Alistair Croll: [00:00:01] I think you have to take a leap of faith that this idea that you're absolutely obsessed with is right in the face of criticism and this is a real challenge for founders because you also need to listen to people like you need to have reality. But at the same time, you have to have ridiculous faith that the world is going to be true and real and accurate, that the real challenge here is that we need to instill leaps of faith into people who want to found things. The wonderful thing about the modern world is that with social media and Kickstarter and cloud computing, all these things, you can try stuff out very, very quickly and very, very easily.

Harpreet Sahota: [00:00:51] What's up, everyone? Welcome to another episode of the already some data science. Be sure to follow the show on Instagram @TheArtistsOfDataScience and on Twitter @ArtistsOfData. I'll be sharing awesome tips and wisdom on data science as well as clips from the show. Join the Free Open mastermind Slack Channel by going to bitly.com/artistsofdatascience. I'll keep you updated on biweekly open office hours that I'll be hosting for the community. I'm your host, Harpreet Sahota, let's ride this beat out into another awesome episode. And don't forget to subscribe. rate, and review the show.

Harpreet Sahota: [00:01:37] Our guest today is an author, analyst and entrepreneur. He spends most of his time finding interesting stories of how technology and society intersect.

Harpreet Sahota: [00:01:45] He's known for founding Coradiant, The Strata Conference, Year One Labs, and writing Lean Analytics. Recently, he's been teaching class on data science and critical thinking at Harvard Business School. A Product manager by trade, he's launched five major conferences, including O'Reilly's Strata, the International Startup Festival, the Ford 50 Digital Government Event and Cloud Connect. Until recently, he spent a lot of time on planes speaking about data science, innovation, scaling startups, digital government, A.I. and applying critical thinking to technology. But he spent the last month trying to understand the future of public events, partly because he's managed to break his leg during a pandemic. So please help me welcoming our guest today. A man who tries to mitigate chronic ADD by thinking about far too many things at the same time - Alister Croll!

Harpreet Sahota: [00:02:31] Alistair, man,  thank you so much for taking time out of your schedule to be here. I really appreciate you.

Alistair Croll: [00:02:35] It's great, great to be here Harpreet. Thanks.

Harpreet Sahota: [00:02:37] Talk to us a little bit about about your journey, how you first kind of got into the data sphere. What kind of drew you to the field?

Alistair Croll: [00:02:46] I can go back pretty far. I as a kid, I had an Apple 2 with a 300 baud modem. So I had to, like, spend my summers figuring out how to get that to do things. And then in university, I had a feud with the dean of the business school. It's a long story I won't bore you with. That's to do with student fraud and all kinds of stuff. And we kind of uncovered some stuff that was going on with the University.

Alistair Croll: [00:03:08] And we were running the student council and that caused a lot of discomfort for the dean of the business school. Turns out he was also my stats teacher. And so I had to do really well because if I was going to make any mistakes, he was going to fail me. Right.

[00:03:20] So I was like I actually had to open up the books and work hard. And so I got some good stats background there. And my parents are both scientists. So I grew up with the scientific method and thinking about, you know, biases and how to how to understand things properly. And then a few years later, I started this company with some friends called Coradiant. And Coradiant - It was real user monitoring. So Web analytics shows you what people do on your Web site, but it doesn't show you if they could do it. So, like, maybe the person didn't bike as the page took forty, forty seconds to load. Maybe the person didn't buy it because they got a five hundred error and there was no JavaScript to tell you that. Right. And so with this product was called TrueSite. It was part of what we call real user monitoring products. But in order to sell it, we started out selling to the technologists - they didn't have the budget or the sort of authority within the organization. So we wound up having to sell to marketers. And so that forced me to get into, you know, speaking analytics to them, even though I was like a networking head. And then we kind of moved from Web analytics - I wrote a book with a a guy named Sean Power called Complete Web Monitoring that got into - like, how do you measure social profiles? And all this other stuff that requires a lot of big data. And then O'Reilly was putting out a book series called Lean -  Call It based on a lean startup series.

Alistair Croll: [00:04:32] And they asked my co-author Ben Yoskowitz and I to write a book. So we wrote Lean Analytics. Lean  - The Lean Startup is an amazing book. It's a book that's launched thousands of ships, that's proverbially speaking, but it is very aspirational.

Alistair Croll: [00:04:46] It's not specific. And we're more like Bob Ross like paint a Happy Tree, you know, like very boring. Here's the prescriptive stuff. If you're here, do this until it gets this. And so I think that's one of the things that that helped the book catch on. Nobody's more surprised at how far it's gone. I got a mail from someone whose taking university in Madagascar who's like, this is my textbook. But I think that if you are trying to understand the modern world and you're not thinking critically about data and statistics, you are probably being tricked or taken advantage of almost every day. I mean, we need data literacy to survive the modern Internet. And so part of it was business through analytics and web. But a lot of it was just figuring out how navigate today's information heavy world.

Harpreet Sahota: [00:05:33] Pretty interesting, man. And your book has been really influential, I think to a lot of up and coming data scientists. It's one that I recommend to pretty much all of my mentees to get a understanding of how to develop a business sense and a business acumen.

Alistair Croll: [00:05:46] Thanks.

Harpreet Sahota: [00:05:47] So where do you kind of see, you know, taking into consideration the history you have with the data, with analytics. What do you think the next two to five years is going to look like for businesses leveraging data and analytics?

Alistair Croll: [00:05:59] That's a good question. I think one of the dirty secrets of data science, at least in the early days, was that 80 percent of what people call data science was just ETL. Just cleaning up data and moving it from one place to another, you know, moving stuff between buckets. We're starting to see a lot of democratization of those tools. And so you're starting to see things like datarobot that will lead a non-technical person, experiment with models and kick the tires and so on. But I think one of the things that is going to happen is, as when things start out, we use them tentatively. In the early days of cloud computing, you know, that was fine for QA. It was fine for like putting a dev build on there. But you didn't really do it in production.

Alistair Croll: [00:06:36] And so if you look at the early use of QA, it was all startups that couldn't afford to spin servers in a data center. Sorry. In the early days of cloud, as all startups that couldn't afford to put stuff in datacenter and people doing non-critical workloads. Same thing with virtualization. It was what you used to put your mail and print server on the same machine. And so early on, data science is being used for things where there's very little risk, right. Like cleaning data, moving, suffering. But there's always a human in the loop. So you're informing someone. It's like, hey, data science, explain this data to me. I think when that gets hooked up to automation, when we are trusting it a little more. The way that today people use cloud computing for production servers, right. Production environments. I think that when we start to trust it a little more, we're gonna see data science that's making a change, that is essentially running an experiment which updates the model. So. So data science that continues to correct it's own drift, and the human is the exception rather than the rule. Today, I think in data science, the humans still the rule. You do the data science. Someone looks at it. Does that make sense? Yeah. What could it be? And I think when when we only throw stuff to a human, if the machine can't deal with it. So you have these feedback loops that's going to change things a lot. Because humans can conduct a couple of experiments a week. Machines are experimenting with every click. Right. And so I think the human is going to come in and be much -

Alistair Croll: [00:07:55] I did a talk last year for the IIA about being A.I.'s therapist. Like, we're gonna have these algorithms that are working and where the therapists we check to make sure it hasn't gone turn into a sociopath.

Alistair Croll: [00:08:07] We check to make sure that it's it's, you know, still aligned with the business goals. But I think that the change we're going to see in the next two to five years is we're going to see the data scientists hooking their models up to automated systems that produce new models. And once that cycle starts, I think it's going to accelerate the deployment itself pretty quickly.

Harpreet Sahota: [00:08:26] So it's this new vision of the future then what's really going to separate, like the great data scientists from just the merely good ones?

