{"id":449823,"date":"2017-12-20T08:44:17","date_gmt":"2017-12-20T16:44:17","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=449823"},"modified":"2023-05-01T11:56:45","modified_gmt":"2023-05-01T18:56:45","slug":"visualizing-data-big-ideas-dr-steven-drucker","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/podcast\/visualizing-data-big-ideas-dr-steven-drucker\/","title":{"rendered":"Visualizing Data and Other Big Ideas with Dr. Steven Drucker"},"content":{"rendered":"\n<figure class=\"wp-block-image alignright size-medium\"><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/sdrucker\/\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2017\/12\/BlogPost_600x400_Steve_Podcast-300x200.jpg\" alt=\"photo of Dr. Steven Drucker - Principle Researcher\" class=\"wp-image-449826\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/12\/BlogPost_600x400_Steve_Podcast-300x200.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/12\/BlogPost_600x400_Steve_Podcast.jpg 600w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><figcaption class=\"wp-element-caption\">Dr. Steven Drucker &#8211; Principle Researcher<\/figcaption><\/figure>\n\n\n\n<h2 id=\"episode-5-december-20-2017\" class=\"wp-block-heading\">Episode 5 | December 20, 2017\u00a0<\/h2>\n\n\n\n<p><strong>Visualizing Data and Other Big Ideas with <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/sdrucker\/\">Dr. Steven Drucker&nbsp;<\/a><\/strong><\/p>\n\n\n\n<p>In a wide-ranging interview, veteran Microsoft Researcher, Dr. Steven Drucker talks about his work in data visualization, the importance of clear communication in a world of complex algorithms and big data, and the long, slow work of big breakthroughs. He also offers some pro-tips to aspiring researchers, and tells us why stand-up comedy is an important skill for computer scientists.<\/p>\n\n\n\n<section class=\"wp-block-msr-subscribe-to-podcast subscribe-to-podcast\">\n\t<div class=\"subscribe-to-podcast__inner border-top border-bottom border-width-2\">\n\t\t<h2 class=\"h5 subscribe-to-podcast__heading\">\n\t\t\tSubscribe to the <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/podcast\">Microsoft Research Podcast<\/a>:\t\t<\/h2>\n\t\t<ul class=\"subscribe-to-podcast__list list-unstyled\">\n\t\t\t\t\t\t\t<li class=\"subscribe-to-podcast__list-item\">\n\t\t\t\t\t<a class=\"subscribe-to-podcast__link\" href=\"https:\/\/itunes.apple.com\/us\/podcast\/microsoft-research-a-podcast\/id1318021537?mt=2\" target=\"_blank\" rel=\"noreferrer noopener\">\n\t\t\t\t\t\t<svg class=\"subscribe-to-podcast__svg\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" fill=\"black\" viewBox=\"0 0 32 32\">  <path d=\"M7.12 0c-3.937-0.011-7.131 3.183-7.12 7.12v17.76c-0.011 3.937 3.183 7.131 7.12 7.12h17.76c3.937 0.011 7.131-3.183 7.12-7.12v-17.76c0.011-3.937-3.183-7.131-7.12-7.12zM15.817 3.421c3.115 0 5.932 1.204 8.079 3.453 1.631 1.693 2.547 3.489 3.016 5.855 0.161 0.787 0.161 2.932 0.009 3.817-0.5 2.817-2.041 5.339-4.317 7.063-0.812 0.615-2.797 1.683-3.115 1.683-0.12 0-0.129-0.12-0.077-0.615 0.099-0.792 0.192-0.953 0.64-1.141 0.713-0.296 1.932-1.167 2.677-1.911 1.301-1.303 2.229-2.932 2.677-4.719 0.281-1.1 0.244-3.543-0.063-4.672-0.969-3.595-3.907-6.385-7.5-7.136-1.041-0.213-2.943-0.213-4 0-3.636 0.751-6.647 3.683-7.563 7.371-0.245 1.004-0.245 3.448 0 4.448 0.609 2.443 2.188 4.681 4.255 6.015 0.407 0.271 0.896 0.547 1.1 0.631 0.447 0.192 0.547 0.355 0.629 1.14 0.052 0.485 0.041 0.62-0.072 0.62-0.073 0-0.62-0.235-1.199-0.511l-0.052-0.041c-3.297-1.62-5.407-4.364-6.177-8.016-0.187-0.943-0.224-3.187-0.036-4.052 0.479-2.323 1.396-4.135 2.921-5.739 2.199-2.319 5.027-3.543 8.172-3.543zM16 7.172c0.541 0.005 1.068 0.052 1.473 0.14 3.715 0.828 6.344 4.543 5.833 8.229-0.203 1.489-0.713 2.709-1.619 3.844-0.448 0.573-1.537 1.532-1.729 1.532-0.032 0-0.063-0.365-0.063-0.803v-0.808l0.552-0.661c2.093-2.505 1.943-6.005-0.339-8.296-0.885-0.896-1.912-1.423-3.235-1.661-0.853-0.161-1.031-0.161-1.927-0.011-1.364 0.219-2.417 0.744-3.355 1.672-2.291 2.271-2.443 5.791-0.348 8.296l0.552 0.661v0.813c0 0.448-0.037 0.807-0.084 0.807-0.036 0-0.349-0.213-0.683-0.479l-0.047-0.016c-1.109-0.885-2.088-2.453-2.495-3.995-0.244-0.932-0.244-2.697 0.011-3.625 0.672-2.505 2.521-4.448 5.079-5.359 0.547-0.193 1.509-0.297 2.416-0.281zM15.823 11.156c0.417 0 0.828 0.084 1.131 0.24 0.645 0.339 1.183 0.989 1.385 1.677 0.62 2.104-1.609 3.948-3.631 