Alter Everything

MaddieJ · ‎01-29-2019

We're joined by Dr. Nick Jewell for a chat about motivation, going with the flow, and how data is revolutionizing hockey.

Panelists

Brian Oblinger - @BrianO , LinkedIn, Twitter
Dr. Nick Jewell - @NickJ, LinkedIn, Twitter

Topics

Community Picks

Nick:

@chris_love

Safari Books Subscription

Brian

Alteryx Japanese Localized Community

Transcript

Episode Transcription

BRIAN: 00:13	[music] Welcome to Alter Everything, a podcast about data science analytics culture. I'm Brian Oblinger, and I'll be your host. We're joined by Dr. Nick Jewell for a chat about motivation, going with the flow, and how data is revolutionizing hockey. Let's get into it. All right. Dr. Nick Jewell, welcome to the show.
DR. NICK: 00:46	Thanks for having me, Brian.
BRIAN: 00:47	Well, I have to say, this is a bucket list item for me. When we started this show, you were on the list of just that silky smooth-- you're going to put me out of a job is what's going to happen because people are going to be immediately calling for you to now be the host.
DR. NICK: 01:01	Fantastic. It's going to be my career of the future; I can see this happening.
BRIAN: 01:04	That's right. Yes. That's right, in case this analytics thing doesn't work out for you. So speaking of that, why don't you tell us a little bit about you. Tell us about your origin story, as it were, for analytics and data.
DR. NICK: 01:14	Yeah, can do. So I basically started out in a sciences background. I was actually really lucky when I started out. I got to do a degree in two subjects that I really loved, so I got to do chemistry and computer science. And then rather than just bolting these two together, I actually found a degree where I got to apply them together. That integration was actually really important for me. I spent a year in industry. I got to work with the pharmaceutical giant Pfizer in probably the remotest bit of Britain. I think they call it Brexit Britain these days. And I got to work in their computational chemistry division, so that meant I got to work with X-ray crystallographers, the kind of people that actually work out the shape of molecules in drugs, quantum physicists, and practitioners of something that was called QSAR, or quantitative structure-activity relationships. Now, I'm not going to bore you with the details of that too much, but it really set me on the road towards data science, so using numbers, using analytics, statistics, and applying that to make a difference in science.
BRIAN: 02:10	Wow. Okay. So that's a lot to unpack there. So what was the one-- you were talking about QSAR. What's that all about?
DR. NICK: 02:16	Oh, QSAR. Yeah, so, quantitative structure-activity relationship. So, essentially, it's the idea that molecular properties, the structure of drug compounds, they're directly related to their biological effects. So when you have a certain chemical structure-- do you know what I mean, those little matchstick diagrams you drew in university?
BRIAN: 02:31	Sure. Yeah.
DR. NICK: 02:33	The position and the orientation of those compounds really affect the way that the biology of those drugs actually work. So the best example is, take the lock of a door. So you roughly know what the lock of a door looks like. You've got maybe a hundred, maybe a thousand different keys. All of these keys roughly fit the lock. None of them are perfect, but by overlaying all the keys together, you start to understand which bits of the keys actually fit the lock really well, helping you design a better key to fit the lock. And then the lock in this case would be a protein, some sort of active site that you're trying to target for. That might be for HIV, might even be for things like cancer. And obviously, the keys themselves are the drugs that the companies are trying to build.
BRIAN: 03:10	Wow. So where does the data for that come from? I mean, it occurs to me, just thinking through as you were talking, the sources of that data and what that might look like. It has to be pretty interesting, right?
DR. NICK: 03:21	Yeah. And it's both a mixture, I would say, of internal and external data sources. So, within the company, you're talking about internal sources like these X-ray crystallographic examples, big machines trying to work out the actual physical structure of either the lock or the key. But you've also got enormous databases - that you can buy if you work in the pharma industry - with hundreds of thousands, millions of different compounds. And the idea is you need to search through those databases to find the most effective drugs to then model into your lock and key environment.
BRIAN: 03:50	Wow. Okay. So you also had a number of other items in there, and it's one of the things we always like to talk about a little bit is you went through a lot of different things kind of rapidly in terms of the roles that you've had and the places you've worked. Talk to me a little bit about that and maybe what motivates you. And how has that driven those movements that you've made kind of along the progression there?
DR. NICK: 04:16	Yeah, sure. So after I finished my PhD-- and my PhD was really around using a technique called genetic algorithms to kind of take those keys and make them more floppy and try and work out exactly which was the best configuration, and sort of interesting properties in 3D space around these keys. It was fantastic, great to do this kind of academic work, but I really wanted to get into the commercial space. So I spent a few years consulting. I spent a number of years in a large financial services organization kind of doing every bit of the business, so working in analytics applied to retail, applied to investment banking, private banking, insurance, a really good way to get exposure to data across all of these different divisions. Been with Alteryx for three years, I'm into my third decade now as an analytics professional. Yes, I have gray hairs on my beard, but I still wake up in the morning; I get really excited about the mission that's making people win their day back with analytics. It's the idea that analytics can help you augment what you do. You can make better, data-driven decisions. You can do things faster, and you can interpret this deluge of data that we often see in our day-to-day lives.
BRIAN: 05:19	Well, yeah. I mean, I think that's a great point. And so in those decades, as you said, that you-- we won't say how many, but in those decades that you've been doing that, do you feel like we're accomplishing that mission? It seems like people are starting to understand this stuff more, but there's still quite a bit of challenge around it and education. Kind of where do you see that at right now?
DR. NICK: 05:39	Ah, I think there's never been a more exciting time to work in this space. We talk about the analytic pipeline. We talk about how analysts discover the data sources they work with. They go on to prepare, blend, enrich that data, and then they build that analysis. Now, that could be descriptive or that could be predictive in nature. But then the insights that come out of those analyses, they get distributed into the hands of decision makers. And there's innovation at every single step of that process. The industry just doesn't stand still. It's a fantastic time and a place to work in it.
BRIAN: 06:08	Yeah. So speaking of innovation, what's the biggest one you've seen over the years? Or maybe not the biggest, but what's the most exciting to you? What's the thing that really changed? What was that moment like?
DR. NICK: 06:20	Okay, probably two things here. So I think number one, open data, the explosion of data and the variety of data that's now available to you. So previously, you worked in an organization, the organization generated data exhaust. Some of it was kept; a lot of it was thrown away. We can now start to augment the analysis that we do by bringing in data sources from the World Bank or data.gov or Quandl if we're doing financial analysis, a huge opportunity to really enrich what we're doing.
BRIAN: 06:48	Yeah. So the data that you just talked about, it was kind of-- what did you say? Data exhaust?
DR. NICK: 06:52	Yes.
BRIAN: 06:53	I've also heard data rot. I've heard some of these terms. Do you think that-- first of all, what kind of data is that to you? And second of all, are companies going to regret letting some of that stuff go? Is it now valuable and they didn't know it in the past? Or is it truly just something that there's some percentage of all the data that's generated that isn't useful?
DR. NICK: 07:14	Well, I'm doing a lot of work at the moment in the healthcare sector. And healthcare is a particular problem when it comes to data exhaust. So they capture a number of critical elements. They put that into something called the electronic health record, or the EHR, but they throw away so much information. So when you go for a medical scan and you get an image taken, the doctor will make notes on that and that'll go into your record, but a lot of the other information gets thrown away. There's simply no way to store a lot of this unstructured data. And that's a real risk because, three or five years down the line, you might want to return to one of those images or to an audio transcription of an event and you just simply don't have it. So alongside the explosion in data, we've got the explosion in cloud, the idea that, actually, it's relatively cheap to store and then process this data later on. It's a fascinating sort of tandem of innovation.
BRIAN: 08:00	So what do we do about that? I mean, if we're throwing away some large amount of that data, is it literally just because of the size requirements that they don't want to store it? Is it part of the regulations? Why are we throwing away that data?
DR. NICK: 08:14	It's partly down to the structure, actually, Brian. So when data arrives from, say, a transactional system, that's generally quite easy to capture and you can stick it straight into a relational database. You can do that on Premise. You can do that in the cloud, same difference. When it comes to unstructured data like all of the notes-- even think about the podcast we're recording right now. We could transcribe those notes. They become slightly more structured, and then we can do more advanced machine learning, pull out the keywords we're talking about, gets closer and closer to being structured data. But in its essence, it's a podcast. It's an audio file. We have to have ways to store that and actually retrieve the information inside it. People are only now cottoning on to the idea that there's huge value in retaining this unstructured data source.
BRIAN: 08:53	Right. Okay. Well, we definitely won't throw this away. We're going to hang on to this and put this out there for the world. So given that we've talked about where you came from, kind of where we're at today, and what you're seeing the differences are, I'd love to kind of put you forward-thinking now. And let's talk a little bit about, where do you think things are going? What is your outlook on that? What kind of timeline are we talking about? I'm trying to just get you to get out your crystal ball here and as much as possible.
DR. NICK: 09:22	Of course, that's what we do in data centers, right? We have that crystal ball.
BRIAN: 09:24	It's a little bit of predicting happening, yeah.
DR. NICK: 09:26	No problem. I'll give you two themes; we'll take them one at a time. I'm going to start with automation. I think there's a massive change already underway, but I think there's a lot more that we're going to do. So you take a platform like Alteryx, it takes broken processes and tools like Excel and it helps analysts clean them up and allow them to run repeatedly-- or maybe reliably as part of a schedule. And I think we're doing a pretty good job, actually, in terms of changing the culture for analysts across many organizations, freeing them up, allowing them to do their next challenge. I mean, who wants to sit waiting for 15 minutes while a pivot table recalculates in Excel? And I see this all the time, right? But I think what you're going to find is automation's going to go a lot deeper in the years to come. So we're already seeing something called RPA, or robotic process automation. That's starting to become a major investment for many financial services companies. The idea that software, robots can replace pretty mundane tasks, maybe completing forms, looking up information across multiple systems and giving those answers back to a human analyst to make a decision when it's needed. And I think that's going to be a massive change in the years to come. To get RPA to actually work, you need to have some pretty good data-cleansing, data-preparation, integration technologies behind the scenes supporting that process. So we see lots and lots of customers already using Alteryx to take those first steps with RPA. It's really valuable.
