Microsoft Research Podcast - 084 - Beautiful data with Dr. Nathalie Riche

Episode Date: August 7, 2019

Dr. Nathalie Riche envisions a future in which all of our data will be accessible, meaningful, compelling and artistic. And as a researcher in human computer interaction and information visualization at Microsoft Research, she’s working on technical tools that will help us wrangle our data, extract knowledge from it, and communicate with it in a memorable, persuasive and aesthetically pleasing way. In other words, she wants our data to be both smart… and beautiful! Today, Dr. Riche shares her passion for the art of data driven storytelling, reveals the two superpowers of data visualization, gives us an inside look at some innovative projects designed to help us th(ink) with digital ink, and tells the story of how a young woman with an artist’s heart headed into computer science, took a detour to the beach, paid for it with research and ended up with a rewarding career that brings both art and computing together. https://www.microsoft.com/research  

Data Inc. made me realize how expressive the pen can be. And when I was thinking about this project, I was like, I don't have to think when I use the pen. It's just like, you know, I'm just expressing myself and I don't have to think about how I'm going to do it. It's just so natural for me. I just put my pen down and then that's the way it goes. And so how cool would it be if, you know,

I could use this for just expressing my thinking all the time? You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting edge of technology research and the scientists behind it. I'm your host, Gretchen Huizinga. Dr. Natalie Reich envisions a future in which all of our data will be accessible, meaningful, compelling and artistic. And as a researcher in human-computer interaction and information visualization at Microsoft Research, she's working on technical tools that will help us wrangle our data, extract knowledge from it and communicate with it in a memorable, persuasive and aesthetically pleasing way.

In other words, she wants our data to be both smart and beautiful. Today, Dr. E shares her passion for the art of data-driven storytelling, reveals the two superpowers of data visualization, gives us an inside look at some innovative projects designed to help us think with digital ink, and tells the story of how a young woman with an artist's heart headed into computer science, took a detour to the beach, paid for it with research, and ended up with a rewarding career that brings both art and computing together. That and much more on this episode of the Microsoft Research Podcast.

Nathalie Rich, welcome to the podcast. Bonjour. Bonjour. I like to start my podcasts by situating my guests and their research. So let's situate you. You conduct research in the field of data visualization. In broad strokes, tell us what problems are you trying to solve? What questions are you asking? What gets you up in the morning? I guess what gets me up in the morning is to be able to create visuals from data that actually can be useful to a lot of people. And, you know, everyone has data, whether it's like personal or at work.

So that's sort of this unique opportunity to try to leverage that and then help people use that data, right, to gain knowledge and insights. And what really gets me up in the morning is this creativity part. Because I have this data that's really like, you know, ugly in some ways, and I have to make it sort of inspiring and rich of insight and synthesize it. And so I have to craft sort of art from that data that could be also hard that is, useful and like touching people. It's really akin to art, right? It's like, how do you extract the message and how do you best communicate it? And people are just very receptive to things that are beautiful, right? And that touch you emotionally or just simply like, you know, stop Cognition, the group. It's a thing here. I've had a few of your colleagues on the podcast already,

and your work falls under the larger umbrella of Human-Computer Interaction, or HCI. But you follow specific lines of research within that on smaller groups or teams, and EPIC is one of those. So why this particular group? What does your team bring to the HCI table? We do EP epic research. Of course you do. Well, so first, I really love our group.

Our group is very diverse. We don't really like focus on one specific device. But what we care about is the experience. So from end to end experience, for example, I care about inking. So what do people do with a digital pen in their actual day-to-day job or life? And so we do care about the breadth of the activity and not this one particular domain or type of audience or task. What I really care about these days is how do you help people think with this digital ink? Because I think with ink all the time, right?

With pen and paper on my whiteboard and so on. So how can I really support this broad activity, right? Like what do people do when they're thinking? And how can we help them think with their device that has this pen? Right. You know, drilling in just a little bit there, I write a lot for my job and I think on a whiteboard and I have notebooks all over the house. In fact, have filled notebooks. But I feel like there's a connection there that I don't have right now with any of my digital devices.

