In this episode of There Has to Be a Better Way?, co-hosts Zach Coseglia and Hui Chen interview their colleague David Yanofsky, a former award-winning data journalist who joined R&G Insights Lab as manager of data visualization. David shares how he finds stories in data and uses them to help companies achieve their goals and live up to their ideals.
Zach Coseglia: Hi, everyone, and welcome back to the Better Way podcast where we are on an adventure to find better ways to tackle various organizational challenges. I’m Zach Coseglia, the founder of R&G Insights Lab, and I’m joined, as always, by the one and only Hui Chen. Hi, Hui.
Hui Chen: Hi, everybody—it’s good to be back. And we have yet another thrilling conversation to look forward to.
Zach Coseglia: We do. And who do we have with us today, Hui?
Hui Chen: We have our new colleague, David Yanofsky.
Zach Coseglia: We’re so happy to have you here, David. We’re so happy to have you in the Lab. So, we’ve been on this journey over the course of the past few episodes, exploring various better ways. One of the better ways we explored was cultural psychology. One of the better ways that we’ve explored was the power of storytelling. David, what better way are you here to talk to us about today?
David Yanofsky: I’m here to talk to you about data—knowing your data, finding the stories in your data, and making sure that you’re understanding that the results that you’re getting can be measured.
Zach Coseglia: Amazing. I’m guessing that everyone is going to be very excited to hear what you have to say. This is such a topic that is top-of-mind for all of us, and hopefully for a lot of our listeners. But before we even get into all of that, let’s get to know you a little bit better, so, tell us a little bit more about your journey to the Lab.
David Yanofsky: I was a data journalist who was covering business from all sorts of angles, from international trade and immigration, to social networks, to environmental issues and greenhouse gas emissions. Seemingly, when you’re in this business, there’s an endless amount of data that you can use to tell stories and inform people about what’s going on in the world. So, I did that at a publication called Quartz for the last ten years, and before that I was at Bloomberg.
Hui Chen: A data journalist. I think something about the evolution of the 21st century is that you have all of these new terms popping up—so, we never had “data science,” certainly when I was going to college, and now, you’re introducing this profession of “data journalism.” Tell us what that is, and maybe a couple of the best or most interesting stories you had worked on as a data journalist.
David Yanofsky: Yes, data journalist—I guess I take that for granted because in the journalism industry, data journalism has been such a hot area for the last couple of years. One of the founding fathers of this work had a great term for this called “precision journalism,” which I think anyone can relate to, of wanting more precision in your work. And not just going off of what people are telling you or what people are saying about things, but actually being able to measure using scientific methods and using data analysis.
One of the last stories I worked on was about lead emissions from private aircraft, and which airports in the United States are the largest emitters of lead into the air around them. We did that by looking at the flight patterns of hundreds of millions of aircraft—looking at where they were flying, the altitudes that they were flying, and the types of engines that they had. And what it really comes down to is that there is still a tremendous amount of lead. Leaded fuel has been banned in cars around the world—there’s no country on Earth that allows leaded fuel in cars anymore, but we still let them in airplanes. And so, we are aerosol-ing lead into the air around airports—and around the highest volume of lead-emitting airports, the children around those airports have measurably higher amounts of lead concentration in their blood.
On the other end of the spectrum, I’ve done a story where we looked at photographs on Instagram, and we were able to quantify the skin colors of models that were being used by fashion and beauty brands to show which brands were showing a diverse group of people in their marketing and advertising, and which weren’t. And we also used that in the context of the George Floyd protests, because if you remember, there was a moment where a lot of companies and brands were posting on their Instagram pages black squares to show their solidarity with protesters, or with racial justice in America and racial justice around the world. We specifically focused on those companies and showed which ones a year later had shown a greater diversity in the talent that they’re using on their Instagram, and which had made no changes despite making this big public statement about it.
Zach Coseglia: It’s amazing work. And I love this new term that you’ve just gifted us, which is “precision journalism,” because we talk a lot here and in our work about modern compliance, about data-driven human-centered compliance, about data-driven human-centered organizational culture, about data-driven human-centered diversity, equity, and inclusion. I love precision compliance, precision culture, precision DE&I—Hui, that’s what we’re all searching for, is that more precise way of actually looking at the impact that we’re having and the work that we’re doing.
Hui Chen: In fact, I don’t know if you remember, Zach, years ago when I was still at DOJ, I rarely made social media postings then, but one of my first postings while still there was called “precision matters,” and it was addressing something that I saw in the compliance world that was a lack of precision in describing things, in describing our accomplishments and our goals. So, I got chills when David started talking about precision journalism.
Zach Coseglia: Definitely. So, David, I want to talk a little bit more about you before we dive into the work. You actually started out by studying art and graphic design, and now, your work is very much focused on data visualization. What drove that change? Tell us a little bit about that journey.