[00:08:33] If you'd look at an exponential curve. Right. What matters in the exponential curve is the starting number. Right? Because the sooner you start, the better. And the slope of the curve. I think you're going to see that really great data scientists can narrow - it can arrive at a working model much more quickly. Partly through their intuition, and that they will transition from building a model to managing the model. Which is actually a different set of skills, correcting for drift, finding out what could go wrong, are the factors still there, and so on. And I think they're also much more focus on like fast start learning. So instead of needing, you know, millions of compute hours to actually generate a good model, you're going to have data scientists go, oh, I know the model. That's going to be very close to what I need ahead of time.

Alistair Croll: [00:09:24] And I think that that process of anticipating, you know, what will make something happen sooner is going to be the mark of a great data scientist. There's also I do a lot of work with a company called Georgian Partners. Georgian is the biggest V.C firm in Canada, and they have a thesis for investing based entirely on trust. They're like, look, in the future, everything's gonna be data driven. Everything's gonna be running on a model. And the companies we trust will be the ones that don't squander the trust we've given them. So I think that data scientists who build ethics and trust into their models. Things like differential privacy are going to be much more sought after because especially with GDPR and the consequences there. But also as this stuff becomes commonplaces there, as every vendor has access to some kind of A.I., you're going to choose the vendor that doesn't, you know that is using a for you instead of on you. And I think that data scientists that are conversant in that are going to do really well to.

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Harpreet Sahota: [00:10:51] That's pretty interesting. Yes. It seems like the data scientists that are going to excel are really only the ones that are able to almost like diagnose a model to see where it is that it's failing. And Then..

Alistair Croll: [00:11:01]  Right. They're going to be the therapists.

Harpreet Sahota: [00:11:03] You kind of mentioned something there about, you know, privacy, GDPR. Is that going to be a huge concern going forward in the next two to five years for data scientists?

Alistair Croll: [00:11:11] So if it isn't, those data scientists should be replaced. The UK can fine - the European Union, not the UK -  can fine organizations, hundreds of thousands dollars. And there's no organization that can say fine we'll only serve American customers. Right. So whatever the UK does becomes policy in the U.S. because Facebook and Google obviously have large presences in Europe. I think that the understanding your data and how valid it is, how clean it is, what you're allowed to know about a user. I'll give an example. I there's a picture of Barack Obama in my Google Photos. I didn't take it. I just happened to have it on a computer that syncs to Google Drive. Right. When I go in and look at Google Photos, it does this thing where it suggests names for you. Shows me Obama's picture and it says, who's this? Clearly, Google knows who Barack Obama is. Right. But the point is, Google is not allowed to know who Barack Obama is in my photos unless I've told it I know this is Barack Obama. Once I tag it, it's now allowed to know anybody who looks like him. Even though Google knows who Barack Obama is. It doesn't tag him in my photos because that's a violation of - That's creepy, right? Like now all of a sudden he's taking all my friends? Wait, it now knows I know that person I met in Vegas for a horrible one night 20 years ago or whatever, like awkward. And I think that thinking that through if you do a Google search for how to kill myself painlessly, should Google give you the answer or should it give you a suicide prevention hotline? Right. So that it's not just is the data right. It's am I allowed to know that I know. Is it appropriate for me to provide the right information or should I provide like someone? I did a talk for some librarians awhile ago, and in the old days, the librarians had this equivalent of their Hippocratic oath that was like, just give the person the information.

Alistair Croll: [00:12:48] Your job is not to editorialize. Right, because they were the first source. Today Librarians like the last source. And so if someone comes in and says, give me all the books on why vaccines kill kids or give me all the books on why the Earth is flat. Should the librarian now go, I'm going to bring you some books on the shape of the earth, right. Here, and educate. And that's a very different role from what a librarian traditionally was, which was like an essentially a database query, like you'd write the query saying, give me books on the flat earth and you get the query back. And so I think it's it's also a good example of this is if you take out your phone, this used to happen a couple years ago and you type in my friend is a nurse. And the next word it suggests will be she. And she's on the one hand, that's a really good data prediction because 87 percent of nurses are female. On the other hand, it's not the future we want. That's based on decades of patriarchy and shitty employment inequality and so on. And so I think the skills of a data scientist to have to incorporate things like what am I allowed to know? What should I show the user? I know, you know, should I first seek to do no harm? How could this be misused? Those are like philosophy skills. And I think that's the fascinating part. That's why the the field is so interesting.

Harpreet Sahota: [00:13:56] So kind of running with this idea of privacy here and blending that in with the concept of one metric that matters. How do you think that's going to manifest in terms of, you know, measuring privacy or measuring. 

Alistair Croll: [00:14:07] Sure. 

Harpreet Sahota: [00:14:08] Security And things like that?

Alistair Croll: [00:14:09] Yeah. So. So just for context, the one metric that matters is a concept that Ben and I talk about in Lean Analytics. And the idea is that as an early stage company, your focus is your biggest currency. And so you need to focus. And yes, there are 10 things that might be important.

Alistair Croll: [00:14:25] But like if you have a website and you get no traffic to it, your biggest problem is I got no traffic. Now you get some traffic. Okay, what's the problem? Not enough conversions. OK, I get conversions. What's the big problem now? Oh, now it's the shopping cart isn't full. OK, shopping carts are pretty full now. The problem is too many returns, and so on. Right. And so you pick the biggest problem that you need to fix right away, keeping in mind the sort of minimum viable product and what your goals are and so on. That data point is usually collected through analytics and other stuff. I don't think early on that companies will say, like, my one metric that matters right now is, you know, the thing I need to focus and optimize for his privacy. I think we do have to think about it in the context of product design. So we're using Zoom to record this right now. Zoom's a very popular platform. Zoom does not deserve to be the best videoconferencing platform in the world. And I see that as a paying user.

Alistair Croll: [00:15:17] Zoom got where it is because they made the product so easy to use, it was broken. You could literally join a Zoom thing without logging in. This way, the whole zoom bombing thing happen.

Alistair Croll: [00:15:29] If I said to you, hey, I've got a great idea. My new e-mail account is really easy, Harpreet. Just go to this, your URL and it works. And you're like, oh, I clicked on it. It works. This is great. And then you go, wait a minute. Other people can click on that link and they can read my mails. No, you've got a broken platform, right? That's what Zoom did. I mean, they fixed it well, but that's what they did. They they said we're gonna make it as easy as. So it's like Einsteins, old thing as simple as possible, but no simpler.

Alistair Croll: [00:15:56] Right. I think. As simple as possible, but no simpler, considering privacy and data ethics and all those other things. And the problem is we're going to see a lot of bad actors who run fast and loose around the standards of data privacy. And it's up to us as the market to punish them for it. If we don't have regulation and we don't have consumers and frankly, I'm worried about this because, I mean, Zoom is where it is because it made it mindlessly easy. But that word mindlessly has a bad side eff- connotation, Right.

Alistair Croll: [00:16:28] So I think that one of the big challenges for anybody involved in AI driven product is where I draw that line, whereas the ethical line about making something easy in in the pursuit of my one metric that matters. Right. If oh, I really need conversions to be easy. Great. We'll just put things in people shopping carts and they can remove them if they notice.

Alistair Croll: [00:16:48] Yeah.

Alistair Croll: [00:16:49] That sound like a good stat?. Well it's one hell of a way to maximize shopping cart size, but it's probably not very appropriate. Right. And so I think that's where privacy manifests is like in pursuit of your metric that matters where you've got to do whatever the hell you can. Where are the guardrails? And I think those are going to change a lot. And you need people who are paying close attention to that because, you know, one bad thing can end the company. If if it hadn't been for the fact that it was using Zoom to hang out in a pandemic, they probably have been punished a lot more by the market. As it is like the federal government of Canada banned them for a while. But like last week, they or this week they had a parliament setting in Zoom So clearly they were OK with it eventually. And by the way, the first MP had their microphone muted.

Alistair Croll: [00:17:29] So that started well,

Harpreet Sahota: [00:17:30] We only started this episode like that. So switching gears here a little bit, I mean, you've been involved in a number of amazing entrepreneurial initiatives. Do you have any advice or tips for anyone who's been toying with the idea of entrepreneurship?

