Become a Creator today!Start creating today - Share your story with the world!
Start for free
00:00:00
00:00:01
The Early Days of Tableau with Jock D. Mackinlay
306 Plays3 years ago
Jock D. Mackinlay is the first Technical Fellow at Tableau Software. He believes that well-designed software can help a wide-range of individuals and organizations work effectively with data, which will improve the world. He is an expert in visual analytics and human-computer...
The post Episode #211: Jock D. Mackinlay appeared first on PolicyViz.
Recommended
Transcript
Introduction of Jock McKinley
00:00:13
Speaker
Welcome back to the Policy Vis podcast. I'm your host, John Schwabisch. And on this week's episode of the show, I am excited to have Jock McKinley join me on the program. You probably know Jock from his work at Tableau Software. If you are a researcher in the data visualization field, you surely know Jock's name from his long experience and his long
00:00:35
Speaker
a body of work in the field. He has basically done everything there is to do when it comes to visualizing data. And so on today's episode, we talk about his career.
Career and Contributions of Jock McKinley
00:00:45
Speaker
We talk about his work at Tableau. We talk about the various teams that he's built and pulled together to help make that tool what it is today.
00:00:53
Speaker
So I hope you're going to enjoy this week's episode of the show. I'm sure you will. If you have comments or questions or suggestions for new guests or other things that you'd like to hear about on the show, please do reach out. You can reach me on Twitter. You can reach me at the policyvis.com website, or you can reach me at this YouTube channel where you might be watching this video of my conversation with Jock. So on to this week's episode of the policyvis podcast. Here is my conversation with Jock McKinnon.
00:01:25
Speaker
Hey, Jock, good afternoon. Well, good morning, your time. How are you? Good to see you again. I'm fine. It's good to see you as well. I hope that we can see each other in person sometime soon. Yes, that would be lovely. That would be great. I'm excited to be able to chat with you. Thanks so much for coming on the show.
00:01:42
Speaker
So your spot in the data visualization world is very interesting because you've had a long career of doing a lot of research and then you basically helped build Tableau into what it is today.
Jock's Journey with Tableau
00:01:55
Speaker
And so I thought we would talk about a number of different things. And I thought maybe we would talk real briefly about how you helped get Tableau started with helping Chris Stolte in his original dissertation. But then I wanna focus a lot on the work that you've done at Tableau and where you see
00:02:12
Speaker
not just Tableau going, but the date of his world going in the next three years. So maybe you just give folks like that quick history and then we can really dive into the work that you've been doing at Tableau for the last several years. Yeah. So I did my undergrad math, computer science at Berkeley graduate work after being a programmer for a while at Stanford.
00:02:36
Speaker
And then I went off and started to be a research scientist at the Xerox Palo Alto Research Center in Berkeley. And then Pat Hanrahan, who was one of the founders of Tableau, is an expert in computer graphics. And I got to meet him when I was at PARC. And he introduced me to Chris Stolte. And Chris ended up having me be on his dissertation committee.
00:03:03
Speaker
because I was already doing work in Visual Analytics. And that was my start for Tableau. And it turns out, Chris is very entrepreneurial. I ended up joining Tableau in 2004. And then, yeah, as you said, I had a really long career. I was employee six at Tableau, got the start.
00:03:27
Speaker
both the design team and an industrial research team in 2011. And I'm now a technical fellow, which means I'm an individual contributor, but no longer a manager. So I've had a very long arc at Tableau. So in over 17 years,
00:03:48
Speaker
Yeah so if like chris and pat are like the founders and the fathers of tableau you really like the grandfather of tableau like that. So chris literally used my PhD dissertation in his PhD dissertation so there is some truth to that.
00:04:08
Speaker
developed an algebra for automatic presentation. And Chris and Pat did a domain specific language called VisQL that also combined in the database part. That was sort of the golden spike that led to Tableau. Connecting visual analytics experiences to actual data, particularly valuable data and databases led to the success of the company.
00:04:35
Speaker
So
Building Teams at Tableau
00:04:36
Speaker
that's the background. So 2011, you start the design team and the user research teams. So can you talk about...
00:04:43
Speaker
I don't think we need to talk about the ins and outs of being a manager, but like, what were you looking for when you were building those teams? How did you have those teams work together and work separately? I mean, you know, Tableau sort of goal is to, is to help people get insights into their data and there's people facilitating that. So how, so maybe you just talk about like building those teams together, what you were looking for and how that all shook out, I guess.