David Yanofsky: Yes, so I was an art kid in high school and that really pushed me towards being an art kid in college. I ended up studying graphic design because the program was so focused on information design—and that was a thing, that after taking all of my early classes and everything, I started really liking the ability to make sense of things while making it visual. A lot of people think about art and design as making things look strange, and making things look new and different—and the thing that got me into data visualization and data analysis was this idea that, “You can use design and you can use visualization, and all of these artistic, creative skills, and you can use it to make sense of the world, and use it to help inform people.” And I remember sitting at my desk in college and looking at (it might have been the first) interactive election maps from The New York Times, and it was made in Flash (this technology that isn’t even supported by browsers anymore), and it was really rudimentary. But I remember looking at that and being like, “If only I could do that after school—that would be amazing.”
Hui Chen: David, I find it interesting that someone who started with an art background ended up doing something that makes sense of data. I say that because so many lawyers, myself included, feel like we’re afraid of data. Was that intuitive to you, when you bridged that from when you’re trying to make sense of data as opposed to an object of art?
David Yanofsky: My best subjects in elementary school and high school were always math and sciences, and the art thing was the thing that I really enjoyed doing, and so, the melding of those two things came together, I guess, pretty naturally. The thing that I will say about all that, though, and what you mentioned, “I’m not a data person,” I think we’re all data people, and I try and associate with these people—I think that the team has a lot of these people on it, and all the people that I’ve interacted with in my career so far have it, is a desire to improve the world. We can improve the world by helping people on the street, we can help the world by exposing ills that are facing society, and we can improve the world by making companies and organizations be better, treat their workers better, and have better hiring practices so that we as a society have more equality and more fairness everywhere. And so, whether or not you’re coming at that with a skill of data, design, law, or anything else, just having the appreciation for that and having all of those skills together, I think, it’s the reason why we all ended up together.
Zach Coseglia: Let’s talk about compliance. What has been your impression thus far about the discipline that is compliance?
David Yanofsky: From the outside, and especially from the journalistic lens, you’re so used to seeing companies fail. And the reason why I would become familiar with a company and its compliance policies is because it failed—it’s because I or a colleague discovered, or another publication or whoever revealed something that the company wasn’t supposed to be doing, and didn’t have the right controls in place for. Now, to be on the other side of that, and to see these companies that are trying very hard to make sure that that doesn’t happen is a complete 180°. And it’s very interesting to see just how concerned and how seriously companies take their responsibility to not be bad, to comply, to live up to their ideals, and to make sure that the systems are in place, that the trainings are in place, and that their staff appreciate the behaviors that are necessary to continue to do good work in their industry.
Zach Coseglia: What takeaways have you had so far around the ways that companies are using data, or using visual storytelling to contribute to their compliance programs?
David Yanofsky: As a data and visualization person, the thing that stuck out to me most is how many companies just want to slap data into a dashboard, or even worse, into a PowerPoint and call it a day—that, “We track this thing—we made it a chart, and now it’s slide seven.” And there’s not so much interest in the storytelling through that data—there’s certain expectations that the people who are reading the data have the same appreciation for it that the person creating it does, which I think is usually a false assumption. There’s a willingness to just use the pre-baked visualization types, charting types, or “do what’s easy,” basically, to convey the information in a chart type that may not be ideal, or may not actually be showing what the person who made it is trying to show, or wishes it shown. And so, the biggest takeaway is that there’s huge opportunity for companies to improve just the communication around their programs through visuals, because the visuals that are showing up right now are so poor—I don’t want to call them “ill-conceived,” but they have lots of room for improvement. They just are not as communicative as they ought to be and as they could be.
Zach Coseglia: Talk to us a little bit more about your point of view of the human’s role in all of this, as distinguished from the computer’s role.
David Yanofsky: There is a contemporary understanding of computers and data, and that what you just talked about, Zach, about automation is what people think of, and they do think that, “We can automate everything away.” We did it with cars—it used to take 100 or 200 people to build a car, and now it takes 25, or whatever it is. We did that through industrialization, mechanization, computers and whatever. But all you’re really doing is moving the human intelligence down the supply chain. At the end of the day, you still need someone defining rules, and you still need someone creating policies. The computer doesn’t know on its own what is good and bad—we need people to tell computers how to figure out what is notable. And once we do that, we can automate some of these things, like, circling a data point that is a deviation from the mean, or two deviations from the mean is not a hard problem to solve, but telling a decision maker what to do about that is a hard problem to solve for a computer. But a human, a person who’s looking at this data every day, who is looking at these reports that might be automatically generated, and then digs in a little deeper and sees the issue or sees the reason why it’s not an issue, and can annotate this automatically created report with their lived experience of this thing, their prior knowledge of how the world works, is never going to go away. And so, it really is about combining what humans are good at with what computers are good at. Computers are good at doing millions of operations very quickly, and humans are good at identifying patterns, understanding processes, understanding behaviors, and making determinations. And so, it’s all about creating systems that take administrative and busywork burdens off of people so that they have more brain capacity and they have more time to do the things that they are really good at, which is think, infer, deduce, and use the computers and the automated analysis to help them do that, to flag the things that are interesting or worrisome without having to go through the tedium of doing it themselves.