Alistair Croll: [00:17:43] One of the reasons America is amazing at launching companies and I'm Canadian, but I love America.

Alistair Croll: [00:17:48] I've lived there twice. Is that it is a faith based nation. And that isn't what most people expect me to say. I don't mean religious. I mean faith based people in America have faith that they can do something. Faith is the reinforcement of belief in the face of being disproved, right. If you take a leap of faith, it's because you don't see a bridge in front of you and you're jumping anyway.

Alistair Croll: [00:18:11] The idea of a startup is to build a sustainable, repeatable business in conditions of high uncertainty. Right. You're trying to find a new business. Hair salons, not a startup. That's just entrepreneurship because hair salons are easy to run.

Alistair Croll: [00:18:23] You know, the model, a startup is looking for something new. If something new was easy to do, it would already have been done.

Alistair Croll: [00:18:30] Therefore, it's something that a lot of people - that either technology just changed and you're having this vision of the future you can't get out of your head or you're trying to solve a problem that other people try to solve. They couldn't solve. Which tells me you need to have this blind faith that for some reason used spurring startup entrepreneur with no experience can solve this problem. Right. I mean, the amount of faith you on Moskva had to say, I'm going to build a spaceship company, a solar company in a car company at once. It's pretty funny. I think one of the things about that group that graduated from PayPal is that group all realized they could do whatever they wanted. And so you did, right? I think you have to take a leap of faith that this idea that you're absolutely obsessed with is right in the face of criticism. And this is a real challenge for founders because you also need to listen to people. I used to wear a T-shirt that said your mom is not a valid test market. Like you need to have reality. But at the same time, it is ridiculous faith that the world is going to be true and real and accurate and stuff like that.

Alistair Croll: [00:19:22] So I think that the real challenge here is that we need to instill leaps of faith into people who want to found things.

Alistair Croll: [00:19:33] Usually the only people who will take a leap of faith is someone who can afford to lose or someone who has nothing to lose. Right. And I think that the people who have a bit to lose in the middle need some nudging. The wonderful thing about the modern world is that with social media and Kickstarter and cloud computing and these are the things you can try stuff out very, very quickly and very, very easily.

Alistair Croll: [00:19:54] And I think that that's where, you know, that ability to have friends who will cajole you and bug you and get you to do stuff, but also the ability to sort of take a leap of faith. Knowing that you're probably wrong, but it doesn't matter is is more challenging. It's figuring out how to have faith in something improbable and then pursue it to the point where, you know, if it's actually going to happen or not is really important.

Harpreet Sahota: [00:20:19] So would you say those are the two kind of key traits that would make someone, you know, become a full fledged entrepreneur is it's to have faith that the idea that they have is going to be novel and solve a problem that nobody...

Alistair Croll: [00:20:33] I'm not sure if I'd say it's that they have faith in their idea, that they have faith that this is a problem that needs solving. Right. And I think that that way, that's a better way to define it, because there may be lots of products that can solve the problem. But if you're obsessed with the problem, you'll probably find a way to solve it or find out that it's not a real problem. That's the first thing. And so it's this weird Zen you have to, like, have incredible humility to learn what other people think and adjust your perception. And then you also have to have this crazy faith to keep you going and believe that you're absolutely right. And finding people that can do both those things at once is very difficult.

Harpreet Sahota: [00:21:06] So in terms of data science, entrepreneurship in this COVID/post-COVID area. What do you see as some problems with tackling that in enterprising data? Scientists can can identify and then get into an opportunity?

Alistair Croll: [00:21:20] Sure. I think one thing that's obvious is we're doing much more stuff online now. And so there are going to be opportunities.

Alistair Croll: [00:21:27] I've been digging deeply into the conference and event industry. Here's a here's a thought about that.

Alistair Croll: [00:21:33] When you attend a virtual event, you're always in the shopping cart. Like, if I chat with someone about virtualization, then any vendors that know that they're selling virtualization products may go. Maybe they go, hey, that guy's probably a better prospect, right? Everything we do on like when humans do stuff in the real world, we're kind of sloppy. We don't take good notes. When you do online, you have no no excuse but to take notes. And so I think that as our behavior gets more and more analyzable, whether we're talking about COVID tracing, you know, contact tracing or talking about increased use of online tools or whatever, crises have a way of accelerating the inevitable. And I think one of the things that COVID is going to do is accelerate a lot of these trends to a quantified life with, you know, constant tracking of what we do analytics. And there are tons of opportunity for data scientists in that space. New industries that are going online are suddenly manufacturing way more data than they used to have and need data scientists to crunch it.

Harpreet Sahota: [00:22:29] So you've been writing a lot about innovation at Tilt to the Windmill. How should be incumbents think about innovation?

Alistair Croll: [00:22:35] So tilt the windmill is a project that I started a few years ago, because after the analytics, a lot of enterprises asked me to go in and help them with innovation and very quickly realize that innovation is not the problem there. It's usually cultural. The way and I've written a few things tiltthewindmill.com. But the way that it - that I think about this is there's been a lot of models that separate different kinds of innovation. So Clay Christiansen talks about sustaining versus disruptive innovation. There's Horizon one, horizon two, horizon three.

Alistair Croll: [00:23:02] There's lots of models that talk about this. The reality is there are several different types of innovation and there are different models for how to judge whether those are working. So if you just say, that's my innovation group, bad things are going to happen. If on - pardon me, if on the other hand, you say I've got sustaining innovation, adjacent innovation, disruptive innovation, discontinuous innovations are different groups, I'll tell you what they mean in the sec. Then you can allocate the right kinds of people in budget, each one. And that's a lot like if you have finances, you're managing, you know, your stock portfolio and your 401k or RRSP or whatever. Most investors understand the idea of have some stuff in savings that's low risk, low return, have some highly volatile, safe investments and so on. But for some reason, we forget that when we start looking at innovation in a company. And so sustaining innovation is just doing more of what you said you do. The CMO of Coca-Cola, Sergio Zyman, had this great line defining marketing, which is just sell more things, to more people, for more money, more often, more efficiently. Which is if you if you're a little concerned about globalization, that's probably a lousy way to look at the world. But sure, I'll think that definition - that would be sustaining innovation. Right. Mercedes Benz comes up with the car next year. It's just like this year's car. A little more black. It's got more cup holders, but it's pretty much a car. And 70 percent of an organization's innovation efforts should go into that. Right. Better marketing campaigns, whatever. Adjacent Innovation is, we change either the product or the market or the go to market method. So if I'm selling cars and I sell those cars direct to consumer instead of through dealerships, I change the method. That's adjacent innovation. It's pretty easy to understand. Change one part of the mix, right? If I say I'm going from from internal combustion cars to electric cars, just change the product.

Alistair Croll: [00:24:50] Right. That's also adjacent. If I change more than one thing at a time. And I know that by selling this new thing, I'm going to kill the old thing - that's disruptive innovation. And I hate the term disruptive, unless you can tell me what you're killing. So Mercedes Benz owned Smart Car. But they also own car to go, which was the OnDemand driving app. The more people use car to go, the fewer people buy Mercedes Benz.

Alistair Croll: [00:25:17] So that would be an example of disruptive innovation, because I can see that this car rental service is disrupting traditional car ownership. Also, it's a different buyer. It's a different target market. Somebody doesn't want to buy a car. It's a different model - in paying per kilometer versus buying the whole car. Right. Different kind of car. It's a little smart car. So that's a good example of disruptive. And then discontinuous innovation is like the self-driving car. I don't know what the world's going to be like. You know, I'm going to expected to get some video calls during my commute. People. My daughters give me a they let you drive.

Alistair Croll: [00:25:48] When did you shave? Because they're all shaving in car. I don't know what it's gonna be like. Right. And so the thing I think but companies have to think about is - I said think too many times - is.

