Happy New Year! Welcome back to another episode of the PolicyViz Podcast! I kick the new year off with my special guest Chantilly Jaggernauth, a data visualization specialist and Tableau Zen Master.
Chantilly’s mission is to empower corporations and individuals through the use of data visualizations and data analytics. She is a two year Tableau Zen Master who specializes in data visualization, data analytics, design, and training.
Currently, she is the Vice President of Data Visualization and Training at Lovelytics based in Arlington, VA. Prior to joining Lovelytics, Chantilly worked for Johnson and Johnson and Comcast.
In addition to her day job, Chantilly is the founder and CEO of the non-profit organization Millennials and Data (#MAD). Through #MAD, she works to bridge the data literacy and analytical skills gap by training, mentoring, and preparing millennials to enter a data- driven global environment.
In this week’s episode of the show, we talk about Chantilly’s work, her development process, her nonprofit Millennials and Data, and her work on the new Tableau Community Equity Task Force.
New video series: One Chart at a Time
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Happy New Year, everybody, and welcome back to another episode of the PolicyViz podcast. I’m your host, Jon Schwabish. I hope you had a safe, happy and healthy holiday season, and are getting ready for what promises to be a much better 2021 than we experienced in 2020. I’m excited to bring you a whole new slate of podcast episodes, focusing on all of the stuff that I like to talk about, data visualization, presentation skills, and data communication, and I’ve got a great lineup for you over the next several weeks and months. So I hope you will join me and keep tuning in every other week for the show. Of course, January brings a very exciting milestone in my life. My new book, Better Data Visualizations is set to hit bookshelves any day now, and I hope you will check it out, grab it from your favorite bookstore, Amazon, my publisher at Columbia University or wherever you get your books. It is a two-year project in the making, and I’m very excited to get it out into the world to help people do a better job visualizing and communicating their data. But on to the podcast, so I’ve spent a bit of time in the last few episodes talking about how we could do a better job approaching racial and equity work in our data visualizations. We’ve talked about inequality in algorithms and in our search engines, and I hope you’ll go and check out all those podcast episodes from the fall. But today we’re going to turn to talking about how to effectively help people learn about data and graphics. And to help me do that, I’m very excited to have my friend Chantilly Jaggernauth on the show with me. Chantilly is a Tableau Zen Master, she is also the founder of a nonprofit group called Millennials and Data where she helps bridge the data literacy and analytical skills gap with students and also adults by training and mentoring and preparing people to work in a data driven environment. She’s also a Tableau Zen Master, so in this episode, we talk about her background in Tableau, how she got interested in it, how she took off in her skills, and also how she started the Millennials and Data project and also her work on the Tableau racial equity task force that has recently started up and I’m excited to see what they come out with. So I’m not going to belabor the introduction anymore. Again, Happy New Year, I hope you’re all well. So here is my interview, the first podcast of 2021. Here’s my chat with Chantilly.
Jon Schwabish: Hi Chantilly. How are you? Welcome to the show.
Chantilly Jaggernauth: Hello, thank you so much for having me.
JS: I mean, this is a big thrill, right? Like, I get to see you twice a year when we get together for watching you teach my Georgetown students how to actually do Tableau as opposed to having me ruin any potential Tableau skills they could ever have.
CJ: No, it’s always a pleasure having those sessions and always a pleasure speaking with you.
JS: That’s great. I’m so glad we get to chat. So you’ve got a lot going on, and some really exciting projects and endeavors, and I also want to talk about how you got into Tableau and what it’s like being a Zen master. So maybe we can start just by having you introduce yourself and talk about your background a little bit, so people can sort of get to know you a little bit.
CJ: Awesome, for sure. I’m Chantilly Jaggernauth, I wear a lot of titles, so I would say my full-time title is Vice President of Data Visualization and Training at Lovelytics. Lovelytics is a Tableau partner consulting company based out of Arlington, Virginia. In addition to that, I’m also the CEO and founder of Millennials and Data which I’ll speak more about, but we’re a nonprofit organization who works to bridge the data literacy gap between millennials and what they’re learning in school and kind of what’s taking place in the real world in terms of looking at data, then I’m also a two-time Tableau Zen Master. My journey into Tableau took place about 10 years ago actually when I was a student at Howard University, based out of Washington DC, and I had an assignment that was given by my professor, and he just said, make sense of this data. This data was just so massive. It was like a million records plus, had no clue that it was that big. I just knew that every time I tried to open it in Excel, it would crash my computer a bunch of times. And I simply just googled that night what can analyze large amounts of data and a Tableau advertisement appeared, and I saw that it was free for students, and I just downloaded it, knocked out my assignment, presented it to my class the next day. My professor was intrigued and I was intrigued as well that I was able to kind of find some insight in this data, so that’s really where my love for Tableau began.