Hui Chen: What you’re talking about is so spot-on. So, things like which data sets we should look at, that’s a human decision. The computer goes and collects those data sets, and it can run the kind of analysis you want it to run, but deciding how many data sets and which data sets come into the data pool, data lake, whatever it’s called, is a human decision. Also, getting the insights out of it, and even more importantly, making decisions based on those insights, those are distinctly human.
David Yanofsky: Yes. The whole field of data science is based on science, and science starts with a hypothesis. Your computer is not coming up with your hypothesis. You need people to say, “We have this data—we have a way of understanding things from it. What do we want to know?” And a computer can’t tell you that.
Hui Chen: And, “What are we going to do with it, once we’ve learned what we have learned?”
Zach Coseglia: I want to take us a little bit in the opposite direction for just a moment because of your background. We talk a lot at the Lab about thinking like a scientist. As you just point out, data science is a science, we start with a hypothesis, but there is this other component to what you do that’s thinking like an artist. So, talk to us a little bit more about that. What does it mean to think like an artist in this context? And why do you think that is also important to effective data storytelling, and precision compliance using data?
David Yanofsky: Creativity is extremely important. In terms of communicating information to people, there is no singular best way, and so, you are trying to create a rhythm and a cadence towards the display and communication of information through whatever venue that it’s happening—and you see this in all art forms. You don’t walk into a playhouse and expect to have one note of a performance going the whole time. It might be a three-act play, and the first act is bringing you up, the second act is giving you some conflict, and the third act is bringing you back down and resolving the whole thing. And that is the same when it comes to communicating any sort of information—you need to bring people in, you need to give them information to think about, and then you need to help them resolve it—and that is art everywhere it happens. When I think about the artistry of it, it’s not just about the aesthetics—it is very much about understanding the systems in which humans consume information and their emotional response to the ways in which it can be presented. There are different ways of interacting, there are different ways of designing, there are different ways of presenting that all are going to affect how effectively someone is going to be able to take what you are telling them and turn it into something that is useful to them.
Hui Chen: David, you have brought us in, you have given us a lot of information—now, help us resolve it. What are the takeaway thoughts that you would have for our audience, from having listened to this episode?
David Yanofsky: This work often comes with, “We’ve measured something and now we need to say something about it.” And I’m in this unique position, or somewhat unique position, where I get to also measure how good I was at doing that. I think everyone benefits from this—to be able to actually clearly assess the efficacy of the ways in which you’re doing things, and using that information to improve, iterate, and to continually find and optimize towards better ways of working is key to finding success.
Zach Coseglia: There was something that you just said that I just thought was so insightful, that also intersects with the conversation, David, you and I were having. So, Hui, David and I were talking the other day in the context of a client project, and we were looking at the ways in which data was being presented, not just for one client, but for many clients. And David asked, more or less, something like, “Why is it that it’s always presented in an 8.5 x 11 landscape? Why is it always presented like it’s a PowerPoint slide? Why is that we can’t scroll down, or go this way or that—why is it always the same shape?” And what you just said that I just thought was so insightful and giving me inspiration from outside of our space is, it’s like, no matter what the play is, no matter what the show is, no matter what the performance is, the stage is going to be configured the same way no matter what. That’s what we see so much in our work—it’s the conflation of the idea of analytics and visual storytelling with the word “dashboard,” and it’s the reductiveness of confining ourselves to a PowerPoint when there very well may be more effective ways to communicate our message.
David Yanofsky: What you just said made me realize this, that even a black box theater—there are hundreds of black box theaters around the country—none of them are the same. And there’s nothing to them, and they are all unique. The way in which we see these PowerPoints come in is as if every black box theater has the same size stage, the same audience configuration, the same lighting, and that we can just walk into any space and do the same thing unchanged, always.
Zach Coseglia: All right, so we’re running out of time. We could literally talk for hours more, and we will in future episodes, so David, you’ll very much be welcome back. But now, it’s time to actually get to know you even better. Everything we do, we try to make it a little human-centered, including the Better Way podcast, so you are now the human at the center, and we have some questions for you, standard questions that we ask everyone. This is our Proust questionnaire—it’s inspired by Bernard Pivot and James Lipton from Inside the Actors Studio. You can answer one of the following two questions: If you could wake up tomorrow having gained any one ability or quality, what would it be? Or, you can answer: Is there a quality about yourself that you’re currently working to improve—if so, what?