Alistair Croll: [00:26:00] How do I manage a portfolio of sustaining adjacent, disruptive, and discontinuous innovation with different metrics for each one? Because some of those metrics are like, how many assumptions have I validated? And some of the metrics are like, what's the ROI on that ad campaign? And if you start asking an early stage, you know, fledgling car to go company to tell you your margins per vehicle, like they're just thinking about business, there's no way. But we try to apply a consistent mathematical like analysis across them. So I think big incumbents need to think about innovation as a portfolio and they need to assign different people on different budgets and different success criteria to each of those portfolios.

Harpreet Sahota: [00:26:38] So these big incumbents, they typically hire - you mentioned innovation departments or people to work on their innovation team or their innovation lab - would this constitute the concept of an intrapreneur.

Alistair Croll: [00:26:49] Yeah,

Harpreet Sahota: [00:26:50] Yeah. What does it mean to be an entrepreneur? Like, what are the qualities or traits that an entrepreneur has? And maybe just talk about how a data scientist could cultivate these qualities within themselves.

Alistair Croll: [00:26:59] So I think intrapreneurship is a weird term because an entrepreneur is just someone who is undertaking something right. Like an entrepreneur is a hair salon designer. And so if you're an entrepreneur in an existing business, that doesn't tell me much. Of course you're entrepreneur, right. I think that the idea that intrapreneurship tries to capture is somebody who's building something that's not the core business. And there's an old slide I have about this that says if you're gonna be an intrapreneur, you'll be a pariah because like, imagine that you work at a big company, but you're the kind of person who wants to go find the big new thing. So what do you like? Well, you're always questioning stuff. You can't stop talking about your new idea. Your you hang out with, like, the people who aren't really the good customers. Like, if you think about Amazon, they were talking to CTOs and startups. Meanwhile, you know, Carly Fiorina and Michael Dell from HP and Dell were out golfing with the same old clients, the CIOs of big banks.

Alistair Croll: [00:27:48] So you're hanging out with people that everyone's like, why are you hanging out with them? You you definitely know anything that excites you is going to put someone else out of work. These are horrible traits I wouldn't have with a person like that. Right. So you're going to be a pariah and you need executive air cover from that. And I think that's also true for data scientists. Data scientists job is either to confirm and to report on known unknowns. So like what were sales this month, that's a known unknown. I can - I know I don't know sales month, go run the report. For a data scientist to become an intrapreneur. They have to transition to the unknown unknowns. Like what don't I know? Go look in the data for patterns. Hey, I noticed this thing. Well, this is an interesting pattern. Great story from Lean Analytics. There's a guy who started a company called Circle of Friends, had millions of users. It was like Facebook, for groups before Facebook had groups. The problem was nobody was using it. They'd see it create an account for their church group or their baseball team or whatever, but nobody would come back. And he was pretty frustrated and he was going to shut the platform down. And any it looked and he found there was one demographic group that did all the things he wanted.

Alistair Croll: [00:28:51] They interacted more in long threads. They had longer conversations. They clicked on links, all that stuff. Turns out to be moms. So he just went through the data. He's like, oh, look. He's like, what do all the people here's a known unknown. What do all the people are? Sorry. Know what? All the people who do what I want have in common. And he went looked and look, they're all mothers. Change the name of the company to circle of moms from circle friends and you go to your board and saying I atomically users, I want to switch to the segment that only has half a million users and they let him do it. He grew it to four million very engaged users sold the company. That's the making of a data scientist who becomes entrapped an intrapreneur is they go and look at the data and say, what can the data tell me? That I don't know. Right. And it means taking a step back from like, how many moms do I have? Because you don't know you that you care about moms yet saying, is there a group that is doing the things that are interesting to my business model more? And then once you've figured out that group, what can I do to change my business model to capitalize on their support?

Alistair Croll: [00:29:49] And I think if you do that, then you've if you move from the known unknowns, which is just running reports to the unknown unknowns, which is go and find me unexpected facts and the data that I can turn into hypotheses and test then become an intrapreneur. 

Harpreet Sahota: [00:30:02] Ok, so its kind of a combination of being willing and able to talk to different groups within the organization and then taking in that information to then think of questions that are a bit left field or out of the box.

Alistair Croll: [00:30:14] And if it's something that's obvious, someone's already using it for business. If you and again, this is why if you want to have that startup thing where you're searching for new businesses, do this. But you also need to cultivate a few high powered friends because those people usually don't last long. If they find awesome, awesome things no one doesn't see.

Alistair Croll: [00:30:32] You need to you need the need the air cover of a big brother and the executive team somewhere. 

Harpreet Sahota: [00:30:37] Kind of thinking about a one man data science team who's in charge of, you know, building up a datasets practice within their organization. How could they use the philosophies of Lean Analytics in pursuit of, you know, an intrapreneurial journey?

Alistair Croll: [00:30:51] So we - Ben and I - talk about five sort of stages or maturity levels of lean analytics and our framework. First is empathy, which. Just know your customer, right? The next day, just stickiness. Which is if you get someone to sling, will they come back? Do they keep using it? And in the case of a one time purchase - did they like the thing? Did they use the product?  Did they send it back? Then the virality stage, which is like, do they tell a friend? And there's no point to doing virality until it's sticky because otherwise you tell your friend and they agree that it's crappy. Right. You got to get the stickiness working. Then you get into revenue and price, which is like I'm now making a surplus of money. I can pay for a SalesForce. I can pay for ads, something that will artificially generate stuff. And then finally scale, which is how you actually grow the business. And we stole some of the ideas from Dave McClure who to who has this thing called pirate metrics. If you haven't read pirate metrics, it's a very interesting concept. He talks about awareness, activation, revenue, retention, and referral. So the reason it's called pirate metrics is that that spells "AARRR". And then the second one is Eric Ries talks about three engines of growth in Lean Startup. So there's the stickiness engine, which is people keep using the thing.

Alistair Croll: [00:31:58] So his point is that growth comes from the action of your customers, if you have to be the one doing all the growth, you're not really sustainable. Remember, the goal of a startup is to find a sustainable business model. You have to get up every day and do it by hand. It's not very sustainable. So one way to make sure your business keeps growing is to make it sticky. Someone uses it and they never leave. Another way is to make it viral. Someone uses it and they tell their friends. And then the third way is to make it revenue based where you pour some of your money back in. So the money from your customers is going back into marketing budgets. In those ways, If I get hit by a bus, the company still works. Right? And so we stole those stages because of the virality, the stickiness. But I think what you got to do is look at selling and say, first of all, have I passed the empathy stage. I have a good understanding of a known unmet need with a market I can reach. And you say, OK, stickiness, when I deliver the thing to my pilot or my beta or my test users, do they keep using it?

Alistair Croll: [00:32:53] Do people cancel their subscriptions or is my churn reasonably low? And you have morality. Do they tell other people, do those people try it out? Do they become users? What's the ratio that happens? That's called the viral coefficient. And then you say, okay, revenue. What's the cost of customer acquisition? How much do they give me to bring in a new customer versus the customer lifetime value? Am I earning several multiples of what I'm investing in acquiring a customer. Because if I pay 100 bucks for a customer and only give me a hundred and ten over a year, I'm effectively loaning someone a hundred bucks for 11 months. I'm not a bank. That's a terrible business model. And then finally, there's the scale idea, which is how do I grow the business while maintaining my culture? All these other factors that so often fall by the wayside as a startup grows. If you don't understand as a data scientist, which stage your business model or group is in, you will be focused on like scaling before you found the right product, or you'll be trying to get word of mouth to go before - and never will show up.

Alistair Croll: [00:33:50] And they won't like it because because it's not a good product or whatever. So I think those those stages are a really good way to get people to think through, especially if they like a one person team where, you know, that way they can be the guide to the rest the organization and help them understand what to focus on at the time.

Harpreet Sahota: [00:34:07] Kind of manifests itself in a way where you're developing a data product that is solving a lot of people's problems and has wide enough kind of robustness that that data product can be used in other different silos within the organization. Right.

Alistair Croll: [00:34:23] Yeah. And I think it's important to note that right now we see two big communities for A.I., I'm working with Georgian on some events for them.