JS: Wow. And so from there, once you had the student version, did you take classes or did you just keep playing around with it and just got, you know, just built your own skills?
CJ: So I kept playing around with the tool, so I love to self-teach myself things, I love watching YouTube videos. I’m also very active when I’m when I’m learning something. So in addition to watching videos, I’ll also be following along with my own dataset. But what was unique about my journey was that at the same time that I was a student, I was working for Johnson & Johnson at the same time. So it was easy for me to speak with my manager, my advisor at the time and ask them how do they use Tableau or have they heard of Tableau, how does Johnson & Johnson use data analytics. And I was fortunate enough that next summer to actually be placed in a role where I could start to analyze data and see how such a large organization uses data. So I would say, me teaching myself and then also having some real world hands-on experience very early on assisted me in my journey.
JS: Yeah. That’s great. So you had mentioned that when you were going through the YouTube videos and looking at the tutorials, you had your own data that you were using. So when you are talking to people, when you’re teaching your classes and you’re working with clients, you teach your day-long class or half-day class or four days of class, then they have to go learn it on their own. So what is your recommendation for people to really learn a tool, is it, go get some data and just go make as much stuff as you can with those data?
CJ: Yeah. So I think now data, there are so many sites that have data available. When I was starting out 10 years ago, we didn’t have, like, I think, data.gov was the main site that we would probably get open source data from, but now there are so many websites that provide you with open source data, data.world, you have Kaggle. You still have all of the dot gov sites. There are just so many avenues that you can fetch data from, and I feel as though when you’re learning something new, the best way to learn it is to use it in addition to doing something that you love. So for me, I like sports, right, so I would look at sports data. Or I like, you know, what other types of datasets do I like, I just like random datasets. I’m not a particular person that has a passion around it, but [inaudible 00:07:34] that’s how some people learn. Right? So if you love basketball, analyze some basketball data using Tableau. Follow the tutorials and use the data that you like. I think that’s how most people learn and catch on to things.
CJ: I think, now the community is massive, right? I’ve stepped away a little bit from the community because of just how much it’s grown. So I would say, for a newcomer, it’s a little difficult to, and a little overwhelming, not difficult, it’s a little overwhelming, right, coming into the new community. But I would say, if somebody is really looking for good feedback, what I found for some of the individuals that I work with, the best way to get the best feedback would not be from the community as a whole but more so from a mentor. Right? So I would say, for new individuals who are looking to get feedback, constructive feedback, because there are so many individuals who are online who could give you good feedback, could give you bad feedback, I think the best feedback might actually come from you finding one or two individuals who are in your circle who can provide you with constructive feedback. That’s the route that I will go personally.
JS: Yeah, that’s interesting. I mean, it also sounds like you had a job where they supported your efforts, and were interested in potentially utilizing that particular tool within the organization, which I would guess is not the case for a lot of – well, maybe less people now than 10 years ago.
CJ: So, you’re right, one of the things that enabled my journey was that I did have a job that already used the tool, but that was just one role. So at Johnson & Johnson, it’s like an organization within an organization. You can move around, you can work for one department who has never heard of Tableau, and then you can go to another department who has everything developed and designed inside of Tableau. So what I found actually was that I would seek out those roles and those positions who did not have Tableau, and I would introduce them to the tool. It would take me about two to three months to talk them into it, and then I would start migrating over those legacy reports that were done in some of those older systems [inaudible 00:10:40] just anything that was done manually, I would transition that over to Tableau. So that was always my challenge, and I felt, once I was able to get an org set up, or a department set up, then that would be my success story, and I’ll kind of move on to finding a new team that I can do that for. So that was like my [inaudible 00:10:59].
JS: Interesting. Right. Did you ever find that you got into a department and introduced the folks there to Tableau and then they would start to use it, and then people got so good at it that you would start to learn from, you know, start to learn certain things from them that you hadn’t even seen before?
CJ: I want to say that I would learn things from them, I would learn things that they asked for. So unfortunately, a lot of the roles that I was in, I was the main developer, and I was supporting a lot of leaders. And the leaders that I was working for, they were only going into the tool to look for what they wanted, not necessarily to analyze and develop. But I would say that in each of those roles, and even still today, there are requests that I get that I’m like, all right, give me a day to find that out. So before I used to say no, Tableau couldn’t do that; and then, I would say, oh look, I was able to do it. So I’ve learned to stop saying no, we can’t do that, and just say, okay, let me find out how to do it inside of Tableau now. So I would say, over the course of the 10 years, and even still today, I’m still learning just so much in terms of things that you can do, because of the requests that I get from my key stakeholders.