DR. NICK: 10:46	I think, secondly, we talk about automation in the context of data science. Again, that's an idea that's just starting to sprout, just starting to really take hold. So you could take an automated machine learning platform, you could develop thousands of potential data sciences models, and then you can have your data scientist to actually go through, screen those and then tweak the very best of those models. Again, it's about making the most of that really valuable resource, that data scientist unicorn, and getting the most out of their daily work. I think we're going to really start to see this idea of a data science assistant really grabbing the attention of analysts in the years to come. I think that's something I'm particularly excited about, putting this in the hands of the analyst, the citizen data scientist, rather than just being the preserve of the pure data scientist.
BRIAN: 11:30	Yeah, it's interesting. We've talked a number of times on the show here, as you were talking about RPA and sort of the efficiency and the assistant, is that I think there's a general fear from people who haven't maybe dug into this as much as we have. They're worried about, is this going to replace me? Or, is this going to replace my people? And I think we're definitely of the camp or the opinion that no, it's actually going to allow you to do something greater. But when you're out talking to folks, how do you wrap that message to kind of say, "Hey, here's what the actual value of it's going to be, and it's going to be an enhancement rather than something that becomes a problem"? How do you talk about that?
DR. NICK: 12:08	Yeah. We should never be afraid of where technology is going to take us in our jobs. In the 1850s, there was a job description for the role of computer. That was a human job in the 1850s, right? We should not be worried about this at all. I think the application of AI, of advanced machine learning, is all about allowing people to have more time, to level up their own skills and to apply it to the next task. So it's nothing to be afraid of at all. It's about taking mundane work and giving it to the right automation machine to allow us to have more time to do where we have the best value, the human insight. Cool. I was going to say the second big trend that I see is probably around how we actually consume analytics. And it's an area that I'm quite interested in is where analysis, the results of the models that we build, I think they're going to become more universal, more accessible, but I also think they're going to become more invisible. And I brought along a little story that I want to tell you about how technology evolves. And I think we've got something that might be pure magic when we start out, maybe a completely new innovation, maybe a POC. It's really exciting. We don't quite know what to do with it. We move it into a product. Eventually, it might turn into what we call a utility.
DR. NICK: 13:15	Take electricity as an example, right? So electricity, the origins, were probably from about the 3rd century AD. Over in Iraq, there was a thing called the Parthian battery. They had no idea what to do with it, apart from maybe electroplating jewelry, okay? So it created an electric charge; that was it. It was magic to these people. Fast forward to what, the 1820s, and we started to see the first electrical generators, 1860, Siemens, Westinghouse. You got a building-sort-of-sized generator producing everything that a factory needed. Fast forward to the 1920s, the national grid. You plug something into a wall; you know exactly what it's going to be. Electricity becomes boring. It's a utility. It's really invisible now, but we can start to build more things off it, 20 years later, computers, 30, 40 years later, the internet. It's really interesting to see the evolution of these different products and how they become invisible in our lives. But we can build higher value on top of it. We see the same with analytics. You take an on-premise data center full of computers. You send it to the cloud; it becomes EC2. You take hard drives; that ultimately becomes something expandable like S3 in the cloud. You even take functions that we build now; we can call that Lambda. And at every point in this journey, we're moving from a product - something we know and touch and buy - into a utility. And we can start building really cool analytics on top of these utilities. It's really exciting.
BRIAN: 14:32	Yeah. No, that's a great story. I'm reminded too of a-- I often tell this story kind of in terms of how far we've come in a short period of time and what the utility is, like you were saying. When I was a kid, we used to go on road trips all the time. And of course, back then, it was paper maps and you had to really pay attention to where you were at and where you're on the road and where you need to make that turn and all that. And I remember that, one year-- I'm going to guess this was in the early '90s maybe, early-to-mid '90s. They came out with this device and it looked like a calculator, kind of that size roughly. And what you would do is you would type in-- it was basically a little database in your palm. So they had preloaded in all of the attractions or points of interest and so you had to punch in, "We're northbound on whatever highway and we're at mile marker 10," and what it would do is spit back at you, "Hey, McDonald's is coming back," or, "Some landmark is coming up." And we just thought--
DR. NICK: 15:31	Brian, you were living in the future.
BRIAN: 15:32	I know. We thought this was the most incredible-- I remember when we got this thing and I think we played with it until the batteries died on a road trip to Pennsylvania or something. And that was just what, not even 20, 25 years ago. And now looking at Google Maps, to your point about it becoming transparent and it just kind of happens, now you get in the car and you talk to it and you say, "Hey, I want to go here," and it does all that stuff. And the speed at which these things are moving, like you said, because of the cloud is pretty insane.
DR. NICK: 16:03	Exactly. I mean, even traveling to Denver, where we are now, I'm getting in a cab. It's got [Satin?] Ave on the screen with live traffic telemetry. This is all military tech from 30, 40 years ago that is now just completely available. You turn the tap; it's like water. It's incredible.
BRIAN: 16:17	Right. So one of the things you mentioned, I've heard this term used quite a bit and I'd love for you to just dig into it if you could. Lambda, it's hot right now. A lot of people are talking about it, but I don't know that there's a general understanding of what it is yet, so maybe a little bit of your perspective on that.
DR. NICK: 16:34	Oh, for sure. So there is a war coming, Brian, a war in the world of technology infrastructure, okay? And it applies directly to the world of analytics. So camp number one in this war is all about containers. So they use technologies like Docker, which some of our listeners might've heard of, and it's the idea that, pretty much, I can spin up a completely self-contained environment with everything that my application needs to run. So that might be data; it might be the functionality of an application. I press a button-- or I type into a terminal, "Docker up," and up comes my complete image with everything that I need. Very, very elegant solution, a great way to scale applications, particularly in the cloud. The alternative approach is to say, "Right, well, as a developer, I'm writing analytic functions and I don't exactly know how they're going to be used, but I need to deploy those functions as part of a bigger application." So Lambda, it's basically the brand term for Amazon's Serverless. The idea is that I can deploy this function into the cloud and I can call it whenever I need to as part of an application using all of Amazon's cloud infrastructure. Very, very efficient, but I get an exact price every time I call that function. So very quickly, as maybe a chief architect or a chief technology officer in a company, I can work out exactly how much my application is costing today, what it might cost in the future. And it's really easy to scale this serverless function across multiple data centers globally if I need to. So two different ways of looking at developing maybe analytics of the future: I build a big box and I can deploy that through Docker, which is great, or I can develop analytic functions and deploy them in the cloud as well. And Amazon do it, Microsoft do it in their Azure environment, and I'm sure Google have an equivalent as well. So it's very much sort of competing technologies at the moment. It comes down to the level of control you need over that individual function.
BRIAN: 18:18	So if you were again to get your crystal ball out and look into the future, in the war of those two things, which one outlasts? Or do they both stick around?
DR. NICK: 18:28	It's really interesting. So I follow really closely a guy on Twitter called Simon Wardley. And I strongly recommend, if you're interested in sort of the future of technology and these kinds of patents, have a look at what he does on Twitter. Have a look at his YouTube videos. He forecasts 10, 20, 30 years across a number of different topics. And he spots the point where war happens, maybe where peace breaks out, and then when the next level of war happens, but also which countries are involved in this innovation as well. So forecasting where China might be in 10 years' time versus the US, really interesting stuff. He is strongly of the opinion that Serverless is going to win this one out purely because of the flexibility and the simplicity, the elegance of the solution. Docker is great for today, but it's not where we're going to be in 5 to 10.
BRIAN: 19:09	Got it. Okay. Good. So let's move on to-- I know one of the other things we wanted to talk about is, and you've already mentioned it a couple of different times in a couple of different ways, is humans are evolving as well with the technology, and one of the things that we hear consistently is people tell us all the time how much they love what they do, right? And they talk about how they got their life back, and their job is so much fun, and they love the ability to problem solve that they maybe couldn't do before because of technology or construct of their job or whatever. So when you hear people talk about that, sometimes we hear the word addiction used, which--
DR. NICK: 19:51	It has connotations, right?
BRIAN: 19:52	Yeah. It has a little bit of baggage there, but I understand what they're trying to say. They're trying to communicate their excitement by saying, "Oh, man, I'm addicted to it. This is amazing. I love doing it." What have you seen around that? Why in your perspective are people so excited, and maybe what's the genesis of that? But also, kind of what's to come with that, getting people excited about data and analytics?
DR. NICK: 20:17	Sure. So, last year, we had our customer conference, the Inspire conference at the Anaheim Convention Center, the ACC. So, fantastic event, the opening key note fronted by The Doors and I think a very familiar lead singer. You remember that, Brian?
BRIAN: 20:28	Yeah, I think I've seen that guy before.
DR. NICK: 20:30	Yeah. And it was closed out by Jane McGonigal, who gave this fantastic talk around how games can change the world. And Jane's done some amazing research using games to live better, to overcome personal challenges. And I think actually in her case, she had a physical head injury that caused her real societal issues, and she built a game system to push her towards recovery. So it's really interesting stuff. As part of her talk, she mentioned two things that really resonated with me. First of all, she said she did some MRI scans of gamers and it revealed huge flashes of brain activity around the pleasure centers of the brain during the playing of games, not unexpected, right? But researchers also found out that there were similar patterns of activity for people watching people playing games. Now, this says two things to me. So first of all, this explains the success of esports--
BRIAN: 21:18	Twitch.
DR. NICK: 21:18	Twitch. Exactly. And secondly, the Alteryx Grand Prix, we've got 3,000 attendees screaming like crazy as three contestants build out workflows on a stage. We're all sharing in that collective thrill and that's just magic to me. Secondly, Jane talks in her books about attaining this thing called a state of flow. Now, this magical brain mode, this is where time slows down. It's like the Matrix, right, bullet time. You don't notice distractions around you; you're really in that zone. And this is a big deal to me. So I find that Alteryx gives me that sense of flow; it helps me solve problems. And I think other people get this as well. So that's my real takeaway from everything that Jane sort of said during her talk. I talk to my colleague Shawn in the UK. We're always joking. We're nearly always on WhatsApp at about 11 o'clock at night, plugging away on a workflow, something like that, not just for work, but we genuinely got ourselves into that state of flow. We can't stop. We're addicted to this, solving this problem. It's fantastic.