Alistair Croll: [00:34:32] There's a post that came up for Andreessen Horowitz last February talking about how the A.I. Industry looks a lot like the professional services business. They're offering to one half of the AI industry. When I use Instagram and it suggests things for me in the feed, there's machine learning running in the background. That's all data science. But I don't have to do some big integration with it. It just works. Right. And then there's somebody who's selling a fraud detection algorithm to a bank. They spend four months talking to the banks data science team about governance and compliance and PCI and all these other things. So half of the data science and A.I. companies are like professional services businesses. Long consulting engagement upfront, lots of needing the customer to work with you side by side. And the other half, the applied A.I. and data science, are happening within the products. And I think that's where it's much more interesting to work right now because you get instant feedback.

Alistair Croll: [00:35:18] So you may be at a bank, you know, building some A.I. that detects when the user accidentally tried to transfer some it didn't mean to. There's a reason for the user to know that anything's happening is works. Right?

Alistair Croll: [00:35:30] We use auto correct and Google search and all these things every day. That's a I am using it right now, but.

Alistair Croll: [00:35:37] So I think it's really important to distinguish between A.I. driven products that you make and selling A.I. and data science to a customer where you're doing a lot of the consulting.

Alistair Croll: [00:35:49] The latter is still pretty boring, to be honest.

Harpreet Sahota: [00:35:52] What would you say is the hardest stage for data scientists to overcome in that journey.

Alistair Croll: [00:36:01] That's a good question. I mean, honestly, when you start to get into revenue in price is when it becomes all of a sudden you have to talk to someone an F in their title, chief financial officer. Right. And that person is immediately gonna go, oh, did you know what Bob was working on? I don't know about that. That's really weird. It's kind of competing with.. All of a sudden you hit the Department of NO. And I think that that's one of the big challenges for of people is how do I manage the change as I need money for it. You can test scale and you can test empathy and just test formality very early on pretty easily. It's only when you get later into the game and need the approval of someone, the business title that you find out that nobody actually loves you.

Harpreet Sahota: [00:36:39] So switching it up completely here. I thought this was fascinating when I was doing some research on you. That into something called music science. So I was wondering if you could talk to us...

Alistair Croll: [00:36:48] A little bit. Yeah.

Harpreet Sahota: [00:36:50] Talk to us a bit about that. Talk to us about about music science and how you think data science can impact it or, you know, the intersection...

Alistair Croll: [00:36:56] Sure. So a few years ago, I wrote a - what started out as a blog. I tend to do this a lot. I just write a thing and then, oh, crap, I wrote a book. So I wrote this - I've just been reading this 70 something page document on future events because I broke my leg and that's all I had time to figure out. Music has changed dramatically with whence it became digital. Every industry, when something becomes digital, obviously the energy changes completely. Well, music is no exception. Now we all have digital audio workstations. We have tools like Ableton where, you know, if garage band ships but by default on Macs. It's the idea of digital music means that anybody can put together pieces like that. It also means that you can analyze that, whether that's Shazam, you know, plucking a baseline out of the air and telling you what song it is in a loud bar, which I still think is magic. Like, I still have no idea. That's Harry Potter stuff right there. Then you have the ability to analyze stuff. You know, I grew up, I'm old, so I grew up in a world where if I wanted the song. I'd sit by the stereo for an evening. My finger over the record button, and press record right. Today, like my daughter wants the same thing. She sends me a Spotify, like I'm done. That's weird. And with that came the ability to track a lot of the stuff. So I wrote this. This 80 page document. It was a lot of fun because I wound up getting to interview a lot of my heroes at Spotify and Pandora and stuff. The switch from analog to digital music has had incredible consequences because now you can measure the spread of songs and I can go into lots of these things. But I'll just tell you one very funny story. There's a wonderful technologist in England. Her name is Kate Reardon. She's now the CIO of the Financial Times. She manages 400 people at F.T. And she managed to get them to profitability or to their subscription break even a year before they planned it.

Alistair Croll: [00:38:34] She's fantastic.

Alistair Croll: [00:38:35] She was the Head of Product at Shazam. Shazam, for those people who don't know it, you press a button. It tells you what song is playing. Even in a crowded bar, it's magic. So when Kate was running this product, one of the things that they can do is they can now find out, you know, it used to launch a track and you find out within a few months if that was doing well. People would call the radio station and say, hey, were there requesting a lot of that ABBA song, I guess is doing well in the ABBA timeframe. Katy Perry and Lady Gaga launched a song on the same day in Europe, and within four hours, the record label knew which one was going to be successful because they could see which of the two songs people were Shazam-ing them most. So they took a signal that used to take weeks and they collapsed it to hours. And of course, that becomes a self-fulfilling prophecy because the album - the marketing team takes that early start and throws money at it. And then all of the sudden that one's doing better. Right. It turns out there's some other weird side effects because you can tell where in the song someone Shazams. So there was a song by a band called Clean Bandit that is a very unique cello sequence. At the beginning. That was one of the most Shazam songs. But you could tell that was when this cello riff started. And because of that, artists all started putting distinctive riffs into their song. And in fact, I vividly remember Kate onstage playing the song for us. It's a rapper from the UK and he goes, baking powder.

Alistair Croll: [00:39:46] I got baking powder. And then Kate pauses the song and in a very proper British voice goes, He's not talking about baking powder. They know exactly when you hit Shazam. So there was like a collaboration. She goes it turns out Nicki Minaj was of the five artists, the one people listened to the most. That's a level of granularity we never had right at scale and speed. And so I think what's fascinating is that music went digital and as soon as it went digital, everything was grist for the analytical mill. So I'll leave you one quick story on this. There is a company called Arbitron back in the golden era of broadcast. If you wanted to listen to the radio, you've turned on the channel. You wanted list of music. Musical advertisers wanted to know who'd advertise to, so they wanted to know who is listening. And so Arbitron came up with this thing called Listening Diaries and Listening Diary. It's just a little book. And you write down in the book like what you listen to as they go to a Hispanic man in L.A. and say, please write down what you listen to every 15 minutes. So we know and those men would break this down in the mail, them back in every quarter, and then Arbitron would collect the data and they go, oh, you want to reach Hispanic men from 30 to 45? You should use this radio station. Well they go, hey, this radio station, the ad should cost this much because this many people listen to them. Right. Everybody is happy. Problem is, everybody is lying. These groups would actually fill out what they'd listen to on a Saturday morning and then mail them.

Alistair Croll: [00:40:59] Because they didn't during the week, which meant that the radio stations learned that they could do that weird call it. You're listening to K.R.O.C. - K Rock. They did that on Friday because they knew it would be on your mind for Saturday. Radio stations would also run those little dialing contests at exactly 828 in the morning because they knew that people who are listening from 15 to 30 would stick around for me, 30 days, 45. And that was the most lucrative segment. Somebody was lying and everyone's happy. And then in around 2012, Arbitron came out with a little device called the Personal Performance Meter, or PPM. And it's a little brick that fits on your hip, like a pager. And it listens to whatever is playing. You may not know this, but broadcast radio in North America and I think elsewhere in the world has a tone in it that you can't hear. But this machine can and it can tell what radio stations listen to. So you walk into a mall playing country music with one of these things on you're a country western fan for the next 15 minutes. In 2012, a song that was in what's called heavy rotation. You know, the labels are pushing it. It's popular, was played on average 5.5 times a day in 2016. The song is played a little over once an hour. Yeah, it went from 5.5 to around 26 times because we now know what song makes you change the channel and what song makes you stick around, because we know what station you're listening to and when you change the channel for this little device.

Alistair Croll: [00:42:10] So you might think, oh, this device is neat because now I get the data back, you know, faster or nobody's lying. But the real impact is I now know what made you change the radio station. So in the old days, you had an album that kind of came out. There are a few tracks. Slow Burn got popular. Now it's like new Katy Perry song is on every day until the day. It's not because we've decided to change the channel like that. One thing completely changed the nature of of broadcast radio, just being able to find accurate information on what you listen to and we don't notice it. But, you know, radio today is very different from radio 10 years ago because of technology. It's just capitalism. We want to play whatever keeps you on the radio. Broadcast radio is doing great. I don't listen too much, but it's doing great.