Well, in fact, that's what we try to bring to the table in lots of our projects, like the recent project we're doing. You know, somehow we don't have that connection with digital ink, but yet digital ink can do crazy things for you because it knows like everything is in the computer. So could imagine if you start writing down some notes and then it bring you back three of those ideas you had in those half notebooks all over the place. That's like this unique superpower. Sure. So that's the sort of things we want to try to leverage, bring that connection, but then also the power of the computer. Speaking of superpowers, you have said famously, to some degree, that data visualization has two superpowers.

Okay, what are they? And why are they super? Obviously, I have young kids and they love superheroes. So I just give superpowers to everything in my life. So, yeah, data visualization has two superpowers. The first one, it's about identifying insights. And the way visualization does this is by basically answering questions you didn't even know you had about your data. So you just look at it

and it's a bit like looking at the clouds in the sky and then you see patterns. And it's not really that you were thinking about those patterns, right? It's very different from like running an analysis test, but it's more about something you're noticing and that basically generates questions in your mind. And then that's like, you know, this making you see through some of that data. So that was about the exploration, right? And like sort of

learning from your data and identifying things. The second one is about communication. So a picture is worth a thousand words, right? And when you see the picture, a lot of that superpower that the visual has is just directly going through your sensory system. That's what we call pre-attentive. You're not even aware that it's happening in your brain. And so you're perceiving that message extremely fast. And so that's like this superpower of communication that is, you know, much faster than verbal or like textual. And so that's the superpower of communication. As a researcher, you've made it clear that you're not a data scientist. Rather, you work on building tools that data scientists will be using.

And we can extract from that to some degree that that means you have to predict the future. So tell us about your thought process as you seek to equip the next generation data scientists with tools for jobs that maybe haven't even been invented yet. I don't know, no pressure. So first, you know, I do think about data scientists like as we know them today, which helps. And I see a lot of them struggle. But I do think about, you know, the future in which every one of us is going to be a data scientist because, you know, we're going to have even more access to data and we have to make decisions from it. So the tool I'm sort of thinking are more and more designed towards helping anyone deal with their data and leveraging those two superpowers. So sort of the way I predict the future is relying a lot of my own experiences,

what I can learn, what I can do. I'm sort of the super data scientist, right? Because I'm the user of my own tool that I haven't done yet. So sort of trying to rely on my own experience and then other people's experience. And so I spend a lot of time observing people and watching them work and then trying to sort of extract what are the key activities that they're doing

that we really don't support right now and then trying to build tools to help support those. Let's talk about the data scientist for a second, because that's your kind of target audience, even though it has a broader application. Yes. What do they say to you when you talk to them sort of upstream in your research about the kinds of problems they're facing and what they wish they had and what they wish they could do. Right.

So a lot of the problem led to a lot of the research that I've done in the past five years, which is a lot of data scientists, they're very good at what they're doing. So they're very good at basically getting into the data and then kind of massaging it and then extract those insights from it. But one of their key struggles is to actually transform that insight

into some communication that they need to do to their upper management or, you know, to other people that aren't really that familiar with the data or with the method. Or technically sophisticated. Exactly. And, you know, you need to have this good sense of design. And a lot of data scientists, although some are really amazing, still they had some issue like, you know, crafting aesthetically pleasing charts

or even memorable ones. And so a lot of this, that's like sort of the activity we call storytelling. Yeah. And so a lot of the struggle was about, you know, once you have the data and then you have the insights, how do I sequence it and message it? And so that I have actually a story with visuals out of all of that masses of data and charts that I've created. Well, since you mentioned storytelling, I want toade upstream before we get specific and talk about this big idea of data-driven storytelling. You have a lot of research projects going on in this space,

but before we get granular, give us a sort of raison d'etre, if you will, for data-driven storytelling. So storytelling, you know, it's been there forever, right? I mean, that's the way humans communicated before they know how to write. Right. And in fact, a lot of them drew. And so we sort of kept this going on, right? And that's one of the best ways to convince people and try to convince them to make decisions. And so storytelling is really this notion that you can take this person with you along the path of your reasoning so that that person can see your argument and understand that and hopefully, you know, agree with you all the way until you can make your point.