3.005h-0.015c-0.953-0.443-1.464-1.276-1.475-2.36 0-0.979 0.541-1.828 1.484-2.328 0.297-0.156 0.709-0.235 1.125-0.235zM15.812 17.464c1.319-0.005 2.271 0.463 2.625 1.291 0.265 0.62 0.167 2.573-0.292 5.735-0.307 2.208-0.479 2.765-0.905 3.141-0.589 0.52-1.417 0.667-2.209 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.33-.036.48-.09v-9.418a1.406 1.406 0 00-.48-.09zm1.44 0V21.76c0 .793.646 1.44 1.44 1.44h10.557c.793 0 1.44-.647 1.44-1.44V11.204H9.878zm14.876 0c-.169 0-.33.035-.48.09v9.418c.15.052.311.09.48.09a1.44 1.44 0 001.44-1.44v-6.719a1.44 1.44 0 00-1.44-1.44zM11.8 24.16v1.92a1.92 1.92 0 003.84 0v-1.92h-3.84zm5.759 0v1.92a1.92 1.92 0 003.84 0v-1.92h-3.84z\"\/><\/svg>\n\t\t\t\t\t\t<span class=\"subscribe-to-podcast__link-text\">Android<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/li>\n\t\t\t\n\t\t\t\t\t\t\t<li class=\"subscribe-to-podcast__list-item\">\n\t\t\t\t\t<a class=\"subscribe-to-podcast__link\" href=\"https:\/\/open.spotify.com\/show\/4ndjUXyL0hH1FXHgwIiTWU\" target=\"_blank\" rel=\"noreferrer noopener\">\n\t\t\t\t\t\t<svg class=\"subscribe-to-podcast__svg\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" fill=\"none\" viewBox=\"0 0 32 32\"><path fill=\"currentColor\" d=\"M16 4C9.383 4 4 9.383 4 16s5.383 12 12 12 12-5.383 12-12S22.617 4 16 4zm5.08 17.394a.781.781 0 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class=\"subscribe-to-podcast__svg\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" fill=\"none\" viewBox=\"0 0 32 32\"><path fill=\"currentColor\" d=\"M6.667 4a2.676 2.676 0 00-2.612 2.13v.003c-.036.172-.055.35-.055.534v18.666c0 .183.019.362.055.534v.003a2.676 2.676 0 002.076 2.075h.002c.172.036.35.055.534.055h18.666A2.676 2.676 0 0028 25.333V6.667a2.676 2.676 0 00-2.13-2.612h-.003A2.623 2.623 0 0025.333 4H6.667zM8 8h1.333C17.42 8 24 14.58 24 22.667V24h-2.667v-1.333c0-6.618-5.382-12-12-12H8V8zm0 5.333h1.333c5.146 0 9.334 4.188 9.334 9.334V24H16v-1.333A6.674 6.674 0 009.333 16H8v-2.667zM10 20a2 2 0 11-.001 4.001A2 2 0 0110 20z\"\/><\/svg>\n\t\t\t\t\t\t<span class=\"subscribe-to-podcast__link-text\">RSS Feed<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/li>\n\t\t\t\t\t<\/ul>\n\t<\/div>\n<\/section>\n\n\n\n<h3 id=\"transcript\" class=\"wp-block-heading\">Transcript<\/h3>\n\n\n\n<p>Steven Drucker: It\u2019s hard enough to make sense about small amounts of data, much less big data. There are so many problems with it. And each of them is like a speed bump. And I really want to lower some of those speed bumps and make it a little easier. But at the same time, if you lower them too much, you get a lot of people that are making false conclusions, misunderstanding and misapplying data.<\/p>\n\n\n\n<p><strong>Host: You\u2019re 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\u2019m your host, Gretchen Huizinga. Today I\u2019m talking with veteran Microsoft researcher Dr. Steven Drucker about data visualization, his advice for aspiring research scientists, the long, slow work of big breakthroughs, and why standup comedy is an important skill for computer scientists. That, and much more, on this episode of the Microsoft Research Podcast.<\/strong><\/p>\n\n\n\n<p><strong>Host: Tell me a little bit what your background is and how you actually landed doing what you\u2019re doing.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Oh boy, I have a long and twisty path. I actually started out as a neuroscientist. I did my undergraduate work in neurosciences, and I was really inspired as a kid by the sort of bionic man, and prosthetics, and trying to hook up devices to the brain. And I got into robotics from that. And so there was \u2013 from neurosciences to robotics was a very logical progression. Then \u2013 maybe this is like going back too far. But at the time, my advisor didn\u2019t get tenure. So I left. And I switched over to a different group that was doing computer graphics, because computer graphics and robotics are really similar. And so I did computer graphics. That\u2019s what I did my PhD in. And I came out to Microsoft 22 years ago where there was a group that was looking at graphical environments for social communication. Did that for a number of years, and you know I was hoping that it was going to be the new media form. That this was going to be the new way to communicate, the new way to tell stories. And yet, it got to be very much sort of hit driven, iterative. Kind of like