Harpreet Sahota: [00:42:48] Yeah. Now, don't listen to it all. I think Spotify is amazing. Their API has so many interesting metrics that you can collect just based on their own listening history. It's super fascinating.

Alistair Croll: [00:42:58] Oh yeah, I. I need music that doesn't have words when I'm working. Otherwise I start typing the words. And I used to deejay once upon a time, and wish I still did. So, you know, having stuff like that, if I find stuff that's like Spotify radio, I just put that on and work for two hours. Great.

Harpreet Sahota: [00:43:13] What's your go to music?

Alistair Croll: [00:43:15] There's a guy named Ben Bohmer out of Germany who's fantastic, B-O-H-M-E-R. There is gonna Max Cooper. He's a former engineer who makes amazing sort of ambient electronic stuff and underworld. If I'm gonna listen to anything, I'm feeling nostalgic.

Harpreet Sahota: [00:43:31] Very nice. Yeah. My go to during work hours is typically either instrumental Math rock or a low fi study music. And this shows just how granular genres have become now.

Alistair Croll: [00:43:42] I mean, even the idea of lo fi, how a straight like the idea that you can throw in some deejay Seinfeld, it's like house music, but someone's got in their mess with filters that would have been hundreds of pedal's. And, you know, now it's just like someone clicking options on stuff, so.

Harpreet Sahota: [00:43:54] Yeah. So shifting gears again, and I've got a lot of up and coming data scientists who are part of my audience here. When people are transitioning into the field of data science, they tend to focus primarily on just the hard technical scales, and they think that that's what's going to really separate them from the rest of the crowd. What would you say are some soft skills that candidates are missing that are really going to separate them from their competition?

Alistair Croll: [00:44:16] Yeah. I mean, this is this is a classic problem. And I don't want to draw too many stereotypes, but it is often when somebody looks at things objectively, they have a hard time understanding subjective reasons. I have a little voice in my head that I listen to enough that goes, hey, you should probably just do that thing that everybody's telling you to do. And I like to be the guy that's like, oh, no, I found the right solution. It's awesome. It's the thing that solving the problem. But the reality is sometimes -  there's an old line from The Simpsons, you know, sometimes Lisa things are too hard to do, they're just not worth doing. There's a bit of truth in that. Homer's is a little bit of genius there. And I think the reason is you've got to pick your battles and know which hills you're willing to die on and be willing to compromise the others. I think that's something that a lot of technologists forget about, is that you've got to know what you're willing to stake your claim on, what you're willing to fight for. And then the second thing is try and understand the customer journey. There's a lot of I know that sounds kind of like platitude, but like I'd been evaluating a lot of Web conferencing solutions for virtual events and they're all great.

Alistair Croll: [00:45:24] And I have a long list of features and I have zoom and big marker and click meeting. And there's so many of these tools. Right. And they all do stuff and some don't do things. But what really matters is when you step back and go, OK, how do I run my event? What's what are the steps that users gonna go through? And then how do I use those steps to make sure that there's a satisfying experience - from the moment they get an invite, to they attend the event, someone follows up. And it's easy to say, hey, you know, choose a tool. It's the most powerful. Don't underestimate ease of use. There's a reason why when we're speaking on stage, we tell people to think about edutainment. Because if you're not entertaining, no one is going to listen. So you got to first seek to engage and entertain and then you have the ability to inform people that document you wrote. Make it a quarter of the size. Those PowerPoint presentations you made that have 10 bullets on them put one bullet per slide, so people don't read ahead. There's just some basic stuff like that. And it's a combination of communication, empathy and realizing -

Alistair Croll: [00:46:21] And this is something that took me a long time to deal with. I was always taught that clever is better. No, we evolved to get the approval or try not to be right. And you have to understand that there's a great book by Jonathan Haidt called The Righteous Mind that talks about moral reasoning and how people make decisions that I just love. And I think that's one of the big lessons, is get inside the brain of your audience. Don't be afraid to dumb down the things that you're not willing to die on. And then, like, pick the ones that you really know and say, listen, everybody, this is the one thing I really need you to understand or I can't have the rest of the conversation. I'm going to make it simple, but I can't make it simpler than this. Here. You get their attention for five minutes. Make it entertaining. Use good analogies and then go - Okay. Now you understand that we can talk about this until everyone's on the same page. They just view you as very easy. People turn the volume down and go, oh, here he comes again. He's going to tell someone much acronyms. Right. You don't want to be that guy or that woman.

Harpreet Sahota: [00:47:11] That's really good advice. Yeah. Actually, I've read your book. It's in the mail right now. Propose Prepare Present. What are some key takeaways from that book that you think a data scientist should apply when communicating with non-technical audiences?

Alistair Croll: [00:47:25] We originally wrote that because we wanted to improve the quality of the call for proposal submissions, we were getting for Strata. And it's been an interesting book. It's I - I did it as like an 80 page PAF originally. It's I think the key is that conferences have a lifecycle.

Alistair Croll: [00:47:40] So in the first year that I ran Cloud Connect, everyone was a ohh clouds, what are clouds? Right. And so I got Verner Vogels, who's the CTO of Amazon and his counterpart at Microsoft, Google, and RedHat. Three other companies you may have heard of their CTO's is all showed up. Because I asked them, right. Today, I mean, Verner's a nice guy, but he'll be like Allaster, I'm busy. Go call someone else. In the early phase. People want to know, like, what is this thing? What is cloud computing? What is data science? What is A.I., what is Web performance, whatever. Then over time it becomes OK, how do I use it? What do I not use it for? So like we don't talk about cloud storage. They will say storage. Right? There's computing. We don't really distinguish. Whereas once upon a time there was super important. And so the conferences go through this five to seven year cycle where at the beginning there's a conference that's like about data science and it either tends to be going into vertical industry. So you may have a data science track in a retail conference or a data science track and a health care conference, or it goes to the major vendors, so like Dreamforce or Amazon's cloud computing event and so on.

Alistair Croll: [00:48:37] Usually one of those two things happen. And so knowing where you are and then narrative. Am I at the what the hell is cloud computing stage of this conference or am I at the challenges of setting up containers with Docker in a multinational environment? Am I at the what is data science or am I at the how do I use reinforcement learning to improve data cleaning in an in-memory database? If you submit that at the wrong cycle of the conference, nobody wants it, right? Yeah. You're two years too early or two years too late. And so reading the room, like knowing what's the narrative? Where's the where's the conference age? I think is a really important part of that. And it's the same thing as reading the room when you're pitching internally. But I would say that's the biggest lesson, is to understand where in the lifecycle the audience is and that affects what you propose and what kind of content you put on stage.

Harpreet Sahota: [00:49:27] Thank you for that. Let's talk a bit about being evil. You say start-ups should be more evil, that sounds terrible. What are you thinking? What are you trying to communicate with that?

Alistair Croll: [00:49:36] Well, it does sound evil. So I'm working on a book called Just Evil Enough based on some talks I've been doing for the last little while. Well, here's the short version: We live in what Herbert Simon called the attention economy. Pay attention to Facebook. Right. If you're going to try and capture attention, you need to do so better than the competition. When I do this talk, I ask founders in the room to put up their hands if they are like more concerned about whether they can build a thing or whether anyone will care. And they all put up their hands and say, I'm more worried that nobody will care. And I said, okay, what do you spend your time on? How much time do you spend working on building the product versus getting people to care? And none of them spends time on getting people to care. If I were investing right now, my number one question, we why what are you going to do differently that gets everyone to care? Dropbox had this great thing where, like, I invite your Dropbox, you both get some storage. Gmail had artificial scarcity. Right. Farmville had this thing where they found basically a vulnerability and exploit and Facebook, Farmville - you install the app it starts posting to your friends feeds: Hey, Alistair, need some grain. Facebook shut that down.