And so what's really the point of the storytelling is to demonstrate your point you're making. They're substantiated by data. It's not just like, you know, out of my own brain, but it's like data that I've collected. So that is somewhat more objective than my own opinion. And so there's an art to it, right? You need to have the story, but you also need to show that this is coming from the data you've collected. So that's where it's become very difficult because you have to link that data and the objective facts and weave them into a story that is logical and a progression and that people can, you know, come along with you and then understand each step on the way and then be able to sort of agree or disagree or make their decision.

Right. So it's interesting that you frame this in terms of persuasion. And you're talking about data and visuals and stories. How much emotion is necessary or finds its way in this path? That's the big elephant in the room in the field because we want to be truthful to the data and objective. But the reality is, you know know emotion have a big impact yeah and so and the visuals can carry a lot of that emotion but it's also really hard to measure yeah in fact we have a team in in hci that works on this yeah mary chervinsky's yeah but so basically it's very hard to quantify, but it has a very strong impact.

And so right now it's like an open research problem. Well, you can't really extract it because any kind of a visual, a picture, an image that you show will probably have some kind of an impact on a person looking at it. And so you're like, we want it to be emotional enough that we can move people along and motivate and persuade, but not so much that it becomes propaganda. Exactly. And I guess a lot of that is also from the data itself. That's also hard to control. You know, if I show you data about like, you know, World War I or World War II, it's very

different from sales in surface devices. Although, you know, some people at Microsoft might find it more emotional. I don't know. Depends on if it was a bad month. But that is very hard to control. And there is a number of work that start tackling this, but it's still very sort of recent.

Research. Yeah. Well, let's get specific and start with a super interesting project called DataTunes. This was highlighted in a paper you just presented at CHI this year, and it made me want to be a data scientist because I wanted to use it. That's pretty cool. Tell us about DataTunes and what comics or cartoons have to do with the very serious field of data science. Yeah, so when we started to work on storytelling, of course, my brain went to stories that I read to my kids.

And among those, there's a lot of those illustrated stories and comics. And so I sort of stored that in part of my brain and then went on with, you know, the more sort of serious storytelling. We're making air quotes a lot in here. But then after a while, I thought we should really leverage this notion of comics because they're very sequential and they really are good at conveying, you know, like temporal stories. And so this whole line of research started on how we can leverage this genre of storytelling and then make it data driven and so that's what we call the data comics so we've done a lot of research on those data comics how are they differing from infographics what sort of design principle we can give people to create those and whether they're effective as a storytelling

mechanism. And a lot of those research actually ended up in paper at the CHI conference that you're citing. And then a lot of those papers gave us ideas about a tool that could help you craft those. And that tool is DataTune. And given the work in our group about pen and touch and all of those devices that we have at Microsoft, we sort of tried to leverage the Super Studio and then tried to offer this nice pen and touch experience to craft comics from your data. And so basically what we end up doing is you load some data set that you have, could be an Excel spreadsheet, you know, of your finances. Yeah, please no. And you basically drag and drop part of it to create panels. And then as you duplicate panels and you change the date and time or like you change the style or you change, you know, some of the focus of the part of the data you're caring about, you're basically creating this sequence of panels

and you lay them out in space and it becomes a comic. Nice. So that's the idea behind this data tune. Where does that live right now? Is it still sort of in research or is this downstream and I could get it if I had a surface? It's in between. Somewhere between.