what Hollywood is. Here\u2019s the next version of, you know, Tomb Raider 3, or Grand Theft Auto 5. You see this sort of iteration. They\u2019re not taking risks. So, I got a little bit more interested in sort of just organizing and collecting of media. So, I did a lot of things with photos and photo collections, and tagging and organizing photo collections. Partly because in some ways, this was a problem that we were having at home. Well, from organizing and collecting media, it\u2019s not a big leap to go to large collections of things, and large data, and understanding data and communicating with that data. And I\u2019ve been doing that for the last 8 years or so.<\/p>\n\n\n\n<p><strong>Host: Give the listeners an elevator pitch of what you do at Microsoft Research.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Well, I\u2019m in Microsoft Research. And I focus on data visualization, which is trying to understand complex data and communicate insights about that data to others, to take actions on it. And building tools so people can in turn understand that data themselves. So, less data analysis, more building tools so that other people can understand and communicate that data.<\/p>\n\n\n\n<p><strong>Host: You\u2019re building tools for data analysis, though?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Exactly, yes. So \u2013 I mean, there were a lot of people that got hired on as a data analyst. And then they might be using, whether it\u2019s programming, or existing tools to do that. And they have good skills in that. But I\u2019m also really interested in kind of reaching directly to the public. I think \u2013 again, I think about myself as a communicator even more than an analyzer.<\/p>\n\n\n\n<p><strong>Host: When you say building tools, my mind goes directly to the product groups. But you\u2019re in Microsoft Research. Where on the spectrum of building are you?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: I\u2019ve been in Microsoft Research for 22 years. And I\u2019ve gone back and forth in that spectrum. And I think that\u2019s part of why someone might go to Microsoft Research as opposed to going to an academic department. In Microsoft Research, we\u2019re very bottom up driven, so we come up with new ideas that aren\u2019t necessarily what the customers are asking for. It can be very problematic if you just ask customers what they want. I don\u2019t know if you\u2019ve heard these old stories about how you drive cars? In the very first focus groups on driving a car were reins. Where you pull on one side or the other. Because that\u2019s what people were accustomed to. So you can\u2019t necessarily ask existing people for solutions to their problems. And I look at research to a certain extent as trying to break out of innovator\u2019s dilemma. Where we\u2019re trying to come up with new ideas from the bottom up. And sometimes they go into products, and sometimes they go into academic papers and sometimes they just go nowhere, and we drop them.<\/p>\n\n\n\n<p><strong>Host: Some people say that one reason technology is advancing so quickly, exponentially \u2013 if that\u2019s where we go with it \u2013 is that we\u2019re using tools now that are better than the tools before. And so each year, we use \u2013 leading to the question \u2013 what tools are you actually using to do your work? What do you find yourself \u2013 like you have a technological tool belt. What do you pull out the most to do the work you like to do on \u2013 like with SandDance. What did you make that with?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: SandDance is built on top of JavaScript, and we use WebGL, which is the 3D graphics library if you\u2019re doing \u2013 on a webpage. So, this is interesting. The tools that we use to build it are text editors and compilers. They\u2019re not that different than what I was doing 25 years ago. So that\u2019s kind of funny when you look at \u2013 my tool usage has not changed that much. That being said, we are standing on the shoulders of all these things. I don\u2019t start from scratch each time. I actually really like the open source movement, in that we are \u2013 SandDance and most of the Power BI custom visuals are available as open source projects. We actually got the permission to open source it. And it\u2019s fine to go ahead and do that. I think it\u2019s great, because then we are all standing on everybody else\u2019s shoulders. And then we\u2019re combining them in different ways. And I think that is a huge change.