JS: What is, in the newest version of Tableau, what’s your favorite new piece of the software that they’ve added?
CJ: So my favorite piece is the collapsible container, and this isn’t new, new, I think it came out maybe two years ago, but it’s something that I use with pretty much every dashboard. So whenever you are transitioning a department or a user from a tabular report to some form of a visualization, and it’s multiple people who are looking at it, you’re not going to get a 100% buy-in, right? So there are still those folks who are going to want to see those tabular reports. And if the goal of the project was to visualize it in a better way, unfortunately, you’re going to get a thumbs down if you don’t have a text table on there. Some user would [inaudible 00:13:07] for you to redesign a tabular report in Tableau and make it the exact same tabular report, and that’s never the case. So the reason that I love the collapsible container so much is because now I satisfy both audiences by having the main visualization take up all of the real estate, but then I use a collapsible container for those text-happy individuals, so that they can click on that button and the text table will expand, and then they can collapse it and they’ll see the visualization.
JS: Interesting. So you’re trying to get all of your potential users?
JS: Yeah, interesting. Can you talk a little bit about the Zen Master program, and particularly what it was for you, this is your second time, I think, right, being a Zen master, like, what is – for people who don’t know what the program is and what the process is like, if you could explain that for folks, and then also like what was, personally for you, what is it to be to be a Zen master and be recognized as a leader and an expert for that tool and in the field?
CJ: Awesome. For sure. So a Tableau Zen Master is a leader in the Tableau community, who is giving back and who is teaching on their free will. So you have individuals who are very active on the Tableau forum, and they’re answering user questions. I think our forum, the Tableau forum is one of the best forums that I’ve seen out there where any question that you’ve had, somebody is always willing to answer. So you have Zen masters who spend a lot of their free time just answering those questions. You also have some Zen masters who work with the developers and work to enhance the product, all on their free time. You have some Zen masters who write blogs on new tips and tricks or new enhancements on Tableau, who are just always finding ways to give back to the community. You have some Zen masters who are teaching, in their own way, whether it’s through blog. And then, a Zen master myself, I’m teaching through my nonprofit, Millennials and Data. So I think that’s kind of where I got my leg into the program. The program runs every year. It’s on a nomination basis. So they open up nominations once a year and the community nominates you based on the things that they’ve seen. You have to fill out a couple of questions, and then internally, Tableau looks at those nominations, and, I believe, they have a committee who comes up with the final individuals who will make the program, and it lasts for a year. So I became a Zen master in 2018, I believe. And when I had my conversation with the Tableau team around why I became a Zen master, it was really because of the work that I was doing with Millennials and Data.
JS: Great. So let’s turn to Millennials and Data. So this is a nonprofit that you started. Is it housed or is at home now in DC or in Philly?
CJ: So we are virtual.
JS: Virtual, okay. So there’s no home?
CJ: Yeah, there’s no home.
JS: Home is the love that you guys all feel for one another?
CJ: Yeah, we are all virtual.
JS: Yeah, so can you talk about it, what does it do and what do you hope it can accomplish?
CJ: Yeah, for sure. So I started Millennials and Data about two years ago, the idea came three years ago, but I officially launched it two years ago with the intent to make sure that millennials and just other individuals who were around the college student age had an understanding of how they could use data coming out of college. So I would say that, throughout my professional career, what I’ve learned is that when I was turning over some of these dashboards to leadership, some of the questions that I was getting asked weren’t really technical questions about the tool, but more so data literacy questions. And there was this talk that started coming about what is data literacy, and how can we close the data literacy gap within the organization, and a lot of companies were outsourcing to figure out how can they start to train their employees internally to be more data literate; and I decided that I wanted to actually assist in this but more so with my own organization in and going to schools and targeting university students and making sure that they had some form of data literacy education before they graduated. So I started this program at Howard University with 10 students who did not have a technical background, meaning they were not majoring in engineering or data analytics, things like that. These were all business students who were majoring in accounting, marketing, finance, some of those fields, and they had zero knowledge of data, they had zero knowledge of Tableau. And the goal was that within 16 weeks I wanted to make sure that they understood exactly how they can use data within any field that they decided to go into. So I ran the program for 16 weeks, it was a success, based on the feedback that I got from that program, how would I continue to enhance and build upon the curriculum. So I think our first program we just touched on Tableau, but then our second one, we started touching on SQL, because if you want to become a data analyst, SQL might be one of the things that they’re going to ask you if you understand it. So we added SQL to the course, we added live database connections to the course as well. I’ve expanded the course even to professionals, and now I can say that we’re wrapping up with our fourth cohort. We run two cohorts every year, and we’re wrapping up with that fourth cohort right now about 20 students in the cohort, 11 or 12 of whom are students and then we have about seven who are actual professionals who were looking for this training.