Alistair Croll: [00:50:39] But not before Farmville had 30 million users. So in the world of computer security, there are script kiddies who run known attacks to get, you know - if you didn't patch your WordPress, all of a sudden you're selling Viagra. And then they're like zero day exploits, like nation state level stuff where they spin up a centrifuge in Iran, for example. I'm arguing that every startup that wants to succeed has to find a zero day attention exploit. They have to find something that gets a system they use to behave in an unintended way. Just like Farmville was able to use Facebook feed in an unintended way to grow. And you have to - like Tupperware was a great example of I've turned to the dinner party into multilevel marketing system. And when you look at successful companies, almost all of them have something like this in their closet. A lot of them don't want to admit it, but they have something like this in their closet. And so I'm arguing that you don't want to be evil. You're just evil enough. And that's obviously a way to get people to pay attention to title, which is part of the purpose of the book. But what you should do is find a way to capture attention.

Alistair Croll: [00:51:41] You can turn into profitable demand better than the competition. That idea means looking at what happens in your market and how it's framed and so on.

Alistair Croll: [00:51:53] You know, for years we've been selling electrical cars as really good for the environment. And, you know, we're relatively clean and stuff like that. And then, Elon Musk goes no - they're about going faster. So he reframed the conversation around electric cars to make them by going faster.

Alistair Croll: [00:52:10] That's cool. How do you reframe an existing perception so that it advantages you? How do you trick, co-opt a platform and get it to do something it wasn't intended to? The closest thing you can find to those kinds of that kind of thinking is hacking. Like the in the sense of hacking, getting something like a hack is when you know that that hack that you can make a phone holder out of a pop bottle that goes on your life hacks. Right? It's just getting something that wasn't intended to be used in a way to do something for you. And I think that startups have to have that subversive mentality of how do I subvert the system to get it to do something that wasn't intended.

Harpreet Sahota: [00:52:53] What's up, artists? Be sure to join the free, open mastermind Slack community by going to bitly.com/artistsofdatascience. It's a great environment for us to talk all things data science, to learn together, to grow together. And I'll also keep you updated on the open biweekly office hours that I'll be hosting for our community. Check out the show on Instagram @TheArtistOfDataScience. Follow us on Twitter @ArtistsOfData. Look forward to seeing you all there.

Harpreet Sahota: [00:53:23]  What's the one thing you want people to learn from your story?

Alistair Croll: [00:53:26] I'll tell you one thing I'd like people to learn. My philosophy and my company, if it's me, is called solve for interesting. All my life I have seen people solve for lots of things, solve for fame, solve for risk, solve for profit, whatever. All the good things that have happened to me come from solving for interesting. And I think the reason for that is, if we live in an attention economy where we are literally paying attention to things. Right. Because we have too much information. Information consumes our attention. What do we pay attention to? We pay attention to whatever is interesting. And so the one thing I would say is if you can find a way to solve for what's interesting about your company, about your life, about your hobbies, whatever, you will thrive in an attention economy. We spend too much time solving for risk or fame or profit. And it is amazing what happens when nobody cares who gets credit. And so I think if people can think a little more about how to solve for interesting than they will do much better at life.

Harpreet Sahota: [00:54:24] I like the philosophy. Now, solving for interesting..Does that mean innately interesting to you or does there's some element of it having to be interesting for other people as well?

Alistair Croll: [00:54:33] I think we get back to the startup entrepreneur thing there. You have to have the faith that what you find interesting, other people are going to find interesting. I watched a video on The Royal Game of Ur last week, two weeks ago. And I was compelled to write this. So I went down a rabbit role of like game theory and randomness and all this other stuff. Literally had friends of mine running Monte Carlo simulations of probabilities. And it turned into this eulogy for Conway Pressor Conway, who invented the game of life. I didn't know where I was going to go. I just knew that I was nerding out about it. And I was like, I wrote this thing but it's weird. And my friends are like Oh my God, I know this stuff a game and randomness. So I'm going to give the talk. Next week we're doing an Ignite Shelter In Place. Ignite is this five minute talks kind of model. I'm gonna do a talk on it. I didn't know. I just wrote cause I thought was interesting, like I published it because, you know, publishing's free.

Alistair Croll: [00:55:25] I wasn't arrogant enough to assume people liked it, but I put it out there and I would do a check. I'm not an idiot. I'm gonna go look at which posts do well. I'm gonna, you know, lift those posts up. So I think it requires both of those things that an entrepreneur needs. The the faith or the confidence to think that what you you're interested in is interesting and the hubris to check with other people and take their feedback, you know, to heart.

Alistair Croll: [00:55:50] I think if you do both those things, then you'll be fine.

Harpreet Sahota: [00:55:52] I'm looking forward to that talk. That does sound like a really interesting concept that you have there. So let's go ahead. Just jump into a quick lightning round here.

Harpreet Sahota: [00:56:00] What would be the number one book, either fiction or non-fiction or both that you'd recommend our audience read and your most impactful takeaway from it?

Alistair Croll: [00:56:09] So I think I mentioned it earlier, but there's a book by Jonathan Haidt called The Righteous Mind, and he's done a couple of TED talks on it. But he spends a lot of time talking about how humans make decisions, and it's backed by pretty good psychological research. And it is fascinating to think that the human brain is more like a parliament.

Alistair Croll: [00:56:33] We have a bunch of noisy voices and then we convince ourselves that's what we wanted to do all along. So our consciousness is pretty much a narrator that's making a story out of all the crazy stuff our body decided to do. The best line in that book, I think, is that is this line about being right. We didn't evolve to tell the truth. We evolved to be right, because that's what meant that our tribe would protect us from the saber tooth tigers or care for us when we're sick. And if we think we're not doing that well, just go look at modern politics.

Harpreet Sahota: [00:57:01] Yeah, I didn't find myself reading a lot of books on just try to understand the nature of the human mind in like neuroscience and brain science type of books. Would you say this kind of falls into that kind of category?

Alistair Croll: [00:57:11] This one doesn't get so much into the technology, does talk about behavioral psychology, a little cognitive bias, that kind of stuff. It's gets a good read.

Alistair Croll: [00:57:18] It's a lot of fun

Harpreet Sahota: [00:57:18] Awesome, will definitely check that one out.

Harpreet Sahota: [00:57:19] If we could somehow get a magical telephone that allowed you to contact 18 year old Allistair. What would you tell him?

Alistair Croll: [00:57:26] I would probably tell what stocks to buy, but that's that's a whole other story. I think that one of the things that's taken me a long time to learn is how to how to cultivate a personality. I'm not - I'm still very uncomfortable in my own skin in some ways. And I think that we have got this world where we have an idea of people who are very self aggrandizing, the social media influencers and gurus and all that stuff. Right. That immediately puts a bad taste in my mouth. But at the same time, I have this job where I get to either get on stage or get other interesting people on stage, and it seems to go pretty well. So I think I would tell 18 year old me to figure out how to cultivate a personality that's public and fairly strong, even though some people are going to hate that. If people and, you know, people, I say this line about if if if everybody agrees that you're not taking strong enough positions. But I think that in the modern world, branding is such a personal currency that even stuff like getting your own sense of personal style, I probably tell myself, you know, exercise more. Get a sense of style. Those would be good things to start with - he says in a vest that were broken leg. But I think that I'd be able to cultivate a personality in public. Don't be afraid about alienating people. Don't be a jerk. But, you know, pick who you are and build that brand because over the next 20 or 30 years, 18 year old Alistair, the world's going to change a lot and those kinds of currencies are going to matter a lot more like that.

Harpreet Sahota: [00:58:45] I like that phrase you said there. Cultivating a personality. Do you mind just kind of digging a little bit deeper on that. What does that mean to you? To cultivate a personality?