<\/p>\n\n\n\n<p><strong>Host: Right. And I remember back in the day, intellectual property, IP being this big thing, and it\u2019s proprietary. We\u2019re not going to let anyone peek in the Kimono.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Yes.<\/p>\n\n\n\n<p><strong>Host: And it\u2019s like, nobody\u2019s thinking that way anymore.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: It does feel like that. And I think it\u2019s great. Because I can get people out of school. And they could be, oh, yeah, I can do this sort of thing. Instead of saying, well, when you come, you\u2019re going to spend about three quarters of a year learning how to do this thing before you can actually make any contribution. And now it\u2019s like, well, here\u2019s this library out there that we can use this. Because it\u2019s really been pre-approved that we can use it. And we can port this library, and we\u2019ll get something new.<\/p>\n\n\n\n<p><strong>Host: Which is awesome.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: And that\u2019s amazing.<\/p>\n\n\n\n<p><strong>Host: What advice would you give aspiring researchers? What knowledge base or skill sets do you think they need?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: The way I look at that is, I don\u2019t want everybody to be a computer scientist. I look at coding as this sort of, one of the new tools to have. I think learning to code is going to be fundamentally important for any job that you have, whether you\u2019re going to be someone writing the software, or a lawyer, or \u2013 so that \u2013 there\u2019s that. There\u2019s also the style of algorithmic thinking. All of those things are important. Certainly, understanding data is a big challenge. And it\u2019s not a challenge that\u2019s going away. So, having some backgrounds\u2026 in programming enough to be able to do the data statistics. Machine-learning is hot right now. There might be an overhype. Maybe not. I mean, this is \u2013 we\u2019re seeing results. So those knowledges are really important. I guess something that we haven\u2019t discussed that we\u2019ve discussed before is I really look at this computer as a tool that augments our abilities. And I think one of the things that we do bring in, that is harder to duplicate right now is the creativity. I like to see people that have a design sense, who are aesthetic \u2013 you know, can do things creatively and aesthetically in addition to technically. I think it\u2019s really important for someone to have the skills to talk about what they\u2019re doing, why it\u2019s important, and make that accessible for a wide audience.<\/p>\n\n\n\n<p><strong>Host: Has that been a problem for computer science people before?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Oh, not at all. Yeah. I mean \u2013 we have periodically from MSR something called Tech Fest, where we show the rest of the company the things we\u2019re working on. And that\u2019s always fun, because you\u2019re up there talking about this stuff. Some people still are like, oh my god, this is \u2013 you\u2019re not really appealing to everybody here. But if you can explain your things in way that gets people excited about it, and pulls them in, that helps everybody. So, as much as the technical abilities, I think this communication is as important. You know, I\u2019m in research, so I go to conferences. And I speak at these conferences. A skill that\u2019s really important there that\u2019s surprising is stand-up comedy.<\/p>\n\n\n\n<p><strong>Host: Oh my gosh, yes.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: To be able to do improv, and to be able to talk a question, be able to understand that question, and respond to it.<\/p>\n\n\n\n<p><strong>Host: Let\u2019s move onto data. To use a metaphor, we\u2019re kind of swimming in it. And it seems like the problem now is more about how we make sense of it, how we find what we\u2019re looking for, and what we make of it once we find it. So, what\u2019s going on in your world that you\u2019d contend is helping us?