00:05:08
Speaker
Sure, so the core focus from Chris and Pat, original innovation at Stanford was on to help helping people answer questions with data. That's the understand part to help people see and understand data. And it is very cognitively challenging to do that.
00:05:29
Speaker
first of all data has its own challenges and then you're trying to answer a question and that is extremely challenging. So it made sense in 2011 as Tableau grew to hire designers and user researchers that have an understanding of human cognition.
00:05:48
Speaker
And the first two people I hired, one was Jeff Petaras, designer, very good at this. And then John Kim as a user researcher, and he's now a designer. He likes to design.
00:06:04
Speaker
User research does formal studies of people using software, Tableau has a great user research team, but it was trying to support that cognition, that extremely difficult effort to answer questions with data. Now, working with data is a really, really broad thing and Tableau has grown up.
00:06:24
Speaker
a lot. And so answering questions is only one part of what Tableau now supports. It now supports the entire enterprises doing all the aspects of working with data. But that was the genesis of it. And so the start in 2011, in particular, the start was just to get professionals in to help us support people thinking with data. So can you describe maybe a couple of
00:06:55
Speaker
I mean, I think there's a number of different things that those teams are doing. There's the internal part of helping build the tool into what it is, into what people see when they open Tableau and they make things. And then there's also like a whole wing of folks that are doing research. And I'm sure there's internal research, but a lot of what I've seen is like external stuff that's coming out to move the field forward. So can you maybe talk about a couple of those projects on either the design or the user research side and how
00:07:23
Speaker
I guess that's a really broad question, but also, what's the balance like in terms of we're improving the tool and we're helping the community generally, the field movement? Tableau, with the dissertation at Stanford, had an academic tradition. In 2011, we also started an industrial research team. There's a difference between user research and industrial research.
00:07:48
Speaker
Now, I hired one person who did both really, really well. But industrial research is writing academic papers and academic conferences, also prototyping, doing prototyping work, which is hugely valuable. And literally, you can do user research studies on prototypes. So it made sense to start both of these teams at the same time.
00:08:13
Speaker
But the industrial research team is a small forward-facing thing that's different, whereas the design and the user research teams are in the nitty-gritty of building the actual software.
Improving User Experience at Tableau
00:08:29
Speaker
There are two different aspects. Which direction do you want me to go? Why don't we start with the design piece, and then we can go to the industrial research side?
00:08:43
Speaker
So there's a relentless effort to first, you know, with that existing shelf experience that is the core question answering part of Tableau, just making it easier. And I can't completely remember 2011, but there's two parts to it. There is.
00:08:59
Speaker
Sort of the data facing part, so connecting the data and whatnot. And then there's the human, you know, augmentation of human cognition part of it, which has a visual part to it, but also the experience part. And so once the design professionals showed up, there was a relentless effort to sort of continue doing that. And later in Tableau's history, of course, we added in additional products and we were continuing to do that.
00:09:28
Speaker
for people with more skills. And also, right now, we're also focusing on business professionals. In other words, people that certainly don't have a lot of time to play with data and may not have the data skills, data literacy to play with data. And so we're pushing out that way.
00:09:47
Speaker
Right. Okay, so then on the research side, let me count it for you this way. When I worked at CBO years ago, you know, sometimes there would be a request from members of Congress to say, hey, we want to know the answer to this particular question. And other times there was
00:10:05
Speaker
when we didn't have specific requests, I mean there were always specific requests, but some were shorter than others, there was what do we think as an agency members of Congress would want to learn about and what do they want to know about so they could do their jobs better. So I'm curious what that balance was like, you know, there are customer needs and the research and the cognition research about how to solve those problems and then there's sort of
00:10:28
Speaker
What a lot of what I've seen is, uh, well, maybe it's just on the writing, but it's like a little more general. It's like, here's a date of his problem that we are trying to solve. So I guess my, if I was to like hone in on this question, it's like, what does that day to day look like for those research teams?
Role of Industrial Research
00:10:44
Speaker
So the industrial research team is talented individuals who have expertise in various things like statistics, computer graphics, et cetera, et cetera, along those lines. And the team that I built up, I had a passion about making sure it was very customer-focused. So from a customer-focused point of view, you start with problems that customers would have. And for Tableau, of course, it was a very broad set of customers, lots of different industries and whatnot.
00:11:12
Speaker
Then the individual research scientists would have ideas about how to crack open a problem of one type or another. And there were two ways to do the research. One way was to actually build prototypes because it is industrial research and prototyping is a great way to do that. Sometimes prototypes can end up actually becoming part of the product offerings of the company.