Alistair Croll: [00:58:53] Well, I think when I was 18, I was very much like I had my personal life and then I have my work life and you can do that. But the things about your personal life can make your work life more interesting. Like, I wouldn't hit on the thing on music science if I didn't like music a lot, spend a lot of time deejaying nerd out on that kind of stuff. I think that we have a lot of, ,not to get to sociological, but I think we have a lot of issues on shame. Especially in the Western world. In the last 10 or 20 years, it's been much easier to come out. If you're, you know, 18 year old Alistair was living in a world that was like very cis, hetero normative, didn't have a lot of options, didn't have a lot of friends who didn't the same skin color and language and live in a world now, especially with social media, where there's many more people bumping into other people. In some cases we find that we don't like each other, which is one of the reasons why politics are where they are. But, I think that I would've told myself to be myself a little more and like be a little larger than life, as long as that wasn't a jerk. Because it turns out people kind of like that. You know, if you're the guy that was the the sparkly disco jacket to a party, that's all right. You break the ice a little. And I think I think it's OK to be that person or be our person instead of just trying to fit in.

Harpreet Sahota: [01:00:07] What's something you've done at one of your ventures that's been just evil enough?

Alistair Croll: [01:00:14] So Coradiant made these appliances the biggest one cost like 100 grand and we would take them into data centers. And everybody in this, if you're in the appliance industry, you usually have your appliance in a big road case. You wheel it around for tests. People plug it in. They do a demo, whatever. And I remember going to this data center and we had spent and these boxes cost like one hundred thousand dollars. We got to buy. Right. Cardboard box printed on like 20 bucks. So we got a nice cardboard box. We got it printed in two colors with our logo. Very like packaged properly. And we would send these things out for demos. And we if they came back, we just put em in the lab and use them. That tells you we were not getting many of them back, which was good. Our product went in in like 50 minutes. Everybody else took like two months. New to go talk to marketing. We just plugged in. It was. And I remember walking into a bake off against some other vendor and our sales guy showed up and I showed up. I would go on a lot of these customer calls, the sales team and the competitor's product was up. And he comes in a row case gets stickers all over. He pulls it out and we pull our box out. And it's like this beautiful thing. The cardboard cracks as it opens. There's a screwdriver fit inside, in case you forgot one, because that cost the dollar and the customer looks at it goes, wow, I guess I got a new one. And the sales guy next to us, the competing sales guy, kind of laughs. And I go, yeah, everyone gets a new one.

Alistair Croll: [01:01:40] And he goes, What do you do with the ones that get sent back? I go, Nobody sends a back. Imagineer that other sales guy, I just paid 20 bucks for cardboard. Right. Nobody thinks about that. But it turns out that when you're on boxing stuff and you're used to like shitty road cases and knots of cables and I don't wear the screwdriver is in this crisp, beautiful box, shows up with a screwdriver and it goes in in 15 minutes, you know, send it back or you're going to send the other guy out of there. And he looked so sad. This poor sales guy is just like I've lost already because of the box. Is that evil? Sure. But it was fun as hell. That's a good example. You wouldn't have thought that the unboxing process was part of the sales process. But when you spend enough time with a customer, it turns out it is. And then you co-opt that process to own a screwdriver, make it look nice. It's pretty funny, right? And it's funny to talk about it. Now, I'm sure that sales guy was pretty mad. We closed the deal, $105,000 that customer. POC took three days. Once we figured out how to sell the product, we did pretty good. We were toiling in obscurity for like seven years. And then in the same three month period, LinkedIn SalesForce, and Fidelity all bought our biggest box.

Alistair Croll: [01:02:43] And I was like, okay, now we have a company. 

Harpreet Sahota: [01:02:45] That kind of speaks to like a little kid in all of this. Like, you know, they're always happiest playing with the empty box not the cool little toy that...

Alistair Croll: [01:02:52] Absolutely, people are like cats playing with the box.

Harpreet Sahota: [01:02:57] What's the best advice you've ever received?

Alistair Croll: [01:03:00] I will credit this to a sales person that we worked with. The language is a little colorful. So I apologize in advance. I will tell you it because it rhymes and then I'll explain it and I'll give you a better word. He said, Alistair people do things because they want to get laid, made, or paid. And I said, you forgot one, which is they also want to get unafraid. One of the best things that I like to think about when marketing stuff is what's the motivation of the buyer laid - And again, I'm using the air quotes, you can't see here - because it's an unfortunate term. I don't want over sexualize the process. But this is about being attractive. It's being be perceived as attractive. So if you go to an AI conference, like I went to the AI Con, I'm the AI guy or person in this company. Right. Made is like a made man in the mob. It's referent power. So if you go to the AA conference now, you know all about AI, and people don't, you're going to win arguments in the meeting. You're going to maybe manage the A.I. group. Paid is like I got this thing and I got money for it. Right. So there's power, attractiveness or like peer approval and money monetization. And then I think people do things to get unafraid.

Alistair Croll: [01:04:11] So, you know, reduce risk. And the reality is that if you're trying to figure out what you're campaign is. You figure out who the person is to say what they want. We want them to do. And then you say, how does this how would I break a tag line or call to action that makes them feel more attractive? That makes them feel more powerful. That makes them feel wealthier. Or that makes them feel less at risk. And that is an amazing checklist. Every time I'm writing marketing copy, I'm like - And you don't mix them, right? I love you, but I'll kill you if you leave is like laid and afraid. That's really bad. So you've got to figure out how to come up with a campaign around each one and then test them. But I love that way of thinking. That's just a checklist for how humans make decisions. What's the real motivation? Is it laid, made, paid, or afraid.

Harpreet Sahota: [01:04:58] So what motivates you?

Alistair Croll: [01:05:00] I think I mentioned earlier it used to be being clever. Clever is very arrogant, but I was definitely raised by teachers who were like, hey, good job, really well done. Nowadays, I would say it's solving for interesting. It's finding that interesting thread and pulling on the sweater unravels. You're like, oh my God, I had no idea.

Alistair Croll: [01:05:19] This is there. There's actually some recent scientific research that shows that some percent of the population have a genetic difference that causes them to experience pleasure, under MRIs, when discovering new things.

Alistair Croll: [01:05:33] And I think that what motivates me is finding that like feeling of I understand a thing. I just spent the last month marinating in virtual events, and I now know what I don't know, which is a whole lot. But it's great to have that feeling of just marinate in a thing until you absorb it and kind of have a sense of the world and how it works. Not necessarily a correct one, but I love that feeling so motivates me as understanding things at a deep level.

Harpreet Sahota: [01:05:59] I like that a lot. I used to use this analogy. And it was I'm installing light bulbs in the dark places in my mind.

Alistair Croll: [01:06:07] That's a really good way to put, yeah. Absolutely.

Harpreet Sahota: [01:06:10] So what song do you currently have on repeat?

Alistair Croll: [01:06:14] So Max Cooper just did a one hour long live stream from his studio. That is an amazing mix. And there's a Ben Boner song called Submission that's been playing far too often. And then there's a band called H. Her voice over bass that's out of Northern Europe, and they're fantastic, too. So usually I have whatever Spotify says goes with those.

Harpreet Sahota: [01:06:34] Yeah, I'm gonna check those out. So how could people connect with you? Where can they find you?

Alistair Croll: [01:06:39] I'm @acroll on almost every platform except Instagram. So if you're that guy, give me that handle. But yeah, people can contact me on LinkedIn. It's a pretty unusual name, Ali. Like Ali Baba stare like stares cruel like troll with a C a used to auto correct to a lister troll, which sounds like a job description, but I don't think that corrects that way anymore. So yeah, @acroll on Twitter is part of the best way.

Harpreet Sahota: [01:07:03] Awesome so, hey well thank you so, so much for your time. Really appreciate you taking time to schedule a chat with me today. It's been a pleasure.

Alistair Croll: [01:07:09] Yeah, absolutely. And thanks for doing  all the research. This is - I've talked to a few people, but I don't think anybody's ever dug that deep into all the stuff I've done. It makes me feel like maybe I haven't wasted all my time. So, yeah, I really appreciate you looking into the background so we can have a good conversation.