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Again, that\u2019s a huge question. And it\u2019s hard enough to make sense about small amounts of data, much less big data. I find that it\u2019s really hard to make sense of data. You need a lot of skills and a lot of sort of experience and background in statistics and programming and shaping that data. There are so many problems with it. And each of them is like a speed bump. And I really want to lower some of those speed bumps and make it a little easier. But at the same time, if you lower them too much, you get a lot of people that are making false conclusions. I\u2019m not even talking about fake news. But just sort of misunderstanding and misapplying data.<\/p>\n\n\n\n<p><strong>Host: So, let me make that concrete from a research perspective, because you\u2019re a researcher. What kinds of things are you doing? Because I love what you\u2019re saying, and I want that for me.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Well, I \u2013 one thing that we\u2019ve done in my group in particular is worked on a research project that was showing data in \u2013 we call it unit visualization, where you show every piece of data organized in different ways to show different conclusions. And that was great as a research project. You give talks. But we also ship that in Power BI as a custom visual. So anybody \u2013 actually, you can go to a website right now and you can try it without, for free. For just trying these ideas, load your own data set in there, and experiment with that, and look at these things, and examine your data in different ways.<\/p>\n\n\n\n<p><strong>Host: You\u2019re professionally interested in how people communicate with data.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Yes. I definitely am.<\/p>\n\n\n\n<p><strong>Host: Unpack that.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: One of the things I find about a lot of the academics that I work with, or a lot of the researchers is that they are fairly good communicators at their own audience. And they\u2019re not very good communicators \u2013 they don\u2019t necessarily understand when people don\u2019t understand them. You know, how could they not get this? And part of I think effective communication is to try to put this in ways that they \u2013 why I like visualizations \u2013 they can see it. They can ask questions about it. And you have a little bit of a dialogue about it. So, I think that\u2019s one element is understanding where people don\u2019t communicate, don\u2019t understand. Our visual systems are highly developed, and we can see patterns. Oftentimes, we can see patterns when they\u2019re not there. But we can start seeing relationships. And again, we\u2019re wired that way. So, if we can hook into that and pique their interest and start having them understand that, that\u2019s kind of what I\u2019ve been focusing on. It\u2019s interesting. Trying to do this in a podcast is very difficult, because this is inherently visual. There are auditory visualizations that can do that as well. But again, so much of our brain is devoted to understanding visual phenomena.<\/p>\n\n\n\n<p><strong>Host: We talked about the current tension between \u2013 that swirls around deep neural networks. Now, this is a topic that a lot of people don\u2019t really understand. And I don\u2019t necessarily think we have to unpack it here. But algorithms have become so complex that people can\u2019t even understand them, let alone explain them. And you mentioned a recent ruling in the EU that says you can\u2019t make a decision with an algorithm that a human can\u2019t explain.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: I think there\u2019s a requirement to explain those decisions.<\/p>\n\n\n\n<p><strong>Host: Okay, so you can make a decision, but you better be able to explain it?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Exactly. And you think of how important that is if you\u2019re going to be paroling someone, or hiring someone. Or firing someone. And you don\u2019t want to say, well, the computer said to fire you, and so we\u2019re firing you.<\/p>\n\n\n\n<p><strong>Host: Let\u2019s back up. Let\u2019s back up. Is that happening?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: There\u2019s parole. This has been a big argument recently about sort of algorithmic systems for recidivism. How likely is it for someone to\u2026<\/p>\n\n\n\n<p><strong>Host: Repeat.