So it's online. So we like to do a lot of our tool like on the web nowadays. So it's a website you can just go to. It's research in the sense that it's a prototype. So, you know, it doesn't handle tons of data. And, you know, it's not really ready for massive usage. But you can use it with some of your data. And then you can craft comics.

And the whole idea behind it is really to help the research community and people interested in trying to create comics and then see the sort of comics they could create and then reflect upon those and figuring out the sort of comics that are created, whether, you know, people enjoy this medium or not. Yeah, this work that you're doing is incredibly visual, as if that weren't obvious from data visualization. And here we are in an audio podcast. So I'm going to encourage our listeners to hit up the Microsoft Research website and go to your page, Nathalie Reich, that's N-A-T-H-A-L-I-E, R-I-C-H-E, and look at some of the videos of this work because it's really cool, the demos that you've got showing how it works. And we can't really convey that. No. But you're doing a good job with words.

Thank you. So, anyway. Well, creative visual expression, which is kind of what we're talking about writ large, is always a challenge given that most people can't draw. And most data visualization tools are actually designed for data professionals of one sort or another. But your team has developed a tool called Data Inc. for people who might want to express personal data more beautifully or to a less professional audience. And this is a really cool project because it was inspired by another really cool project called Dear Data. So give us a bit of the backstory on Dear Data

and how it informed your work on Data Inc. and where that lives now. So Dear Data is such a fascinating project. Like when I learned about it, my heart burst with love for those people. And basically, Georgia and Stephanie, they are both designers. And one is in New York and one lives in London. So every week they would go about collecting data about their life.

So they would agree ahead of time about what sort of data to collect. And then they would collect that data. And then at the end of the week, craft a unique visualization that highlights that data and, you know, some insight about that data. And then craft this unique and whimsical visualization of their personal data. They would actually draw it on a postcard and then send it to each other. And so they did this every week for a year. Can you imagine? Like, that's sort of the dream of any researcher

because you have all of that data plus the visual language and they're all beautiful and they're all very unique and each convey those emotions about the data. So this was just so inspiring. And knowing that a lot of those postcards, they're created by very simple shape so you know you mentioned earlier like you don't know how to sketch or how to draw but you know how to draw a circle yeah and you know how to draw an arc so a lot of those compositions they're

composed by very simple shapes that anyone can draw but yet putting them together it's like this just unique beautiful art but it's also meaningful art right yeah that tells you a story about a little piece of you and so when looking at this we're just so inspired and we thought that must be such you know a hard work first to collect that data and then to draw it because they actually did sometimes spend hours just redrawing it and so we thought maybe by making it easier people could benefit from you know working with their data and maybe sharing it with others without the dedication required to spend hours drawing on the postcard right and the thing is, once you're invested in this one idea, you just go for it

because it's so long, you know, and tedious. So you can really change your mind as you're doing it because it's so tedious. Like if you have done half of your data, you're like, I'm just skipping this circle. So we thought also that the digital device could help you sort of change your mind

because it will just populate the entire data visualization. You know, if you change your mind from circle to square, boom, it's sort of done in a second. So we developed this tool called Data Inc. where you can start from a sketch and then you can bind properties of that sketch. So say the color of the circle or like the stroke width of the circle to a data property and basically generates the entire visualization for all of your data. So what I'm hearing is that you've got the tactile human interface of pen and touch and then you have the power of a digital tool underneath that. So the computer is basically assisting you, right? So you sort of give the command and it just does it to all of your data. So imagine if you've collected, you know, 2000 sleep pattern.

Yeah. You know, you're deciding about the way you want to encode it. So, oh, I'm going to use shades of colors for like how long I slept every night. And once you've decided about that color palette, then basically you tell the computer, okay, just do it for the 2000 points. Autofill.

Yes. Autocomplete. Yes. That's it. Yes. Okay. So we've got these threads of DataTunes, DataInk. There's another project I want to talk about, which is called ActiveInk.