BRIAN: 22:14	So give me some examples if you can. What are some of the latest flows you've been on? What's exciting that you're building?
DR. NICK: 22:20	So, deep down, I'm an analyst at heart. So I sit here and I really follow ice hockey in the UK. And I was sitting there going, a lot of these players have played with each other on different teams in the past, and I thought, "Yeah, that sounds about right, but can I actually prove it?" So here I go, opening up Alteryx Designer, connecting up to APIs that are available with all of this player history, using the network analysis chart to work out who is the most connected player. And there I am. It's three hours later. I've ignored my children; they've all gone to bed without a story. But now I know who is the most connected player. It's fantastic.
BRIAN: 22:52	Wonderful. It's so funny you mention that. I just saw a story - this was two or three days ago - about the National Hockey League here in the States, and they have converted over all of their things into iPad apps for the coaches. And so they were saying that, up until recently, all of the coaches, when the period is about one minute to go in the period, the guys who are back in the video room and the analysts are mad rushing to a printer to print out the stat sheets of time on ice and how many shots these players take and all these things. And then they hand it to the coach, these hard copies, during the intermission right at the beginning, so that the coach can kind of look at it and kind of review, hey, this guy's getting too much ice time; he's not as effective as the other guy, this kind of stuff. And then the coach would go in and give sort of a pep talk in the middle of the period to say, "Hey, here is what we need to do, guys," with the--
DR. NICK: 23:45	So leafing through this enormous piece of information.
BRIAN: 23:46	Yeah. He's going through this binder of information and then they go out and they play another period. And so what the National Hockey League has done is digitize all of that and delivered it on an iPad app specifically for coaches, that they can see that not just at the end of the period but in real time. So as they're on the bench, they're able to look at it and understand analytically what their players are doing and who's more likely to score and these kinds of things. Really fascinating article that I'll make sure we link in the show notes.
DR. NICK: 24:15	And that's total digital transformation when we talk about it. I see so many customers still relying on this sort of paper and pen approach to many of the things they think of as critical, and digital transformation should sweep most of that away. We talk about things like the internet of things. When it comes to the NHL, there's one particular API that I've been playing with. And all of the players in their jerseys have little RFID chips, so radio frequency ID chips. So when they're on and off the ice, this thing is bleeping, effectively, back to the data center to say, "Right, Matt Sundin was on the ice for 12 minutes. He came off again," that kind of thing. It's really fascinating to know that data is now at their fingertips.
BRIAN: 24:47	Yeah. And I'll have to find this and put it in the show notes, but it was a video from that exact thing that you were talking about, where it was a wide shot of the arena, of the whole playing surface. And they were showing in real time as the players skated around, the actual tracking that was happening and the highlighting and sort of the data overlay over the video of the RFID tracking, which was pretty cool.
DR. NICK: 25:09	Fantastic. So I spend so much time on Twitter both talking about analytics but also blogging about hockey and then arguing with people. You're not data-driven enough half the time. Players do have heart and they do have guts, but I need to quantify that with [inaudible]. I want to know why somebody is better and that's what's fascinating to me.
BRIAN: 25:25	I think the quip that I made on Twitter was-- I retweeted this article and the quip that I made is I bet the players are looking forward to also a companion app where they can see the effectiveness of the coach in real time, of the line changes.
DR. NICK: 25:36	Oh, for sure.
BRIAN: 25:38	Seems like maybe that's a little one-way at this point. But okay. Great. That's really cool. Any other interesting flows you've been on, as you say?
DR. NICK: 25:46	Ah, well, talk about some of the things that I've done maybe with events and with customers. So they invited me to go and speak at a data innovation summit over in Sweden and I was on stage talking about the Alteryx platform. That was excellent. I also got a chance to speak at a booth where we had quite a few people come up and I thought, "What can I do? What can speak to this Swedish audience?" And again, it's ice hockey. So I'm thinking, "They kind of like this stuff."
BRIAN: 26:07	Yeah, they'll be good.
DR. NICK: 26:08	Yeah, exactly. So again I grabbed a whole bunch of statistics from the Elite Prospects website, a really good resource for that, for the Swedish Elite League. I pulled down all of the players for the last 10 years in all the different teams and I built a few different analyses. I built, first of all, something I found out from baseball, which is a technique called Pythagorean expectation, which I'm sure all our listeners will know all about. But it's basically working out how lucky a team is based on the number of goals they've scored, the number of goals they've conceded. And it turns out you can model this really, really well. Now, if a team under or overperforms, they could be considered lucky. And over this 10-year period for the Swedish teams, we identified two or three really, really lucky teams, kind of interesting stuff. Now, remember, I'm doing this booth with about 50 other vendors and I'm just shouting to all the people and I'm getting a bigger and bigger crowd. We did a separate analysis about how a player performs over age. So take all the players at age 26, how do they produce, 28, 29, 30, and so on. And obviously, there's kind of a bell curve going on for forwards on the ice where, about 27, they're at their peak. With goaltenders, it's about 31, 32; it's a little bit later. And while I'm doing this, the crowd is getting closer and closer and I'm shouting and shouting, and one guy sticks an iPhone in my face and starts filming all of this. I'm like, "Okay, I'm glad you're enjoying it." At the end, he comes up to me and I said, "Wow, you look like you really enjoyed this talk," and he said, "Ah yeah, but you were talking about one of my favorite players. I grew up with him." And it's a really small world when I mention one player that plays for Guildford who comes from Sweden. And his friend from university is standing right in front of me. So it's a really small world in analytics. I love it.
BRIAN: 27:41	Yeah. And especially within the sports world, I think, going back to the question I was asking you a little earlier in the show about should we be afraid of this. In a sports world, I think some players should be afraid of it if they've been sort of cruising along for a while without anybody paying attention and then the analytics come and it's like, "Wow, they're not as effective as we thought." But the counterargument to that is you're also getting information about maybe players who in the past would not have made it to the higher echelons of these leagues and sports in general, because it's oh, they don't have what it takes and whatever, but then the analytics tells a different story. And so it giveth and it taketh away.
DR. NICK: 28:18	Well, let's flip it as well. So we can be overwhelmed or we can be driven too much by these metrics, but take it as a way to develop yourself better. You've got a metric; it's a metric that company cares deeply about. So maybe as a professional sportsperson, you need to score more goals or be on more assists or whatever it might be. That can be your goal, is help set targets for the future. I think Jane McGonigal talked about this quite a lot. By having these simple targets laid out in front of you and being able to measure and actually quantify the value of them, that's a really powerful motivator for a lot of people. So don't be afraid about losing a job. Think about making yourself better as a result of having better access to insights.
BRIAN: 28:53	Right. Yep. All right. Anything else you wanted to get out for our listeners here before we go to community picks?
DR. NICK: 29:00	Oh, sure. I have one more kind of interesting little story. So I do some work with an internal program we have at Alteryx called Alteryx for Good. So it allows us as associates to go off and spend so many hours every year dedicated to whatever we want to do with nonprofits or any organization that we feel strongly about. And I do a lot of work with DataKind in the UK. Now, DataKind is a global charity, does work with data scientists and with analysts. It's all about helping out nonprofits, government organizations, charities to make best use of their data. They collect great data. They talk to individuals that have real problems in society, but they don't always apply that data in the best way. So getting a bunch of data scientists together and working on a real problem can actually really move the needle. It's really exciting stuff. So I worked on a project with the Ecological Land Cooperation in the UK, a tiny, tiny little charity. And basically, they are trying to increase the number of smallholder farmers, so basically encouraging people to produce high-quality, small-scale agriculture, kind of a big deal compared to the megafarms that we often see these days. And we did a great piece of analysis in Alteryx.
DR. NICK: 30:04	So first of all, we scraped some websites to take all the pieces of land that were for sale in England and Wales. We did some machine learning to classify whether they really were agricultural land or whether they were just a big country pile for some big city banker to go and buy. Kind of the features we saw in the data, it's like, does it say close to a marina or commuting to London, as opposed to Bali or some other kind of crop. So a really nice bit of classification up front. We took all of those details; we took every single package of land in southern England by using a government open data source - so every single spacial shape representing every sort of land and back garden in all of England - and we mapped the two together. We applied some constraints that the charity had - so they wanted to buy land between 10 and 30 acres - and we could immediately tell them which pieces of land were for sale that met their quality expectations. Previously, they were literally cycling down country lanes, looking over a hedge and saying, "That looks really nice. I wonder if it's for sale." Can you imagine how broken that process was? But they didn't know anything better. We then replaced it with a land explorer. So literally, we could give them a targeted list of 20 pieces of land for sale; they could go and look at those in more detail. That's where the automation comes back in. It's fascinating. But for them, it was like magic. This was honestly such a transformational change using a little spring-cleaning of data sites.
BRIAN: 31:22	That's wonderful. That's a great story. Okay. So community picks, tell us what's been exciting to you out there in the world that you think we should point people to.
DR. NICK: 31:30	Cool. Well, I'll start off in-house. We'll start with the Alteryx community. I can't say strongly enough, we've just hit 2019; get going with those weekly challenges, people. This is so important. If you're not practicing Alteryx and you're not being exposed to all of these different tools and techniques that the community comes up with, you are really missing out. So that's my number one pick, kind of simple stuff, right? I would say, secondly, a huge shoutout to Chris Love, who produces some fantastic content. In fact, it was his YouTube videos on Alteryx three or four years ago that actually encouraged me to join the company, so a really, really big deal. He has a blog on medium.com called Data Beats that he works on with a couple of colleagues where he uses Alteryx alongside Tableau and other techniques to really make sense of some interesting data sets. So if you want to see a real master at work, go and visit Chris Love's Data Beats. I'll finish off by saying, if you want to know where analytics is heading, I would strongly recommend to everybody, invest in a subscription in companies like Safari Books. So we have this available internally. It's a monthly subscription, gives you access to tens of thousands of different technology books, self-improvement books, that kind of thing. But it's worth the price alone, just to get access to videos like the AI Conference, the Strata Data Conference. And you can download all of these on your iPad. Next time you're taking a trip, just watch a 30-minute video and see where the world is going. I've learned so much about healthcare, blockchain, AI, all of the intersections of the things I really care about, just through watching these conference videos; it is like you're there. So, strongly recommend that.