JS: Wow. That’s great. Congratulations. That is incredible.
CJ: Thank you.
JS: So, I guess, kind of backing up a little bit, from your perspective, what should universities, colleges, high schools, middle schools, elementary schools, what should they add to the existing curriculum so that people have more data literacy, more graphic literacy, are better stewards at being able to be better citizens by understanding data in better ways?
CJ: I definitely think that there should be a data 101 course that encompasses visual literacy, visualization literacy, how to read and interpret your basic chart types, and even some intermediate chart types. I also think that the class should touch on, from an analyst perspective, if you’re given a task from leadership or from a key stakeholder, how do you go about executing that task, how do you use data to execute that task? So I definitely think that at least a data 101 class is necessary, how do you interpret various data types, what’s the difference between quantitative versus qualitative data, how do you really use y equals mx plus b in the real world. Right?
CJ: [inaudible 00:20:31] we learn that in school, and I just finished teaching that lesson last week to my students, and it’s like, all right, remember when you were in grade school, y equals mx plus b, we just learned this as an equation in math, but we really didn’t understand, I didn’t understand back then how you could use that in the real world, so like the linear regression model, things like that. So I definitely think that data 101 class that encompasses the teachings that the students have now, especially in math and things like that, groups all of that information together and shows them how they can be more data literate.
JS: And where would you put that data 101 class, is that a post-secondary class or is that somewhere where people are younger?
CJ: I think it can be in any age group, because I’ve done, I’ve actually done, like, I wouldn’t even call it a data literacy class, but I’ve done a data literacy type of mini training at a daycare, where we were using building blocks. We were counting, I was asking them questions in the form of, what we would say is a business question in the adult world, I was asking these little babies these questions, and then showing them how it can use their building blocks to answer those questions, and then we were constructing graphs that produced the final answer. So I would say, all right, we have all these blocks scattered together, what’s the first thing that you want to do if you want to know how many red blocks are in here. Obviously, you want to pick out all of the red blocks now. If you want to tell your mum when you go home, how many red blocks you had to play with versus the green blocks or the blue blocks and make a case for why you all need more roadblocks, you have to make a picture to show her that. So how tall should the red bar be compared to the green bar based on the number of blocks that are available – so I think it can be in any level.
JS: Yeah, I think you’re absolutely right. And I think it should be, or at least expanded for what we have. The last thing I wanted to ask you about was the Tableau community equity task force which I know you’re a member of, and you already had a few meetings. What is the task force like? What are its goals? Are you meeting every day? How is that experience so far?
CJ: For sure. So the task force is pretty new. The task force is a part of Tableau’s racial justice data initiative. We are 12 members within the community, all different backgrounds, and I think that’s what I love the most about it. But we’re bringing our own unique perspective to this group so that we can help Tableau understand how we can do better as a community, how can we be more representative as a community. We’re looking at various entry points into the community and seeing if there’s anything that we can do better, so that we’re making sure that we have equal representation or a good representation really across all of the diversity groups that are in the community.
JS: And so, how many people are on the committee?
CJ: There are 12 of us, and we meet on a monthly basis for about an hour. We typically go over because the conversations are just so great. There’s more to come. We will have some initiatives rolling out in 2021. Right now, we’re in the lead of things and trying to establish exactly the directions and the things that we’re going to put a plan and some action behind next year, because we’re just not another task force that’s saying that we’re going to do something and things just never change, because a lot of companies earlier this year, they were saying that they were going to do certain things when certain events were taking place in United States. And what I really like is that with this particular initiative, I actually see us putting the rubber to the road and making sure that we have some milestones and some things, some actionable items that we want to come out of it next year.
JS: That’s great. Well, I’m looking forward to seeing the work come out of that and see some actionable items. Chantilly, thanks so much for coming on the show. It’s always great chatting with you, and congrats on all the success you’ve had. It’s great to hear about all these initiatives and work that you’re doing.
CJ: Thank you so much for having me.
Thanks everyone for tuning in to this week’s episode of the show. I hope you enjoyed that. I hope you will check out Chantilly’s work on her website and the Millennials and Data site. And I hope you’ll join me in being excited about what comes out of the Tableau community equity task force work. Hopefully, that will come out soon. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.
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