So when we think about traditional data visualization tools, or even other CAD tools, computer-assisted drawing, we think about the classic computer user interface of a mouse, a keyboard, and a screen. But it's much more natural for us to use our hands and pens via touch. And you have an answer to that too. Interface innovation galore. It's Active Ink. Tell us about Active Ink and how it changes our preconceptions about f-inking with ink. Thinking with ink. You do a lot of these wonderful plays on words. It's just, I love it as an English major. So, yeah, in fact, you know, we just talked about Data Inc. And Data Inc. made me realize how expressive the pen can be.

And when I was thinking about this project, I was like, I don't have to think when I use the pen. It's just like, you know, I'm just expressing myself and I don't have to think about how I'm going to do it. It's just so natural for me. I just put my pen down and then that's the way it goes. And so how cool would it be if, you know, I could use this for just expressing my thinking all the time. And especially when I deal with data or when I work with documents and maps and spreadsheets, if I could just express what I'm thinking or like cross-referencing information from one to the other with just my pen. So that's what Active Ink is. And basically the idea of Active is, you know, when you have everything, imagine you have this big canvas and you have all of your pieces of information that matter.

And basically we start drawing on top of it with your pen, you know, to show connection between them. Basically, the system knows what you've drawn on top to, right? It knows the underlying content. So it can sort of transform the rest of the material that you have on your screen. And so that's basically why we say the ink become active. So you draw something that's sort of your externalization of your thinking right there.

And then the system sort of looks at what you've circled there and then shows you relevant information. You know, the way we see the ink is sort of a deferred command that you would do to the system. You didn't yet do the action, but you were going to probably do this. And so we can actually just, boom, remove that friction and that need for you to go and actually do that. We could just do it automatically. So that's what Active Ink is about. All right. So we keep referring to the system. Can you unpack that? Is it the internet? Is it data that I've already collected? Where does all this information come from that it knows?

Well, the system, especially in my world, it's all about a web app. So imagine you go on a URL and then that's an application there. And we always sort of assume that you have a bunch of documents living in the cloud, right? So we know because you've logged in in some other places like, you know, in your Office 365 account. We sort of know all of the documents that you own or that you can have access to. And imagine, you know, it would be like logging on OneNote and just having some special capabilities that we give to the digital ink. Okay. That lives in an app where we sort of know your content and we can retrieve it for you.

Would it incorporate places that I've looked on the web in terms of, you know, searching for information and things like that? Exactly. And so that's another component. Haha, you're interesting. You just... Little threads everywhere. Yeah, look at you. But yeah, one thing, for example, that we transfer from pen and paper

to active ink in the digital world. So you know how when you sort of read documents, you may write a little asterisk someplace, right? Like on important information or important paragraph. And then you put this asterisk in some other places. And so like as a reference or a bookmark. So we sort of do this in Active Ink. So when you browse the web,

you can circle part of a webpage that you're interested in. And you could usually just refer it with the little symbol or little asterisk. And so in Active Ink, then you can just draw that same shape and search for similar ones. And so it can retrieve for you all of the little shapes that were in other pages and then just retrieve that part of the content in the Active Ink canvas. Wow. So it's sort of a way to gather information as well that you've visited. Right.

Even if you can't remember that you visited it, but you might've made a mark on it and it comes back to you. Exactly. Data boomerangs. Golly. Every era has new media and technologies and the requisite need for literacy around them, for better or worse. There's print, media literacy, digital literacy, computer literacy, and now you're suggesting that we need to add data visualization literacy to the list. Not only are you suggesting it, but you've actually created a platform for it to be used in classrooms. You call it C'est la vie, which is a lovely play on words

for the French, c'est la vie, that's life, that's data, that's visualization. Tell us about C'est la Vise. What is it and why do we need it? You know, as I said, we're like sort of surrounded by data and we're surrounded by visualization everywhere, including infographics. But, you know, there's not a place on social media or on your newspaper. There's not a place that doesn't really have some piece of visual information. And yet what's interesting, you know, since I have those two kids, I sort of know what you learn in elementary school now that I had forgotten. Yeah, right. But I find it interesting that, you know, this part of the curriculum hasn't really

