they expect, and lose sight of the bigger picture or the actual point.
SR: Yeah, well, and I think that goes back to why doing requirements gathering is so important. As a part of that requirements gathering, you should know what your use case or use cases are. And so, if you tie all of your design decisions back to the use case, like, what is the goal, what are we doing here, what’s the point, and spending time arguing about the color of a particular graph based on branded colors, and you haven’t stopped to ask the question, like, does this impact the goal of this chart, of this dashboard or report, or does it impact it positively or negatively. If it doesn’t matter, well, this, right, or move on.
JS: Yeah, right. Or not everything needs to be a freaking chart, like, it’s okay to use words, and just…
JS: So just before we finish up, what would you say to someone who listened to this interview, and he’s like, yeah, we need help? So aside from obviously reaching out to you, aside from the folks who are like, I need help building a dashboard, but, let’s say, there is someone, they’re in their organization right now, they’re listening to this and like, yeah, we are in this either, we’re in this early stage or we’re stuck, like, when they reach out to you, like, what should they say, what are the things that you look for that are sort of the key things that get you saying, like, yeah, I want to – this sounds like a project that we can do, that we can help this firm, this organization, this person?
SR: So, I mean, there is a certain amount of, like, how do we educate our clients to be better clients, like, even from the outset, I think, you’ve been getting that, right?
JS: Yeah, absolutely.
SR: Before our clients’ prospects even reach out to us, how can we make sure that they’re talking in the same language that we are, right, and listening – check the box that we’re philosophically aligned, and that’s one of the reasons why we put together all of the eBooks that we have. So we have a section on our website, it’s called free stuff.
JS: Good tab name, yeah.
SR: Free stuff there. And I think we have like seven eBooks out there right now, and they are, everything on the DataViz best practices that we talked about, but there is also how to kick start your data culture, there is also the pillars of data governance, there’s also how to prove your return on investment, like, as an analytics function. Part of the reason why we put that stuff out there is, yes, to position ourselves as experts in these subjects, but also to be really transparent about the way that we think about these things, because sometimes it can be quite different than some of the other mainstream thinking. Laura wrote a book called Disrupting Data Governance, and the first sentence is like, I hate data governance, it sucks, or something like that. So we’re really transparent about how we think about these things, in part, because we don’t want people to come to us and say, well, we think about this differently and maybe we’re not a good fit. It also helps our clients with, you know, sometimes they don’t really understand the real problem. So they might go out and read something there, and then, have an epiphany moment, or, at least, we’re speaking in some of that same language, we’re using some of the same terms. So that helps us start out on the right foot with each engagement, not that everybody goes out to our free section website before reaching out to us, but it sure is a good place to start, at least evaluate whether we’re for them or not, because like, yeah, we may not be for everybody, and that’s okay.
JS: Yeah. Well, that’s great. So the site is moxyanalytics.com, there is a whole tab, I’m looking at it right now, it literally says free stuff on it. And there’s a blog, and there’s a whole section on Ken and Kevin, and lots of other stuff for folks to check out. So I’d encourage everybody to check that out, it’s in the show notes, and links to all the other stuff that we’ve talked about. Serena, thanks so much for coming on the show, this is really interesting.
SR: Thanks for having me. It was fun.
JS: Yeah, all right, well, take care, thanks again, I appreciate it.
SR: Right, bye.
And thanks to everyone for tuning into this week’s show. I hope you enjoyed that interview. I do hope you’ll check out the Moxy Analytics site. There’s a lot of really cool free stuff, free eBooks on their site that you should check out. And, of course, if you have dashboard questions, you have Tableau questions, you have other data governance questions or data culture questions, you should reach out to them. I’ve put their contact information and some other great resources on the show notes for this episode of the podcast. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.
A number of people help bring you the PolicyViz podcast. Music is provided by the NRIs. Audio editing is provided by Ken Skaggs. Design and promotion is created with assistance from Sharon Sotsky Remirez. And each episode is transcribed by Jenny Transcription Services. If you’d like to help support the podcast, please share it and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcasts. The PolicyViz podcast is ad free and supported by listeners. If you’d like to help support the show financially, please visit our PayPal page or our Patreon page at patreon.com/policyviz.