00:11:39
Speaker
And then you can also write academic papers, which is, you know, getting academic papers through review committees is a way of honing the the ideas, the understanding of the ideas, the methodologies about that. And so this industrial research group,
00:11:55
Speaker
to this day is very very good at both of those prototyping and also using the academic review process to hone ideas but always with an eye towards ultimately building software that would help our customers.
00:12:12
Speaker
So you're sort of at that point during that 20 years or so where you're running founding and running those teams, it sounds like you're sort of looking forward, right? You're sort of anticipating customer challenges. And I mean, I'm sure you're answering certain challenges, but you're sort of anticipating questions and challenges. And so I'm curious now that we're in 2022, you have a slightly different role there, but now looking forward, what do you see? I mean, Tableau itself has gone over
00:12:39
Speaker
Sort of at the aggregate level tremendous changes, you know recently being you know Acquired by Salesforce and variety of other things. So I'm curious now as you look forward What do you what do you see both in terms of the tool itself in terms of the data vis community in terms of you know? I mean not the world because we'll never stop talking but you know Yeah, so what like now looking forward what do you see?
Salesforce Acquisition Excitement
00:13:03
Speaker
So the first thing to say is I'm actually super excited by the acquisition in 2019 by Salesforce because Salesforce has a really long history, really successful company focusing on specific business professionals, sellers, marketers, field service, people like that. And so Tableau right now,
00:13:30
Speaker
wants to grow to business professionals. And so this partnership with Salesforce is totally fabulous that way. So that's the first part. The second part is there are sort of two major vectors going forward. One of them was what Tableau was doing all the way back when Chris and Pat were at Stanford, which is the
00:13:55
Speaker
partnership between humans and computers. And the opportunity always, and certainly today, is that humans and computers have complementary but asymmetric skills with respect to data work. On the human side, I go either way. But I'm going to start with on the human side, we have a very rich understanding of the world. And in particular, individuals have a very rich understanding of their organizations. And
00:14:25
Speaker
Comparatively, though, computers have a much more superficial understanding of the world, but they're perfectly willing to dive into vast amounts of data 24-7, 365. Humans need to sleep. Humans find data, you know, a struggle, boring, on and on like that. So there's this huge, huge opportunity around
00:14:49
Speaker
That partnership and in particular right now, 2022, machine learning and artificial intelligence are slowly giving computers more and more capabilities of one type or another, technically. And so that's going to just enhance the partnership.
00:15:10
Speaker
thing is collaboration between people. So the world is a really rich, complicated place. And so you might know everything about your specific thing, your organization, but the world is really, really rich. So you do need to collaborate with other people. And because it's still times of pandemic, I use the example of who knew that we were going to need to have an understanding of COVID data
00:15:40
Speaker
In 2019, well, we didn't know, but we're now avidly reading experts on that kind of data and taking the data and combining it with our organizational data because it's super important to the organizations. And so that's a relentless trend. And so that is why collaboration is super important. Yeah.
AI and Machine Learning in Tableau
00:16:05
Speaker
So on the machine learning and artificial intelligence, like for an everyday Tableau user, can you give me a sense of how they might expect either one of those sort of things to show up in their creation or their use of the tool?
00:16:23
Speaker
So this is a place where the partnership with sales force is really good sales force did the negotiations to have einstein be a brand element about about all of this but the basic idea here is.
00:16:39
Speaker
workflow, you could get recommendations from Einstein about what to do next. Right. Now, you know, humans need to bring their judgment to bear. They know the world about whether they're actually going to follow the computer or not. But that's the idea is the computer can provide recommendations of actions of literally not just decisions, but actions to do next. And that will speed people in their work.
00:17:08
Speaker
So the reality of it, so just to keep the drill down one level farther, there are data professionals who are skilled enough to be able to do the machine learning and whatnot, but the delivery of the machine learning modules into workflow. And so you want to support the earthly and also monitor those models to make sure that they're still providing good recommendations to the individuals.
00:17:36
Speaker
that are doing it. That is a current concern. That's current work research. I'm obviously not going to get into any sort of forward product announcements, but that is what's going on.