been updated. There wasn't much to start with. Like, I don't really recall having, you know having classes on building data. But so there's really this need that we need to sort of teach kids about creating those visuals and decoding them properly. And if you look, like we did, with a bunch of colleagues at Toronto and Turkey, actually, we looked at a lot of those K-4 or K-12 material that teachers are using,

sort of looking at the type of visualization that they're encountering during the school. It's kind of scary, like, how sort of a small amount there is. It's mostly, you know, bar charts and light charts, not even pie charts, really. From that, we sort of... And then none of them is digital either. So there's a lot of content that you can access on the web like nowadays, right?

And then you can click on it and transform it. So none of that made its way into the curriculum as far as we saw. So we decided to build that platform that sort of teaches kids to represent data in different ways through different visual mapping. And like some of the key idea we wanted to convey is that data can be represented in many different ways. So the same data, but many different visuals. And each of those visuals could convey very different insights about that data. So I wanted to try to convey this and then also try to have some of the interaction literacy, so the sort of way you interact with the visuals. And, you know, spur the conversation about just

thinking a little bit more critically about visualization. And one visual, you know, does it accurately represent the data or not? And sort of spark a little bit of thinking around this in the classroom. Right. I would imagine, too, going back to the emotion thing, you could represent by a pie chart or 10 hamburgers and get a different emotional response or, you know, visceral response. Exactly. And then one of the core findings that, you know, looking at all of that material that happened in school is that gradually over the years, the visuals go from very concrete, you know, little apple that you stack to way more abstract visual, like a bar chart. But yet, it was never made crisp to the kids that those are the same data.

Different representation, but of the same data. So we tried to convey this, you know, how we go from very concrete to very abstract visual representation and then try to convey, you know, what got lost in translation, right? Right. The apple is not an apple anymore, but it's this red square. But yeah, it represents the same thing. Right. And so we try to touch upon some of those concepts that, you know, the visual can convey

a very interesting semantic, because if you have a picture of an apple, there's very few data set that could match that, right? Right, exactly. But a red square could be anything. So there's a lot of those concepts there. So have you just used this in research that you were doing for a paper, or is this in a pipeline somewhere as well? It's in a pipeline.

So much good stuff that might be coming out. Yeah, I'm trying to build connection with the Hacking STEM program that we have in Microsoft and try to help them build their data science and data literacy curriculum and provide some components that are visual interfaces for the kids. Well, we've reached, once again, the infamous what could possibly go wrong section of the podcast. And on the heels of that conversation about data visualization literacy, your research revolves around making really powerful tools that enable people to create compelling and perhaps very persuasive visual narratives. So is there anything about this that keeps you up at night, Natalie?

And if so, what can we do about it aside from saying, I hope they use their powers for good and not for evil? Right. Well, that's exactly the inspiration behind the C'est la Vise project. So with a bunch of colleagues, we sat down and we really reflected upon how visualization has this superpower for communication. And as you mentioned, it could be very misleading. You could like, you know, use fake data, but you could also sort of tweak the visual to sort of convey the opposite of what the data tells you. So we sort of sat down and then reflected about what we can do.

What can we do? Can we like enforce some design principle when you create the visualization but i guess it's just really hard because you could just make up the data right and there's no way really we can stop people from collecting or crafting wrong data sets or or visualizations thereof. and to try to ponder, you know, how is this collected? You know, is that data missing? Is that a representative data set? And also beyond that, you know, those important questions, look at the visuals and really try to change the way it's represented in their mind. Like, is that a good mapping for it?

And so that's the motivation behind going in schools and looking at visualization literacy and trying to help teach and, you know, help convey that you have to think critically about those pictures, especially because those are crafted by human beings. They're not like a photograph. Even photograph can be manipulated, right? We could go a long way down that road.