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: \u2026commit the crime again? And they\u2019re trained on data. And of course, the data might contain biases in it. It might be trained on a population that\u2019s not, not the same population.<\/p>\n\n\n\n<p><strong>Host: Oh my gosh. That is such a huge thing.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: It is.<\/p>\n\n\n\n<p><strong>Host: I mean, on so many levels.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Yes, exactly. And machine learning to me is amazing because we\u2019ve had huge strides and progress over the last decade about doing things like understanding voice, and objects, and maybe we\u2019ll have intelligent driving cars. But at the same time, a lot of it really is just looking at statistical relationships between input and output. And we don\u2019t really know what\u2019s going on inside those relationships. And sometimes there might be, again, erroneous data that leaks into it, or some small problems. And I actually see visualization playing an important role in this. It\u2019s very, very hard to visualize these models. But maybe to try to understand where they\u2019re working and where they\u2019re not working, or give people an idea. Or perhaps we can explain most of the cases by a simpler, more visual model. And then sometimes we can\u2019t explain the rest. And so what do we do when there are discrepancies? But I think this is one of these big areas right now, or big challenges.<\/p>\n\n\n\n<p><strong>Host: You talked about big areas and challenges. In my mind, I kind of envision researchers in their labs working for the next big breakthrough. Is that what you guys are doing?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: On one hand, yes. We all want breakthroughs. We always want the big thing. But I look at a lot of the big things now, and like Facebook. Facebook is like the 7th or 8th iteration of a peer-to-peer friend network. Twitter, that was like instant messaging. And you already have all these emails. You have all of these things that are huge now, that aren\u2019t exactly breakthroughs. But they\u2019re big because the time was right, the circumstances were right. They had the right sauce, whatever it was that did that.<\/p>\n\n\n\n<p><strong>Host: And the technology that enabled it was right.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Yes, that\u2019s right. Exactly. And you look at the iPad. There had been pen computing going on since the mid-80s. So these things have happened over and over and over again. And sometimes the time is right. Now, so what are those big breakthroughs? We often don\u2019t know what those breakthroughs are until, you know, hindsight.<\/p>\n\n\n\n<p><strong>Host: That was a breakthrough, but I didn\u2019t call it that when I was making it.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: That\u2019s right. And I think there is a rush towards some of these breakthroughs, whether it\u2019s with augmented reality. There\u2019s a lot of interest in that right now. Or self-driving cars. A lot of interest in that. I think we all kind of want that \u2013 oh, things were like this before, and I\u2019ve done something to make it fundamentally different. Academic research and research in general is not usually like that. It\u2019s usually very, very incremental and careful. And there\u2019s this balance between that careful science, and the, oh my god, we\u2019ve made something big. And we\u2019ve got this rush to a new startup, and that\u2019s really exciting. And I think you kind of want a little bit of the energy from both of those to go together.<\/p>\n\n\n\n<p><strong>Host: I love, love, love how you\u2019ve said that, because it\u2019s hard work.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Yeah.<\/p>\n\n\n\n<p><strong>Host: And it\u2019s slow work. And it\u2019s rarely the big bang.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: The metaphor I usually like is standing on the beach when the waves are coming in. If you just stand there and you watch any single wave, it\u2019s only a little bit further than the last wave. But if you\u2019re standing way at the top, looking down, and looking at this over several hours, like oh my god, I was down there before. And now it\u2019s way up here.<\/p>\n\n\n\n<p><strong>Host: It\u2019s called the tide. Not the wave.