BRIAN: 32:59	Wow. That's great. I'll have to check that out. That sounds really awesome. And I will just give a plug for my community picks, again, kind of in-house here, just to let people know if you don't know. We have set about on a journey to localize our community. And believe it or not, when I look at the analytics for this podcast, of course a good percentage of it is in English-speaking countries, but you'd be surprised; there's a good percentage of it that's around the world in different locales. And so what we are attempting to do is make sure that we have Community available for everybody who wants it. Now, that's a very long road, but we've started out with French. We have German and we just launched Japanese as well.
DR. NICK: 33:41	Oh, arigato, Oblinger-san.
BRIAN: 33:43	Thank you. So definitely go check that out on the Community. If you click the little globe icon on the top right-hand corner, that'll get you into the language [selecsher?]-- selector, I should say, and then you can check that out. And so we're growing those. It's going to be great. We're going to be adding more of those along the way, and I'm so excited to bring the Community to more people around the world in their native languages.
DR. NICK: 34:04	Fantastic. And I was out in Singapore and Hong Kong just before Christmas, fantastic community that's being built over there. They are so passionate about what we're doing and passionate about analytics in general. So I'm glad we can start to really tap into those areas.
BRIAN: 34:17	Excellent. All right. Well, thank you for being on. This has been wonderful. I learned a lot. I have a bunch of research and watching of conference videos to do, so I'm going to go away and do that. Any parting words? Where do we find you? Where do we find more Nick Jewell? We go on Twitter?
DR. NICK: 34:32	You can go on Twitter. If you can stand the live-blogging of the hockey, you can find me at Nick Jewell on Twitter. I do talk about analytics, and I do retweet quite a lot as well from people that I find inspirational in the analytics space. You can also find me on the Community. So I will be there answering questions. I also occasionally write some blog posts around the product on the platform. So come and check me out on Community too.
BRIAN: 34:51	Excellent. Thanks so much.
DR. NICK: 34:52	Thanks, Brian. [music]
BRIAN: 35:02	Thanks for listening to Alter Everything. Go to community.alteryx.com/podcast for show notes, information about our guest, episodes and more. If you've got feedback, tweet us using the #altereverything, or drop us an email at podcast@alteryx.com. Catch you next time. Nick, so I'm really excited actually to hear about your roots in hockey. You may not know this, but I grew up in kind of not rural, but in Indiana where it's cold all the time and the ponds freeze over. And you have--
DR. NICK: 35:48	You get to play hockey like it was meant to be played.
BRIAN: 35:50	You have no choice but to play hockey. It's not a choice that you get to make. It's just who you are as a person growing up. And so I loved earlier on in the show you were talking about hockey. So tell me a little bit about you and hockey. How did you get into it? What's your favorite team? Let's dig into the hockey a bit here.
DR. NICK: 36:06	Sure. Well, it's always one of those things. When you're in the UK, you kind of grow up and when people cut you, you either bleed football or rugby, I guess. It's one of those two things. And they're okay; they don't appeal to me in the same way.
BRIAN: 36:17	We're going to anger all of our listeners, "They're okay."
DR. NICK: 36:19	No, they deserve it. It's a boring, boring sport. But what I found with hockey, I went to do my PhD at Sheffield University, okay, sort of like the middle-to-the-north of England. And again, they've got a couple of really famous football teams. But they had a really good ice hockey team called the Sheffield Steelers, and they played in the Elite League in Britain, which is kind of like the upper tier, probably equivalent to like the seventh tier in the US, but you get the idea. And I loved them. They were great, really really passionate bunch of individuals. When I came back down south to live - so I lived just outside of London - I started following the Guildford Flames. So I went to university in Guildford for my first degree. So it's basically wherever I went to do a degree, I can support that team. That's my rule. And Guildford ended up acquiring quite a lot of the NHL players, wittily, during the lockout. I think it was in 2004?
BRIAN: 37:04	Right. Right.
DR. NICK: 37:05	So we had the Calgary Flames goalie playing in this tiny provincial league because I think he just wanted to come over and see the UK for a bit. Crazy quality, it's just grown from there. I got a season ticket. You get to meet and interact with the players a lot, so you really sort of bond with them as humans rather than just statistical objects. And since the advent of Twitter, it just felt like a natural thing like get on the-- lots of people tweet the goals and the penalties and so forth. I try and add color or opinion, which is kind of completely non-analytical but very subjective, gets into lot of arguments. It's great fun. Just to try and be descriptive about what we do, so even this week over in Denver, I went to a University of Denver game just yesterday. It's been great. I love hockey and I'll watch it wherever is comes.
BRIAN: 37:43	That's awesome. Yeah, so like I said, I grew up in Indiana, in Fort Wayne, Indiana. For our listeners out there, I think I talked about this on the episode with Patrick Digan. But they have a minor-league hockey team there that they've had forever. It's like a 80--
DR. NICK: 37:54	Is that the Komets?
BRIAN: 37:55	It's the Komets. It's like an 80 year, 90-year-old hockey team or something. And yeah, I grew up going to games and just being around it. And I have to say-- and I've also lived in cities with NHL teams. I have to say, I think I like minor-league hockey better because they're more hungry; they want it more. There's more of a-- you can see the drive in the players, that they want it bad. And they're trying so hard to get to that next level, and I think that adds a little bit to it. And then of course, you also have sort of the general shenanigans that happen in minor-league hockey that would never happen on a nationally-televised game or something, the mascots, the shows during the intermissions. I mean, just the--
DR. NICK: 38:40	Well, I'll tell you what. My college experience just last night really put that down. So it's like it wouldn't be college sports in America unless somebody was inside a sousaphone, you know?
BRIAN: 38:47	Yeah. Right. So who's your favorite team these days, or who's your all-time favorite team or favorite player?
DR. NICK: 38:55	So I think we have to look to Toronto; we have to look to the Maple Leafs. That's--
BRIAN: 38:58	You're a Toronto man. Okay.
DR. NICK: 38:58	That's my NHL team. I've got family over there. I think Britain generally has massive affinity for Toronto. I think a lot of people immigrated there about 100 years ago. So Toronto is where it's at for me.
BRIAN: 39:08	Well, they're an Original Six too, so you've got that.
DR. NICK: 39:09	Exactly. They're storied, but they were rotten for a number of years, right? They just could not perform. It's been 50-plus years since they won, right?
BRIAN: 39:18	I heard you name-drop Matt Sundin earlier and I knew that you were a real fan. So, yeah.
DR. NICK: 39:22	Yeah, the almost-glory days of the late '90s, yeah. Absolutely. Now I'm showing my age, Brian. Thanks, you've made me reveal it. Yeah, I think what happened just the last couple of years is, when they've had that complete organizational shift, they've gone with Babcock as their coach, who's been hugely successful for Canada and also for Detroit--
BRIAN: 39:38	Detroit.
DR. NICK: 39:38	--as well. I mean, it's their time; it's coming up. They had the number one draft pick. They managed to lure Tavares out of New York. And that brilliant photo of him aged 7 or 8 sleeping under Maple Leaf duvet with Star Wars Phantom Menace toys in the background. It's like, these people will want to come home when it's safe, was the quote. It's now safe, doors are open. We're going to win it soon.
BRIAN: 39:59	Nice. All right. Well, we'll stay tuned for that.
DR. NICK: 40:01	Thanks man.
BRIAN: 40:02	I will say that-- so growing up, like I said, minor-league hockey. It's interesting because by geography, I should've been a Black Hawks fan, but for whatever reason we always gravitated towards the Pittsburgh Penguins. And this was back when, Mario Lemieux and Jaromir Jagr and Sergei Fedorov and I could go on for days.
DR. NICK: 40:23	And Jagr is still playing.
BRIAN: 40:25	Somehow. I don't--
DR. NICK: 40:27	Age 74.
BRIAN: 40:28	I don't understand it. But someone must be looking at analytics and giving the guy a shot. I don't know. But so I'm going to put super Mario down as my all-time favorite, of course Crosby now, just because I still follow the Penguins. But yeah, I love hockey. It's wonderful. It's interesting to see it spread kind of more across the globe. I know that the NHL is really interested in playing games in London and other places.
DR. NICK: 40:51	Absolutely. Well, a big thing from the UK is that team GB has literally just qualified for the top tier in the World Championships. So I'm going in May this year and we're going to get trashed. Literally the first game's against Canada, so let's put this into perspective.
BRIAN: 41:04	Oh, that's not right. Come on.
DR. NICK: 41:05	But we made it. We made it back to the top tier. It's been 30 years since we've been there, so it's really, really exciting.
BRIAN: 41:10	That is great.
DR. NICK: 41:10	I want to touch on one point there. We talked about Jane McGonigal. We talked about flow around Alteryx. If I had to explain ice hockey to somebody that had never seen it before, there is a moment, I think we all know it when we watch games enough, and when you've had a little bit of practice, you can follow the puck around. That's the number one difficulty for new fans. When you can spot the puck, when an attacker takes that move and does a little deke with the puck, everything slows down. Everybody can see that, and it seems to last for like minutes and minutes. And then they score, and it's just beautiful. And then time comes back to normality.
BRIAN: 41:38	Hold your breath for a few seconds during the epic move, yeah.
DR. NICK: 41:41	It's like the home-run swing and everybody goes, [inaudible], and there it is. It's flying off. And you've been there and you've witnessed that moment. And with hockey, it's even more beautiful. For that control, you're skating and you're controlling a stick and a puck. I wish I could do that.
BRIAN: 41:55	I always used to hear, I think it was John Madden, of course famed NFL guy here in the States, he used to talk about hockey and how he didn't understand it because he was so accustomed to the NFL way of announced and-- substitutions is what he was talking about, how do you substitute a player on the NFL football field here. And he was saying when he went to a hockey game and there were just guys that are jumping over the boards and whatever.
DR. NICK: 42:18	He couldn't understand how they-- oh, chaos.
BRIAN: 42:19	And he's like, "Wait, so they don't have to wait for a stoppage in play?" or, "They don't have to announce who's coming on?" or, "They don't have to swap out a guy for another guy?" And it is a very fast-paced game, especially these days in the NHL when they get into overtime and they do 3-on-3 and it's just back and forth and it's-- I wouldn't say that I regularly root for overtime, but when it comes about, you get that feeling of like, oh, this is going to be good, because that 3-on-3 is amazing.
DR. NICK: 42:47	Exactly. And think about what Moneyball did for baseball, right? So it totally made us focus on different ways of looking at the data and it said, "Right, don't go for the home-run guy. Go for the guy that hits singles reliably." With 3-on-3 hockey-- and I scream this on Twitter all the time. When it gets to the overtime, hold on to the puck. When it's 3-on-3, you have all that space--
BRIAN: 43:03	It's possession.
DR. NICK: 43:04	They have to commit two to come and get you and you've got the time. So if you can hold onto it, you're going to win. That's my two cents on that one.
BRIAN: 43:11	Nice.