But those are truly manipulated. So many, you know, data is curated, visuals are chosen and selected. So really try to educate people and kids in particular to think more critically about what information they're consuming. and rightly so, of developing tools that will help people communicate better and more powerfully. And you just have to hope with education and being optimistic that you're not going to lose sleep over what people could do wrong with it. Yes. Natalie, what's your story? This is the part of the podcast where I like to ask where you came from, what got you interested in your career, particularly for you, data visualization research. And how did you end up here at Microsoft Research all the way from France? So I guess I always wanted to be an artist.

And when I told my mom, I'm going to do a major in art, she said, that's not going to bring, you know, money on the table and you can't live with this. So just do computer science instead. And my mom was a secretary and an accountant and they just had to, you know, switch everything to computers at the time. So she knew, you know, for her, computers were going everywhere. So that's going to bring money on the table, right? So she sort of decided for me. So I made my way to computer science. And then the funny story is

once I was in engineering school in France and I got really bored by all of this computer science thing and I was still doing a lot of art. And then I thought, you know what? I'm big now. I can choose for myself. So I'm going to go have fun in Australia

because I wanted to visit. And then I'm just going to give up all of this engineering stuff and then just going to do my painting career that I always wanted. And my idea of sort of not working and having fun in Australia, but yet sort of finishing my degree was to end up in a research lab.

So I picked this research lab in Sydney and they were doing data visualization, but for me, it wasn't really important. I didn't even know what it was. And so once I got there, I thought, you know what, I'll just show up and then I'll just go to the beach because they don't do anything in research, right? And then when I started meeting with people and they told me what they were doing, I just thought this is it. I can do art with data and computers and this is what I want to do. And so it's very interesting because instead of just, you know, what I envision, which is, you know, go one hour to the lab and then the rest on the beach,

I sort of did the opposite. I just lived in the lab and then I did all of that research. And then when I came back to France in the engineering school, then I chose a track where I could do my master's degree at the same time and then a PhD in that field. And the last year of my PhD, I did a joint project with Microsoft Research, and that's sort of how I ended there. And the rest is histoire. All right, this is my new favorite question. What's one interesting thing that people might not know about you? A trait, a characteristic, a life event, something that may have influenced your career as a researcher? And even if it didn event, something that may have influenced your career as a researcher? And even if it didn't, tell us something interesting about yourself anyway.

You know, I was really into helping kids to think critically about data. And I said, I'll start with my daughter. At the time, she was six. And then we walked down the park and then I said, okay, we're going to collect data about dogs and like, you know, how big they are and their fur. And then, you know, after we done that for about an hour, come back home and I'm like, I'm going to create an Excel chart for you. And we're going to show you, you know, how you transform that data into a chart, which, you know, my daughter is sort of nice. But yet, you know, I was doing all of this in Excel and she's looking at me and she's like, why don't you just draw it?

And I had no good answer. So I thought maybe we should just do that. Just draw it. And that's sort of what started me thinking about pen and touch. And, you know, the six-year-old chose the path of my research first it's your mom then it's your daughter yes well as we close and i'm sad to close because i've enjoyed this conversation very much you get the last word what advice would you give or thoughts would you share with anyone in our audience who might be interested in making beautiful data more accessible?

So I guess what I've learned is to rely a lot on my gut instincts and listen to other people in my life, you know, as you pointed out, my mom and my daughter but really when you have this gut feeling that this is a interesting direction even though it may look silly right use comics really or draw something really you know just follow up that instinct and just try to develop the idea and make it happen I guess that's my piece of advice. Just try to follow what you really think is a good idea and then do it despite what everyone else might say. Or because of what everyone else might say. Nathalie Rich, it's been delightful.

Thank you for coming on the podcast today. Thank you for coming on the podcast today. Thank you so much. To learn more about Dr. Natalie Reich and how researchers are helping us to visualize world data, visit microsoft.com slash research.