00:17:55
Speaker
It's interesting to hear the way you sort of frame that because what pops into my head is like a show me tab, which we have now that's like for some select number of visualizations. But a show me tab that could be infinitely large, that's not just on give me examples of visualizations to create, but give me examples of other data analysis or other threads that I could sort of pull. I've built this thing to help me reach this decision with
00:18:24
Speaker
all these other pieces sort of circulating in and around Tableau, there are these other threads that I can pull. And so it's maybe not necessarily just show me a graph type I could use, but show me a data analysis or a data field that I could go explore or a new data field that I could go explore. Yes. In fact, the computer can easily monitor
00:18:44
Speaker
the data and notice things that draw human attention to them, which then might lead to questions that a person wants to answer.
00:18:57
Speaker
you know super relevant questions to their organization and you know like obviously a lot of business professionals don't have the time to actually answer those questions but they will delegate the answers you know to the to the analysts and their organizations or whatnot or
00:19:17
Speaker
Which is why, by the way, I think that Salesforce acquiring Slack is super exciting for Tableau because it's a natural place to do that kind of delegation or that kind of collaborative work with people. It's already established user experience for that. But yes, for me personally, I find the deeper questions to be the most interesting ones to try and support.
00:19:45
Speaker
how I ended up on Chris's dissertation committee, it's cognitively challenging to answer those questions. And by the way, the 17 years I've been at Tableau, those deeper questions are hugely valuable to our customers. Using data to get into good answer on deep questions
00:20:09
Speaker
brings huge customer value. And so it's super valuable that way. In addition to all the routine data work. Right, from the perspective of the tool,
00:20:22
Speaker
is is there uh well how i know i know there is but like how does the tool help lead people to those deeper questions right so it's really easy for me to pop in if i think about like hr data right um just because you know the sales force link is kind of kind of the obvious one you know i could pop in my hr data i can do some basic tabs but
Human-Computer Collaboration in Tableau
00:20:46
Speaker
How does a tool then encourage me or lead me to ask these deeper questions?
00:20:54
Speaker
Questions occur all the time to people. So it's not so much the computer leading to deeper questions. It's the computer helping a person quickly decide whether, first of all, getting answers to simple fact questions really quickly, and then deciding whether some question actually is worth deeper exploration or not.
00:21:18
Speaker
And because it's the human that has the knowledge of the world that has to be brought to bear rather than the computer. But the user experience needs to be designed so that you can quickly essentially do triage on the question and then spend your time focusing on the ones that have depth to them and might have high payoff to them.
00:21:42
Speaker
I never said the word triage before now but it is exactly it is exactly right it's like the software needs to be set up for that to support that kind of triage not just for the individual but also for the collaborative efforts right going on that right so that's my perspective and but the computer can certainly
00:22:04
Speaker
absolutely help with that because the marshalling up the data, the COVID example is a good example. The deeper questions often involve bringing together data
00:22:17
Speaker
Starting with some data and then bringing in more data right in completely unanticipated ways such that you know the data curators didn't you know they go after the routine data that's going to help the organization but deeper questions often lead to this kind of on the fly modeling that data modeling that has to happen.
00:22:36
Speaker
to get to the answer. So there's oftentimes I see in the data science, data viz community, this sort of argument more or less about whether there should be like, a person should be able to do everything, right? Sort of what I call the unicorn kind of person, or like, is it about teams? And I get the sense from you that it's really all about teams. And so, so can you talk a little bit about like,
00:23:05
Speaker
Yeah, from your perspective, like what's a way I'll make it a little more concrete, actually. So someone's listening to this podcast and they're the one person at their organization that's using Tableau and they love Tableau and they're doing analysis and they're trying to get more buy in at their organization.
00:23:22
Speaker
maybe for the tool itself, but maybe just more generally for like better data is better data communication. So for that person, they're trying to build these teams. Like, how do you see that person? Like, what's their path forward to build these teams and build this, you know, the sort of network of, of people who have this shared interest to ask it to ultimately ask these deeper questions.