BRIAN: 00:13 [music] Welcome to Alter Everything, a podcast about data science analytics culture. I'm Brian Oblinger, and I'll be your host. We're joined by Dr. Nick Jewell for a chat about motivation, going with the flow, and how data is revolutionizing hockey. Let's get into it. All right. Dr. Nick Jewell, welcome to the show. DR. NICK: 00:46 Thanks for having me, Brian. BRIAN: 00:47 Well, I have to say, this is a bucket list item for me. When we started this show, you were on the list of just that silky smooth-- you're going to put me out of a job is what's going to happen because people are going to be immediately calling for you to now be the host. DR. NICK: 01:01 Fantastic. It's going to be my career of the future; I can see this happening. BRIAN: 01:04 That's right. Yes. That's right, in case this analytics thing doesn't work out for you. So speaking of that, why don't you tell us a little bit about you. Tell us about your origin story, as it were, for analytics and data. DR. NICK: 01:14 Yeah, can do. So I basically started out in a sciences background. I was actually really lucky when I started out. I got to do a degree in two subjects that I really loved, so I got to do chemistry and computer science. And then rather than just bolting these two together, I actually found a degree where I got to apply them together. That integration was actually really important for me. I spent a year in industry. I got to work with the pharmaceutical giant Pfizer in probably the remotest bit of Britain. I think they call it Brexit Britain these days. And I got to work in their computational chemistry division, so that meant I got to work with X-ray crystallographers, the kind of people that actually work out the shape of molecules in drugs, quantum physicists, and practitioners of something that was called QSAR, or quantitative structure-activity relationships. Now, I'm not going to bore you with the details of that too much, but it really set me on the road towards data science, so using numbers, using analytics, statistics, and applying that to make a difference in science. BRIAN: 02:10 Wow. Okay. So that's a lot to unpack there. So what was the one-- you were talking about QSAR. What's that all about? DR. NICK: 02:16 Oh, QSAR. Yeah, so, quantitative structure-activity relationship. So, essentially, it's the idea that molecular properties, the structure of drug compounds, they're directly related to their biological effects. So when you have a certain chemical structure-- do you know what I mean, those little matchstick diagrams you drew in university? BRIAN: 02:31 Sure. Yeah. DR. NICK: 02:33 The position and the orientation of those compounds really affect the way that the biology of those drugs actually work. So the best example is, take the lock of a door. So you roughly know what the lock of a door looks like. You've got maybe a hundred, maybe a thousand different keys. All of these keys roughly fit the lock. None of them are perfect, but by overlaying all the keys together, you start to understand which bits of the keys actually fit the lock really well, helping you design a better key to fit the lock. And then the lock in this case would be a protein, some sort of active site that you're trying to target for. That might be for HIV, might even be for things like cancer. And obviously, the keys themselves are the drugs that the companies are trying to build. BRIAN: 03:10 Wow. So where does the data for that come from? I mean, it occurs to me, just thinking through as you were talking, the sources of that data and what that might look like. It has to be pretty interesting, right? DR. NICK: 03:21 Yeah. And it's both a mixture, I would say, of internal and external data sources. So, within the company, you're talking about internal sources like these X-ray crystallographic examples, big machines trying to work out the actual physical structure of either the lock or the key. But you've also got enormous databases - that you can buy if you work in the pharma industry - with hundreds of thousands, millions of different compounds. And the idea is you need to search through those databases to find the most effective drugs to then model into your lock and key environment. BRIAN: 03:50 Wow. Okay. So you also had a number of other items in there, and it's one of the things we always like to talk about a little bit is you went through a lot of different things kind of rapidly in terms of the roles that you've had and the places you've worked. Talk to me a little bit about that and maybe what motivates you. And how has that driven those movements that you've made kind of along the progression there? DR. NICK: 04:16 Yeah, sure. So after I finished my PhD-- and my PhD was really around using a technique called genetic algorithms to kind of take those keys and make them more floppy and try and work out exactly which was the best configuration, and sort of interesting properties in 3D space around these keys. It was fantastic, great to do this kind of academic work, but I really wanted to get into the commercial space. So I spent a few years consulting. I spent a number of years in a large financial services organization kind of doing every bit of the business, so working in analytics applied to retail, applied to investment banking, private banking, insurance, a really good way to get exposure to data across all of these different divisions. Been with Alteryx for three years, I'm into my third decade now as an analytics professional. Yes, I have gray hairs on my beard, but I still wake up in the morning; I get really excited about the mission that's making people win their day back with analytics. It's the idea that analytics can help you augment what you do. You can make better, data-driven decisions. You can do things faster, and you can interpret this deluge of data that we often see in our day-to-day lives. BRIAN: 05:19 Well, yeah. I mean, I think that's a great point. And so in those decades, as you said, that you-- we won't say how many, but in those decades that you've been doing that, do you feel like we're accomplishing that mission? It seems like people are starting to understand this stuff more, but there's still quite a bit of challenge around it and education. Kind of where do you see that at right now? DR. NICK: 05:39 Ah, I think there's never been a more exciting time to work in this space. We talk about the analytic pipeline. We talk about how analysts discover the data sources they work with. They go on to prepare, blend, enrich that data, and then they build that analysis. Now, that could be descriptive or that could be predictive in nature. But then the insights that come out of those analyses, they get distributed into the hands of decision makers. And there's innovation at every single step of that process. The industry just doesn't stand still. It's a fantastic time and a place to work in it. BRIAN: 06:08 Yeah. So speaking of innovation, what's the biggest one you've seen over the years? Or maybe not the biggest, but what's the most exciting to you? What's the thing that really changed? What was that moment like? DR. NICK: 06:20 Okay, probably two things here. So I think number one, open data, the explosion of data and the variety of data that's now available to you. So previously, you worked in an organization, the organization generated data exhaust. Some of it was kept; a lot of it was thrown away. We can now start to augment the analysis that we do by bringing in data sources from the World Bank or data.gov or Quandl if we're doing financial analysis, a huge opportunity to really enrich what we're doing. BRIAN: 06:48 Yeah. So the data that you just talked about, it was kind of-- what did you say? Data exhaust? DR. NICK: 06:52 Yes. BRIAN: 06:53 I've also heard data rot. I've heard some of these terms. Do you think that-- first of all, what kind of data is that to you? And second of all, are companies going to regret letting some of that stuff go? Is it now valuable and they didn't know it in the past? Or is it truly just something that there's some percentage of all the data that's generated that isn't useful? DR. NICK: 07:14 Well, I'm doing a lot of work at the moment in the healthcare sector. And healthcare is a particular problem when it comes to data exhaust. So they capture a number of critical elements. They put that into something called the electronic health record, or the EHR, but they throw away so much information. So when you go for a medical scan and you get an image taken, the doctor will make notes on that and that'll go into your record, but a lot of the other information gets thrown away. There's simply no way to store a lot of this unstructured data. And that's a real risk because, three or five years down the line, you might want to return to one of those images or to an audio transcription of an event and you just simply don't have it. So alongside the explosion in data, we've got the explosion in cloud, the idea that, actually, it's relatively cheap to store and then process this data later on. It's a fascinating sort of tandem of innovation. BRIAN: 08:00 So what do we do about that? I mean, if we're throwing away some large amount of that data, is it literally just because of the size requirements that they don't want to store it? Is it part of the regulations? Why are we throwing away that data? DR. NICK: 08:14 It's partly down to the structure, actually, Brian. So when data arrives from, say, a transactional system, that's generally quite easy to capture and you can stick it straight into a relational database. You can do that on Premise. You can do that in the cloud, same difference. When it comes to unstructured data like all of the notes-- even think about the podcast we're recording right now. We could transcribe those notes. They become slightly more structured, and then we can do more advanced machine learning, pull out the keywords we're talking about, gets closer and closer to being structured data. But in its essence, it's a podcast. It's an audio file. We have to have ways to store that and actually retrieve the information inside it. People are only now cottoning on to the idea that there's huge value in retaining this unstructured data source. BRIAN: 08:53 Right. Okay. Well, we definitely won't throw this away. We're going to hang on to this and put this out there for the world. So given that we've talked about where you came from, kind of where we're at today, and what you're seeing the differences are, I'd love to kind of put you forward-thinking now. And let's talk a little bit about, where do you think things are going? What is your outlook on that? What kind of timeline are we talking about? I'm trying to just get you to get out your crystal ball here and as much as possible. DR. NICK: 09:22 Of course, that's what we do in data centers, right? We have that crystal ball. BRIAN: 09:24 It's a little bit of predicting happening, yeah. DR. NICK: 09:26 No problem. I'll give you two themes; we'll take them one at a time. I'm going to start with automation. I think there's a massive change already underway, but I think there's a lot more that we're going to do. So you take a platform like Alteryx, it takes broken processes and tools like Excel and it helps analysts clean them up and allow them to run repeatedly-- or maybe reliably as part of a schedule. And I think we're doing a pretty good job, actually, in terms of changing the culture for analysts across many organizations, freeing them up, allowing them to do their next challenge. I mean, who wants to sit waiting for 15 minutes while a pivot table recalculates in Excel? And I see this all the time, right? But I think what you're going to find is automation's going to go a lot deeper in the years to come. So we're already seeing something called RPA, or robotic process automation. That's starting to become a major investment for many financial services companies. The idea that software, robots can replace pretty mundane tasks, maybe completing forms, looking up information across multiple systems and giving those answers back to a human analyst to make a decision when it's needed. And I think that's going to be a massive change in the years to come. To get RPA to actually work, you need to have some pretty good data-cleansing, data-preparation, integration technologies behind the scenes supporting that process. So we see lots and lots of customers already using Alteryx to take those first steps with RPA. It's really valuable. DR. NICK: 10:46 I think, secondly, we talk about automation in the context of data science. Again, that's an idea that's just starting to sprout, just starting to really take hold. So you could take an automated machine learning platform, you could develop thousands of potential data sciences models, and then you can have your data scientist to actually go through, screen those and then tweak the very best