David Yanofsky: I wish I could be on time for everything.
Zach Coseglia: I just have to say, that’s a variation on a theme of what mine was, and what I think at least one or two other people’s has been. Mine was actually the ability to manipulate time, which would enable you to, in fact, be on time for everything.
Hui Chen: Next question: Who is your favorite mentor? Or, who do you wish you could be mentored by?
David Yanofsky: I’ve been extremely lucky in my career to have lots of mentors. There have been various points in my career where I’ve ended up in weird groups at companies that were about to be axed, and I needed to find my way out of them. And I’ve had lots of good guidance from people around who were just being nice—they had nothing to gain by helping me keep my job, or helping me learn a new skill.
Zach Coseglia: All right, this is the last pair. So, this question is: What is the best place you have ever worked? Or, what’s the best job, paid or unpaid, that you’ve ever had?
David Yanofsky: I’ll answer this two ways. Best job: I was at Quartz for ten years, and most of that time I was like, “Why would I go anywhere else? This is the best job I could ever have.” And it truly was. The work that I was doing, the freedom I had to do it, the respect I had for my colleagues, the respect I had from the industry, I couldn’t want anything more than that. But in terms of real job perks and everything else, I worked at Fenway Park for five years and I got to go to every game for free—any game that I wanted to, I could just walk in with my badge. And I had discounts on gear. As a high school summer job, college summer job, that was very fun.
Zach Coseglia: That’s a great answer—I love that.
Hui Chen: That’s great. Next one is: What is the best team you have ever worked with?
David Yanofsky: I keep on bringing up Quartz—I was there for ten years. It was my last job—it’s very front-of-mind. I was a founding member of that newsroom, and we started a publication from nothing. And that is an experience that will just never escape me. The 25 people that were in this little 1,500 square foot SoHo loft, putting together this website and literally changing the news business was an extremely special group of people to be a part of—not only for that work that we were doing, but because so many of the people came from organizations where they had problems with management, culture, legacy issues, and politics, and one of the founding principles that brought everyone together was, “We can do this better, and we can do this in a better way, and create policies and a culture that people actually want to be a part of, instead of a culture that people are willing to put up with until they move on to their next thing.”
Zach Coseglia: I love that. All right, so the next questions are all more rapid paced. The first is: What’s your favorite thing to do?
David Yanofsky: Since the pandemic, I’ve been baking a lot of bread, and that led me into a whole rabbit hole of trying to make a whole sandwich from scratch. So, I made a Ruben from scratch—that’s baking the bread, making the sauerkraut, making the corn beef, and I also made pastrami too. I didn’t make the cheese, but I did mix my own Thousand Island. And so, project cooking is a thing that I really like—these multi-day, lots-of-component types of things. I’m also a runner and I do enjoy running—a key part of my mental and physical health, is to get out the door and move a little quicker.
Hui Chen: You have to run off all that food you make. You, I and Megan need to do something together with cooking and compliance—we keep talking about it, but we’re all food lovers. So, next question is: What is your favorite place?
David Yanofsky: My favorite city in the world is Berlin. I’ve been back a number of times, and it is just such a vibrant city. The extremely complicated history of it is endlessly fascinating, and has yielded even a physical landscape that is, I think, unique to almost anywhere else I’ve ever been.
Zach Coseglia: What makes you proud?
David Yanofsky: Seeing people that I care about succeed.
Hui Chen: Going from the deep to the mundane, what email sign-off do you use most frequently?
David Yanofsky: “Bests,” plural, which some people have lots of issues with, but it’s mine.
Zach Coseglia: We need to do a whole podcast about that, David, or maybe we should just have a one-on-one about that. I have a lot to say about that—I really do. What trend in your field is most overrated?
David Yanofsky: I don’t know—Tableau.
Hui Chen: The way people use Tableau. Last question: What word would you use to describe your day so far?
David Yanofsky: Communicative. I had a lot of meetings and phone calls today. I’ve been with a lot of folks on the team, checking in with people, responding to emails via phone calls, and a lot of different ways of communicating today (instant messages, Zoom)—communicative.
Zach Coseglia: That is a wrap. David, thank you so much, and we look forward to having you back. And thank you all for tuning into the Better Way podcast, and exploring all of these better ways with us. For more information about this or anything else that’s happening with R&G Insights Lab, please visit our website at www.ropesgray.com/rginsightslab. You can also subscribe to this series wherever you regularly listen to podcasts, including on Apple and Spotify. And if you have thoughts about what we’ve talked about today, the work the Lab does, or just have ideas for better ways we should explore, please don’t hesitate to reach out—we’d love to hear from you. Thanks again for listening.
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