00:23:43
Speaker
Yeah. So you know, what popped into my head was the Tableau conference and a boy do I wish we could have in person Tableau conferences again, because the brilliance out of Lisa Fink's brilliance about starting the Tableau conference was that the person that you just described would show up at the you know, like I was there at the very first Tableau conference, you know, 160
Importance of Tableau Conferences
00:24:09
Speaker
guests plus the Tableau people. But they were already sharing their knowledge and their best practices about how to get their organizations to use data more effectively. It's a very rich set of things. Tableaus
00:24:24
Speaker
specific responsibility is to build the software. And the good news is, of course, the visual part of Tableau means that you can make the data visible to people, you can tell really good stories about it. But there's a lot of rich, it's like, we don't have time in this podcast to go into all of the different ways. But what I'm fundamentally saying is, if you're one of those people,
00:24:50
Speaker
You know, when Tableau Conference starts to be in person again, come because you will find your fellow people in different industries, but the ideas are fungible, the techniques are fungible. And the thing is, is that companies are all at many different places in the sort of the sophistication with respect that they do the data, but you will find people that are
00:25:14
Speaker
given your organizations where it is right now. And literally some of the Tableau documents, like we have a thing called Tableau Blueprint, which is to help organizations do this sort of thing. And part of the there is a self-assessment of where you are, where your organization is right now, and what proven next steps are for moving forward. Right. So
Career Paths in Data Visualization
00:25:38
Speaker
So I want to just get back to one more question before we wrap up, which we could talk about this one I think for a while too. So you've been in academia, you've been in the private sector and you've built all these different teams with all these different skill sets and sort of built a culture of collaboration. And so I'm curious for younger folks who are interested in the data visualization field. And I was about to say computer science graduate students, but I think that's not
00:26:08
Speaker
like the right framework because it's really lots of different people come to Data Vis from lots of different places. So when folks are thinking about their
00:26:20
Speaker
careers? What is your thought about academia versus the private sector versus the nonprofit sector? And like, I mean, so that's a pretty like general question. But I think if people are, you know, thinking about where do they go once their education is done? What are your thoughts on, you know, what people should be thinking about? What are the questions they should be asking themselves as they start to look for careers and data and data, data vids?
00:26:45
Speaker
Yeah the space is really really big and you're absolutely correct it's not just computer science. People like for example my son.
00:26:55
Speaker
Oh, it's his birthday today. Happy birthday, Gavin. And of course, now I can't figure out what his age is. But he works currently at Amazon. And he started out with a degree in economics. Well, so for example, which of course, obviously very data oriented. But he did need to learn some computer stuff. And he also, of course, partners with with lots of other people.
00:27:20
Speaker
frankly, you can proceed forward. If you have the sort of the data oriented passions, you can proceed forward with academic profit or nonprofit companies. The data is affecting everywhere. And everyone understands that it's really important. And then the challenge is, are you more data oriented than you can learn, you can learn the data, the parts of it, or, or the, you know, the query parts of databases or whatnot.
00:27:49
Speaker
Or are you more interested in a specific domain? In which case, then that will drive you forward out of college. Because, you know, one of the challenges of people who are the unicorn data scientists, in other words, a person who is a unicorn and knows how to do, you know, the statistics, the machine learning, the queries on the database, you know, the programming and all those sorts of things. Unfortunately,
00:28:15
Speaker
that unicorn doesn't actually know about their organization. And they often have to team up with or spend a long time learning about a particular organization. So that's on the data end of the spectrum. And then part of the reason why the Tableau community is so rich and vibrant is because people never thought that they would really be able to deal with data, but really cared about
00:28:41
Speaker
their particular organization discovered that, well, if the software was easy enough to use, they could actually do stuff. And that's the passion on the other side of it. So yeah. Yeah. Yeah. Well, that's great. Um, I can see your passion for it and I can see the passion really for the collaboration, which I think is just so important and the way that we work and communicate data. Yeah. Yeah.
00:29:03
Speaker
Jock, thanks so much for coming on the show. It was a treat chatting with you and hopefully be able to chat in person soon. Absolutely. Looking forward to seeing you in person soon. Thanks. Bye.
00:29:16
Speaker
And thanks everyone for tuning into this week's episode of the show. I hope you enjoyed that. I hope you learned a little bit about the history of data visualization, the history of Tableau, where the company is now and where it's heading in the future. If you would like to support the show, please consider sharing it with your friends, your family, your networks. If you'd like to financially support the show, even just for like cost of a cup of coffee, you can go over to my PayPal page or you can go over to my Patreon page.
00:29:41
Speaker
Get some goodies over there. Your support helps me afford the transcription costs, the sound editing costs, the video editing costs, the web hosting, all the things that's necessary to bring the show to you each and every other week. So, until next time, this has been the Policy Viz Podcast. Thanks so much for listening.
00:30:01
Speaker
A number of people help bring you the policy of this podcast. Music is provided by the NRIs. Audio editing is provided by Ken Skaggs. Design and promotion is created with assistance from Sharon Satsuki-Ramirez. And each episode is transcribed by Jenny Transcription Services. If you'd like to help support the podcast, please share it and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcasts.
00:30:23
Speaker
The Policy Vis podcast is ad-free and supported by listeners. If you'd like to help support the show financially, please visit our PayPal page or our Patreon page at patreon.com slash policyvis.