of those models. Again, it's about making the most of that really valuable resource, that data scientist unicorn, and getting the most out of their daily work. I think we're going to really start to see this idea of a data science assistant really grabbing the attention of analysts in the years to come. I think that's something I'm particularly excited about, putting this in the hands of the analyst, the citizen data scientist, rather than just being the preserve of the pure data scientist. BRIAN: 11:30 Yeah, it's interesting. We've talked a number of times on the show here, as you were talking about RPA and sort of the efficiency and the assistant, is that I think there's a general fear from people who haven't maybe dug into this as much as we have. They're worried about, is this going to replace me? Or, is this going to replace my people? And I think we're definitely of the camp or the opinion that no, it's actually going to allow you to do something greater. But when you're out talking to folks, how do you wrap that message to kind of say, "Hey, here's what the actual value of it's going to be, and it's going to be an enhancement rather than something that becomes a problem"? How do you talk about that? DR. NICK: 12:08 Yeah. We should never be afraid of where technology is going to take us in our jobs. In the 1850s, there was a job description for the role of computer. That was a human job in the 1850s, right? We should not be worried about this at all. I think the application of AI, of advanced machine learning, is all about allowing people to have more time, to level up their own skills and to apply it to the next task. So it's nothing to be afraid of at all. It's about taking mundane work and giving it to the right automation machine to allow us to have more time to do where we have the best value, the human insight. Cool. I was going to say the second big trend that I see is probably around how we actually consume analytics. And it's an area that I'm quite interested in is where analysis, the results of the models that we build, I think they're going to become more universal, more accessible, but I also think they're going to become more invisible. And I brought along a little story that I want to tell you about how technology evolves. And I think we've got something that might be pure magic when we start out, maybe a completely new innovation, maybe a POC. It's really exciting. We don't quite know what to do with it. We move it into a product. Eventually, it might turn into what we call a utility. DR. NICK: 13:15 Take electricity as an example, right? So electricity, the origins, were probably from about the 3rd century AD. Over in Iraq, there was a thing called the Parthian battery. They had no idea what to do with it, apart from maybe electroplating jewelry, okay? So it created an electric charge; that was it. It was magic to these people. Fast forward to what, the 1820s, and we started to see the first electrical generators, 1860, Siemens, Westinghouse. You got a building-sort-of-sized generator producing everything that a factory needed. Fast forward to the 1920s, the national grid. You plug something into a wall; you know exactly what it's going to be. Electricity becomes boring. It's a utility. It's really invisible now, but we can start to build more things off it, 20 years later, computers, 30, 40 years later, the internet. It's really interesting to see the evolution of these different products and how they become invisible in our lives. But we can build higher value on top of it. We see the same with analytics. You take an on-premise data center full of computers. You send it to the cloud; it becomes EC2. You take hard drives; that ultimately becomes something expandable like S3 in the cloud. You even take functions that we build now; we can call that Lambda. And at every point in this journey, we're moving from a product - something we know and touch and buy - into a utility. And we can start building really cool analytics on top of these utilities. It's really exciting. BRIAN: 14:32 Yeah. No, that's a great story. I'm reminded too of a-- I often tell this story kind of in terms of how far we've come in a short period of time and what the utility is, like you were saying. When I was a kid, we used to go on road trips all the time. And of course, back then, it was paper maps and you had to really pay attention to where you were at and where you're on the road and where you need to make that turn and all that. And I remember that, one year-- I'm going to guess this was in the early '90s maybe, early-to-mid '90s. They came out with this device and it looked like a calculator, kind of that size roughly. And what you would do is you would type in-- it was basically a little database in your palm. So they had preloaded in all of the attractions or points of interest and so you had to punch in, "We're northbound on whatever highway and we're at mile marker 10," and what it would do is spit back at you, "Hey, McDonald's is coming back," or, "Some landmark is coming up." And we just thought-- DR. NICK: 15:31 Brian, you were living in the future. BRIAN: 15:32 I know. We thought this was the most incredible-- I remember when we got this thing and I think we played with it until the batteries died on a road trip to Pennsylvania or something. And that was just what, not even 20, 25 years ago. And now looking at Google Maps, to your point about it becoming transparent and it just kind of happens, now you get in the car and you talk to it and you say, "Hey, I want to go here," and it does all that stuff. And the speed at which these things are moving, like you said, because of the cloud is pretty insane. DR. NICK: 16:03 Exactly. I mean, even traveling to Denver, where we are now, I'm getting in a cab. It's got [Satin?] Ave on the screen with live traffic telemetry. This is all military tech from 30, 40 years ago that is now just completely available. You turn the tap; it's like water. It's incredible. BRIAN: 16:17 Right. So one of the things you mentioned, I've heard this term used quite a bit and I'd love for you to just dig into it if you could. Lambda, it's hot right now. A lot of people are talking about it, but I don't know that there's a general understanding of what it is yet, so maybe a little bit of your perspective on that. DR. NICK: 16:34 Oh, for sure. So there is a war coming, Brian, a war in the world of technology infrastructure, okay? And it applies directly to the world of analytics. So camp number one in this war is all about containers. So they use technologies like Docker, which some of our listeners might've heard of, and it's the idea that, pretty much, I can spin up a completely self-contained environment with everything that my application needs to run. So that might be data; it might be the functionality of an application. I press a button-- or I type into a terminal, "Docker up," and up comes my complete image with everything that I need. Very, very elegant solution, a great way to scale applications, particularly in the cloud. The alternative approach is to say, "Right, well, as a developer, I'm writing analytic functions and I don't exactly know how they're going to be used, but I need to deploy those functions as part of a bigger application." So Lambda, it's basically the brand term for Amazon's Serverless. The idea is that I can deploy this function into the cloud and I can call it whenever I need to as part of an application using all of Amazon's cloud infrastructure. Very, very efficient, but I get an exact price every time I call that function. So very quickly, as maybe a chief architect or a chief technology officer in a company, I can work out exactly how much my application is costing today, what it might cost in the future. And it's really easy to scale this serverless function across multiple data centers globally if I need to. So two different ways of looking at developing maybe analytics of the future: I build a big box and I can deploy that through Docker, which is great, or I can develop analytic functions and deploy them in the cloud as well. And Amazon do it, Microsoft do it in their Azure environment, and I'm sure Google have an equivalent as well. So it's very much sort of competing technologies at the moment. It comes down to the level of control you need over that individual function. BRIAN: 18:18 So if you were again to get your crystal ball out and look into the future, in the war of those two things, which one outlasts? Or do they both stick around? DR. NICK: 18:28 It's really interesting. So I follow really closely a guy on Twitter called Simon Wardley. And I strongly recommend, if you're interested in sort of the future of technology and these kinds of patents, have a look at what he does on Twitter. Have a look at his YouTube videos. He forecasts 10, 20, 30 years across a number of different topics. And he spots the point where war happens, maybe where peace breaks out, and then when the next level of war happens, but also which countries are involved in this innovation as well. So forecasting where China might be in 10 years' time versus the US, really interesting stuff. He is strongly of the opinion that Serverless is going to win this one out purely because of the flexibility and the simplicity, the elegance of the solution. Docker is great for today, but it's not where we're going to be in 5 to 10. BRIAN: 19:09 Got it. Okay. Good. So let's move on to-- I know one of the other things we wanted to talk about is, and you've already mentioned it a couple of different times in a couple of different ways, is humans are evolving as well with the technology, and one of the things that we hear consistently is people tell us all the time how much they love what they do, right? And they talk about how they got their life back, and their job is so much fun, and they love the ability to problem solve that they maybe couldn't do before because of technology or construct of their job or whatever. So when you hear people talk about that, sometimes we hear the word addiction used, which-- DR. NICK: 19:51 It has connotations, right? BRIAN: 19:52 Yeah. It has a little bit of baggage there, but I understand what they're trying to say. They're trying to communicate their excitement by saying, "Oh, man, I'm addicted to it. This is amazing. I love doing it." What have you seen around that? Why in your perspective are people so excited, and maybe what's the genesis of that? But also, kind of what's to come with that, getting people excited about data and analytics? DR. NICK: 20:17 Sure. So, last year, we had our customer conference, the Inspire conference at the Anaheim Convention Center, the ACC. So, fantastic event, the opening key note fronted by The Doors and I think a very familiar lead singer. You remember that, Brian? BRIAN: 20:28 Yeah, I think I've seen that guy before. DR. NICK: 20:30 Yeah. And it was closed out by Jane McGonigal, who gave this fantastic talk around how games can change the world. And Jane's done some amazing research using games to live better, to overcome personal challenges. And I think actually in her case, she had a physical head injury that caused her real societal issues, and she built a game system to push her towards recovery. So it's really interesting stuff. As part of her talk, she mentioned two things that really resonated with me. First of all, she said she did some MRI scans of gamers and it revealed huge flashes of brain activity around the pleasure centers of the brain during the playing of games, not unexpected, right? But researchers also found out that there were similar patterns of activity for people watching people playing games. Now, this says two things to me. So first of all, this explains the success of esports-- BRIAN: 21:18 Twitch. DR. NICK: 21:18 Twitch. Exactly. And secondly, the Alteryx Grand Prix, we've got 3,000 attendees screaming like crazy as three contestants build out workflows on a stage. We're all sharing in that collective thrill and that's just magic to me. Secondly, Jane talks in her books about attaining this thing called a state of flow. Now, this magical brain mode, this is where time slows down. It's like the Matrix, right, bullet time. You don't notice distractions around you; you're really in that zone. And this is a big deal to me. So I find that Alteryx gives me that sense of flow; it helps me solve problems. And I think other people get this as well. So that's my real takeaway from everything that Jane sort of said during her talk. I talk to my colleague Shawn in the UK. We're always joking. We're nearly always on WhatsApp at about 11 o'clock at night, plugging away on a workflow, something like that, not just for work, but we genuinely got ourselves into that state of flow. We can't stop. We're addicted to this, solving this problem. It's fantastic. BRIAN: 22:14 So give me some examples if you can. What