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Exactly. Exactly. And when you are standing on that beach amongst doing that work, it\u2019s very incremental. You\u2019re just trying to get past those last little wavelets. And then again, when you step back, wow, this is the \u2013 and then you have to step back and examine, oh, that area\u2019s about to be flooded. We should do something about that!<\/p>\n\n\n\n<p><strong>Host: On the spectrum of, let\u2019s just say Microsoft, because that\u2019s what we\u2019re talking about. But you\u2019ve got your research people that are the out there in five years. You\u2019ve got your product people that are right now. And then you\u2019ve got your end consumers. And so that spectrum of how you contribute to both the company and then the world, the concentric circles out. How do you envision \u2013 how do you internalize that what you do? How does it make a difference?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Yeah, it changes at different times. Sometimes that\u2019s \u2013 you shoot for that outside circle of the world, but along the way, you figure out, like here\u2019s a stepping stone that I can publish, and here\u2019s \u2013 so I\u2019ll at least get this other audience of people working on it. And maybe here\u2019s a product that I might be able to impact, and they\u2019ll reach a different audience. And that\u2019s sort of leading there. So sometimes it\u2019s like, I\u2019ve got this directed thing, and I\u2019ll do those steps stones along the way. Other times, it\u2019s almost the reverse. It\u2019s sort of like, someone has come up with this thing that they really want, and you can give them, you know, beyond that solution. And it was just the sort of short-term that takes you off in a new direction that you didn\u2019t know before. I got into that sort of photo management stuff, partly because talking \u2013 because I had that problem. Other people were talking. We were talking about it at lunch, and we were seeing that. And this was before iPhoto, and before there were any of those organizers&#8230; Let\u2019s focus on this. And we did this for a good five, six years. And that led to a whole flowering of different areas. But that really was in response to some short-term problems that I was thinking, I think this is big.<\/p>\n\n\n\n<p><strong>Host: And don\u2019t you think sometimes that\u2019s how a lot of big solutions started out as accidents?<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Yes.<\/p>\n\n\n\n<p><strong>Host: I think I\u2019m going to test for this, and it turns out that that medicine worked for that instead.<\/strong><\/p>\n\n\n\n<p>Steven Drucker: Yeah, in that field, and all the others\u2026 In fact, I think it was in Hamming\u2019s Turing Award acceptance speech, he talks about how that was the almost the kiss of death, because everybody then was expecting just big projects from him. And therefore, those thousand little tiny projects didn\u2019t have a chance for any of them to bloom into something else. So, starting with this idea it\u2019s going to big can be deathly. You need to have this mix of, you know, these bigger things that people are collaborating on, and the little flowers blooming. So, it\u2019s really hard to say, which circle are you aiming at right now, and fluidly moving\u2026<\/p>\n\n\n\n<p><strong>Host: Right. Sometimes it\u2019s a moonshot. And sometimes it\u2019s like, I\u2019m just going to do what, you know\u2026<\/strong><\/p>\n\n\n\n<p>Steven Drucker: That\u2019s right. Because maybe that\u2019s going to lead somewhere else. And it\u2019s going to lead to something big unexpectedly.<\/p>\n\n\n\n<p><strong>Host:<\/strong> Steven Drucker, thanks for coming in and sharing your passion and your stories and your projects, and everything else.<\/p>\n\n\n\n<p>Steven Drucker: It\u2019s been my pleasure.<\/p>\n\n\n\n<p><strong>Host: To learn more about Dr. Steven Drucker\u2019s work in data visualization, and other great things going on in Microsoft Research, visit Microsoft.com\/research.<\/strong><\/p>\n\n\n\n<p>[End of recording]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In a wide-ranging interview, veteran Microsoft Researcher, Dr. Steven Drucker talks about his work in data visualization, the importance of clear communication in a world of complex algorithms and big data, and the long, slow work of big breakthroughs. 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