are some of the latest flows you've been on? What's exciting that you're building? DR. NICK: 22:20 So, deep down, I'm an analyst at heart. So I sit here and I really follow ice hockey in the UK. And I was sitting there going, a lot of these players have played with each other on different teams in the past, and I thought, "Yeah, that sounds about right, but can I actually prove it?" So here I go, opening up Alteryx Designer, connecting up to APIs that are available with all of this player history, using the network analysis chart to work out who is the most connected player. And there I am. It's three hours later. I've ignored my children; they've all gone to bed without a story. But now I know who is the most connected player. It's fantastic. BRIAN: 22:52 Wonderful. It's so funny you mention that. I just saw a story - this was two or three days ago - about the National Hockey League here in the States, and they have converted over all of their things into iPad apps for the coaches. And so they were saying that, up until recently, all of the coaches, when the period is about one minute to go in the period, the guys who are back in the video room and the analysts are mad rushing to a printer to print out the stat sheets of time on ice and how many shots these players take and all these things. And then they hand it to the coach, these hard copies, during the intermission right at the beginning, so that the coach can kind of look at it and kind of review, hey, this guy's getting too much ice time; he's not as effective as the other guy, this kind of stuff. And then the coach would go in and give sort of a pep talk in the middle of the period to say, "Hey, here is what we need to do, guys," with the-- DR. NICK: 23:45 So leafing through this enormous piece of information. BRIAN: 23:46 Yeah. He's going through this binder of information and then they go out and they play another period. And so what the National Hockey League has done is digitize all of that and delivered it on an iPad app specifically for coaches, that they can see that not just at the end of the period but in real time. So as they're on the bench, they're able to look at it and understand analytically what their players are doing and who's more likely to score and these kinds of things. Really fascinating article that I'll make sure we link in the show notes. DR. NICK: 24:15 And that's total digital transformation when we talk about it. I see so many customers still relying on this sort of paper and pen approach to many of the things they think of as critical, and digital transformation should sweep most of that away. We talk about things like the internet of things. When it comes to the NHL, there's one particular API that I've been playing with. And all of the players in their jerseys have little RFID chips, so radio frequency ID chips. So when they're on and off the ice, this thing is bleeping, effectively, back to the data center to say, "Right, Matt Sundin was on the ice for 12 minutes. He came off again," that kind of thing. It's really fascinating to know that data is now at their fingertips. BRIAN: 24:47 Yeah. And I'll have to find this and put it in the show notes, but it was a video from that exact thing that you were talking about, where it was a wide shot of the arena, of the whole playing surface. And they were showing in real time as the players skated around, the actual tracking that was happening and the highlighting and sort of the data overlay over the video of the RFID tracking, which was pretty cool. DR. NICK: 25:09 Fantastic. So I spend so much time on Twitter both talking about analytics but also blogging about hockey and then arguing with people. You're not data-driven enough half the time. Players do have heart and they do have guts, but I need to quantify that with [inaudible]. I want to know why somebody is better and that's what's fascinating to me. BRIAN: 25:25 I think the quip that I made on Twitter was-- I retweeted this article and the quip that I made is I bet the players are looking forward to also a companion app where they can see the effectiveness of the coach in real time, of the line changes. DR. NICK: 25:36 Oh, for sure. BRIAN: 25:38 Seems like maybe that's a little one-way at this point. But okay. Great. That's really cool. Any other interesting flows you've been on, as you say? DR. NICK: 25:46 Ah, well, talk about some of the things that I've done maybe with events and with customers. So they invited me to go and speak at a data innovation summit over in Sweden and I was on stage talking about the Alteryx platform. That was excellent. I also got a chance to speak at a booth where we had quite a few people come up and I thought, "What can I do? What can speak to this Swedish audience?" And again, it's ice hockey. So I'm thinking, "They kind of like this stuff." BRIAN: 26:07 Yeah, they'll be good. DR. NICK: 26:08 Yeah, exactly. So again I grabbed a whole bunch of statistics from the Elite Prospects website, a really good resource for that, for the Swedish Elite League. I pulled down all of the players for the last 10 years in all the different teams and I built a few different analyses. I built, first of all, something I found out from baseball, which is a technique called Pythagorean expectation, which I'm sure all our listeners will know all about. But it's basically working out how lucky a team is based on the number of goals they've scored, the number of goals they've conceded. And it turns out you can model this really, really well. Now, if a team under or overperforms, they could be considered lucky. And over this 10-year period for the Swedish teams, we identified two or three really, really lucky teams, kind of interesting stuff. Now, remember, I'm doing this booth with about 50 other vendors and I'm just shouting to all the people and I'm getting a bigger and bigger crowd. We did a separate analysis about how a player performs over age. So take all the players at age 26, how do they produce, 28, 29, 30, and so on. And obviously, there's kind of a bell curve going on for forwards on the ice where, about 27, they're at their peak. With goaltenders, it's about 31, 32; it's a little bit later. And while I'm doing this, the crowd is getting closer and closer and I'm shouting and shouting, and one guy sticks an iPhone in my face and starts filming all of this. I'm like, "Okay, I'm glad you're enjoying it." At the end, he comes up to me and I said, "Wow, you look like you really enjoyed this talk," and he said, "Ah yeah, but you were talking about one of my favorite players. I grew up with him." And it's a really small world when I mention one player that plays for Guildford who comes from Sweden. And his friend from university is standing right in front of me. So it's a really small world in analytics. I love it. BRIAN: 27:41 Yeah. And especially within the sports world, I think, going back to the question I was asking you a little earlier in the show about should we be afraid of this. In a sports world, I think some players should be afraid of it if they've been sort of cruising along for a while without anybody paying attention and then the analytics come and it's like, "Wow, they're not as effective as we thought." But the counterargument to that is you're also getting information about maybe players who in the past would not have made it to the higher echelons of these leagues and sports in general, because it's oh, they don't have what it takes and whatever, but then the analytics tells a different story. And so it giveth and it taketh away. DR. NICK: 28:18 Well, let's flip it as well. So we can be overwhelmed or we can be driven too much by these metrics, but take it as a way to develop yourself better. You've got a metric; it's a metric that company cares deeply about. So maybe as a professional sportsperson, you need to score more goals or be on more assists or whatever it might be. That can be your goal, is help set targets for the future. I think Jane McGonigal talked about this quite a lot. By having these simple targets laid out in front of you and being able to measure and actually quantify the value of them, that's a really powerful motivator for a lot of people. So don't be afraid about losing a job. Think about making yourself better as a result of having better access to insights. BRIAN: 28:53 Right. Yep. All right. Anything else you wanted to get out for our listeners here before we go to community picks? DR. NICK: 29:00 Oh, sure. I have one more kind of interesting little story. So I do some work with an internal program we have at Alteryx called Alteryx for Good. So it allows us as associates to go off and spend so many hours every year dedicated to whatever we want to do with nonprofits or any organization that we feel strongly about. And I do a lot of work with DataKind in the UK. Now, DataKind is a global charity, does work with data scientists and with analysts. It's all about helping out nonprofits, government organizations, charities to make best use of their data. They collect great data. They talk to individuals that have real problems in society, but they don't always apply that data in the best way. So getting a bunch of data scientists together and working on a real problem can actually really move the needle. It's really exciting stuff. So I worked on a project with the Ecological Land Cooperation in the UK, a tiny, tiny little charity. And basically, they are trying to increase the number of smallholder farmers, so basically encouraging people to produce high-quality, small-scale agriculture, kind of a big deal compared to the megafarms that we often see these days. And we did a great piece of analysis in Alteryx. DR. NICK: 30:04 So first of all, we scraped some websites to take all the pieces of land that were for sale in England and Wales. We did some machine learning to classify whether they really were agricultural land or whether they were just a big country pile for some big city banker to go and buy. Kind of the features we saw in the data, it's like, does it say close to a marina or commuting to London, as opposed to Bali or some other kind of crop. So a really nice bit of classification up front. We took all of those details; we took every single package of land in southern England by using a government open data source - so every single spacial shape representing every sort of land and back garden in all of England - and we mapped the two together. We applied some constraints that the charity had - so they wanted to buy land between 10 and 30 acres - and we could immediately tell them which pieces of land were for sale that met their quality expectations. Previously, they were literally cycling down country lanes, looking over a hedge and saying, "That looks really nice. I wonder if it's for sale." Can you imagine how broken that process was? But they didn't know anything better. We then replaced it with a land explorer. So literally, we could give them a targeted list of 20 pieces of land for sale; they could go and look at those in more detail. That's where the automation comes back in. It's fascinating. But for them, it was like magic. This was honestly such a transformational change using a little spring-cleaning of data sites. BRIAN: 31:22 That's wonderful. That's a great story. Okay. So community picks, tell us what's been exciting to you out there in the world that you think we should point people to. DR. NICK: 31:30 Cool. Well, I'll start off in-house. We'll start with the Alteryx community. I can't say strongly enough, we've just hit 2019; get going with those weekly challenges, people. This is so important. If you're not practicing Alteryx and you're not being exposed to all of these different tools and techniques that the community comes up with, you are really missing out. So that's my number one pick, kind of simple stuff, right? I would say, secondly, a huge shoutout to Chris Love, who produces some fantastic content. In fact, it was his YouTube videos on Alteryx three or four years ago that actually encouraged me to join the company, so a really, really big deal. He has a blog on medium.com called Data Beats that he works on with a couple of colleagues where he uses Alteryx alongside Tableau and other techniques to really make sense of some interesting data sets. So if you want to see a real master at work, go and visit Chris Love's Data Beats. I'll finish off by saying, if you want to know where analytics is heading, I would strongly recommend to everybody, invest in a subscription in companies like Safari Books. So we have this available internally. It's a monthly subscription, gives you access to tens of thousands of different technology books, self-improvement books, that kind of thing. But it's worth the price alone, just to get access to videos like the AI Conference, the Strata Data Conference. And you can download all of these on your iPad. Next time you're taking a trip, just watch a 30-minute video and see where the world is going. I've learned so much about healthcare, blockchain, AI, all of the intersections of the things I really care about, just through watching these conference videos; it is like you're there. So, strongly recommend that. BRIAN: 32:59 Wow. That's great. I'll have to check that out. That sounds really awesome. And I will just give a plug for my community picks, again, kind of in-house here, just to let people know if you don't know. We have set about on a journey to localize our community. And believe it or not, when I look at the analytics for this podcast, of course a good percentage of it is in English-speaking countries, but you'd be surprised; there's a good percentage of it that's around the world in different locales. And so what we are attempting to do is make sure that we have Community available for everybody who wants it. Now, that's a very long road, but we've started out with French. We have German and we just launched Japanese as well. DR. NICK: 33:41 Oh, arigato, Oblinger-san. BRIAN: 33:43 Thank you. So definitely go check that out on the Community. If you click the little globe icon on the top right-hand corner, that'll get you into the language [selecsher?]-- selector, I should say, and then you can check that out. And so we're growing those. It's going to be great. We're going to be adding more of those along the way, and I'm so excited to bring the Community to more people around the world in their native languages. DR. NICK: 34:04 Fantastic. And I was out in Singapore and Hong Kong just before Christmas, fantastic community that's being built over there. They are so passionate about what we're doing and passionate about analytics in general. So I'm glad we can start to really tap into those areas. BRIAN: 34:17 Excellent. All right. Well, thank you for being on. This has been wonderful. I learned a lot. I have a bunch of research and watching of conference videos to do, so I'm going to go away and do that. Any parting words? Where do we find you? Where do we find more Nick Jewell? We go on Twitter? DR. NICK: 34:32 You can go on Twitter. If you can stand the live-blogging of the hockey, you can find me at Nick Jewell on Twitter. I do talk about analytics, and I do retweet quite a lot as well from people that I find inspirational in the analytics space. You can also find me on the Community. So I will be there answering questions. I also occasionally write some blog posts around the product on the platform. So come and check me out on Community too. BRIAN: 34:51 Excellent. Thanks so much. DR. NICK: 34:52 Thanks, Brian. [music] BRIAN: 35:02 Thanks for listening to Alter Everything. Go to community.alteryx.com/podcast for show notes, information about our guest, episodes and more. If you've got feedback, tweet us using the #altereverything, or drop us an email at podcast@alteryx.com. Catch you next time. Nick, so I'm really excited actually to hear about your roots in hockey. You may not know this, but I grew up in kind of not rural, but in Indiana where it's cold all the time and the ponds freeze over. And you have-- DR. NICK: 35:48 You get to play hockey like it was meant to be played. BRIAN: 35:50 You have no choice but to play hockey. It's not a choice that you get to make. It's just who you are as a person growing up. And so I loved earlier on in the show you were talking about hockey. So tell me a little bit about you and hockey. How did you get into it? What's your favorite team? Let's dig into the hockey a bit here. DR. NICK: 36:06 Sure. Well, it's always one of those things. When you're in the UK, you kind of grow up and when people cut you, you either bleed football or rugby, I guess. It's one of those two things. And they're okay; they don't appeal to me in the same way. BRIAN: 36:17 We're going to anger all of our listeners, "They're okay." DR. NICK: 36:19 No, they deserve it. It's a boring, boring sport. But what I found with hockey, I went to do my PhD at Sheffield University, okay, sort of like the middle-to-the-north of England. And again, they've got a couple of really famous football teams. But they had a really good ice hockey team called the Sheffield Steelers, and they played in the Elite League in Britain, which is kind of like the upper tier, probably equivalent to like the seventh tier in the US, but you get the idea. And I loved them. They were great, really really passionate bunch of individuals. When I came back down south to live - so I lived just outside of London - I started following the Guildford Flames. So I went to university in Guildford for my first degree. So it's basically wherever I went to do a degree, I can support that team. That's my rule. And Guildford ended up acquiring quite a lot of the NHL players, wittily, during the lockout. I think it was in 2004? BRIAN: 37:04 Right. Right. DR. NICK: 37:05 So we had the Calgary Flames goalie playing in this tiny provincial league because I think he just wanted to come over and see the UK for a bit. Crazy quality, it's just grown from there. I got a season ticket. You get to meet and interact with the players a lot, so you really sort of bond with them as humans rather than just statistical objects. And since the advent of Twitter, it just felt like a natural thing like get on the-- lots of people tweet the goals and the penalties and so forth. I try and add color or opinion, which is kind of completely non-analytical but very subjective, gets into lot of arguments. It's great fun. Just to try and be descriptive about what we do, so even this week over in Denver, I went to a University of Denver game just yesterday. It's been great. I love hockey and I'll watch it wherever is comes. BRIAN: 37:43 That's awesome. Yeah, so like I said, I grew up in Indiana, in Fort Wayne, Indiana. For our listeners out there, I think I talked about this on the episode with Patrick Digan. But they have a minor-league hockey team there that they've had forever. It's like a 80-- DR. NICK: 37:54 Is that the Komets? BRIAN: 37:55 It's the Komets. It's like an 80 year, 90-year-old hockey team or something. And yeah, I grew up going to games and just being around it. And I have to say-- and I've also lived in cities with NHL teams. I have to say, I think I like minor-league hockey better because they're more hungry; they want it more. There's more of a-- you can see the drive in the players, that they want it bad. And they're trying so hard to get to that next level, and I think that adds a little bit to it. And then of course, you also have sort of the general shenanigans that happen in minor-league hockey that would never happen on a nationally-televised game or something, the mascots, the shows during the intermissions. I mean, just the-- DR. NICK: 38:40 Well, I'll tell you what. My college experience just last night really put that down. So it's like it wouldn't be college sports in America unless somebody was inside a sousaphone, you know? BRIAN: 38:47 Yeah. Right. So who's your favorite team these days, or who's your all-time favorite team or favorite player? DR. NICK: 38:55 So I think we have to look to Toronto; we have to look to the Maple Leafs. That's-- BRIAN: 38:58 You're a Toronto man. Okay. DR. NICK: 38:58 That's my NHL team. I've got family over there. I think Britain generally has massive affinity for Toronto. I think a lot of people immigrated there about 100 years ago. So Toronto is where it's at for me. BRIAN: 39:08 Well, they're an Original Six too, so you've got that. DR. NICK: 39:09 Exactly. They're storied, but they were rotten for a number of years, right? They just could not perform. It's been 50-plus years since they won, right? BRIAN: 39:18 I heard you name-drop Matt Sundin earlier and I knew that you were a real fan. So, yeah. DR. NICK: 39:22 Yeah, the almost-glory days of the late '90s, yeah. Absolutely. Now I'm showing my age, Brian. Thanks, you've made me reveal it. Yeah, I think what happened just the last couple of years is, when they've had that complete organizational shift, they've gone with Babcock as their coach, who's been hugely successful for Canada and also for Detroit-- BRIAN: 39:38 Detroit. DR. NICK: 39:38 --as well. I mean, it's their time; it's coming up. They had the number one draft pick. They managed to lure Tavares out of New York. And that brilliant photo of him aged 7 or 8 sleeping under Maple Leaf duvet with Star Wars Phantom Menace toys in the background. It's like, these people will want to come home when it's safe, was the quote. It's now safe, doors are open. We're going to win it soon. BRIAN: 39:59 Nice. All right. Well, we'll stay tuned for that. DR. NICK: 40:01 Thanks man. BRIAN: 40:02 I will say that-- so growing up, like I said, minor-league hockey. It's interesting because by geography, I should've been a Black Hawks fan, but for whatever reason we always gravitated towards the Pittsburgh Penguins. And this was back when, Mario Lemieux and Jaromir Jagr and Sergei Fedorov and I could go on for days. DR. NICK: 40:23 And Jagr is still playing. BRIAN: 40:25 Somehow. I don't-- DR. NICK: 40:27 Age 74. BRIAN: 40:28 I don't understand it. But someone must be looking at analytics and giving the guy a shot. I don't know. But so I'm going to put super Mario down as my all-time favorite, of course Crosby now, just because I still follow the Penguins. But yeah, I love hockey. It's wonderful. It's interesting to see it spread kind of more across the globe. I know that the NHL is really interested in playing games in London and other places. DR. NICK: 40:51 Absolutely. Well, a big thing from the UK is that team GB has literally just qualified for the top tier in the World Championships. So I'm going in May this year and we're going to get trashed. Literally the first game's against Canada, so let's put this into perspective. BRIAN: 41:04 Oh, that's not right. Come on. DR. NICK: 41:05 But we made it. We made it back to the top tier. It's been 30 years since we've been there, so it's really, really exciting. BRIAN: 41:10 That is great. DR. NICK: 41:10 I want to touch on one point there. We talked about Jane McGonigal. We talked about flow around Alteryx. If I had to explain ice hockey to somebody that had never seen it before, there is a moment, I think we all know it when we watch games enough, and when you've had a little bit of practice, you can follow the puck around. That's the number one difficulty for new fans. When you can spot the puck, when an attacker takes that move and does a little deke with the puck, everything slows down. Everybody can see that, and it seems to last for like minutes and minutes. And then they score, and it's just beautiful. And then time comes back to normality. BRIAN: 41:38 Hold your breath for a few seconds during the epic move, yeah. DR. NICK: 41:41 It's like the home-run swing and everybody goes, [inaudible], and there it is. It's flying off. And you've been there and you've witnessed that moment. And with hockey, it's even more beautiful. For that control, you're skating and you're controlling a stick and a puck. I wish I could do that. BRIAN: 41:55 I always used to hear, I think it was John Madden, of course famed NFL guy here in the States, he used to talk about hockey and how he didn't understand it because he was so accustomed to the NFL way of announced and-- substitutions is what he was talking about, how do you substitute a player on the NFL football field here. And he was saying when he went to a hockey game and there were just guys that are jumping over the boards and whatever. DR. NICK: 42:18 He couldn't understand how they-- oh, chaos. BRIAN: 42:19 And he's like, "Wait, so they don't have to wait for a stoppage in play?" or, "They don't have to announce who's coming on?" or, "They don't have to swap out a guy for another guy?" And it is a very fast-paced game, especially these days in the NHL when they get into overtime and they do 3-on-3 and it's just back and forth and it's-- I wouldn't say that I regularly root for overtime, but when it comes about, you get that feeling of like, oh, this is going to be good, because that 3-on-3 is amazing. DR. NICK: 42:47 Exactly. And think about what Moneyball did for baseball, right? So it totally made us focus on different ways of looking at the data and it said, "Right, don't go for the home-run guy. Go for the guy that hits singles reliably." With 3-on-3 hockey-- and I scream this on Twitter all the time. When it gets to the overtime, hold on to the puck. When it's 3-on-3, you have all that space-- BRIAN: 43:03 It's possession. DR. NICK: 43:04 They have to commit two to come and get you and you've got the time. So if you can hold onto it, you're going to win. That's my two cents on that one. BRIAN: 43:11 Nice.

This episode of Alter Everything was produced by Maddie Johannsen (@MaddieJ).

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