about the episode
Kevin Wofsy: Here we are in the room. Finally the dream is coming true. We are in front of microphones. All we're lacking right now is a name, a concept and a plan.
Kyle Wild: A reason for even being here.
Kevin: A reason for even being here.
Kyle: Even a theory.
Kevin: We enjoy each others company, and Ted has been kind enough to let us use the studio.
Kyle: Yeah, that's all we got.
Kevin: So, you and I had a conversation last year about failure. It went so well I thought it'd be awesome if we could spin this up into something more than just us sitting in a bar, recording things on an iPhone. And that's the journey. It's taken us almost 12 months to get here, from the bar to this room.
Kyle: It's been a little while. So, it's your idea that we talk about failure here?
Kevin: No, I think we should talk about a lot of things. We always end up talking about failure. I don't think the podcast should be "Failure and Nothing Else."
Kyle: Failure and Nothing Else, that's not a great name. Actually, that's kind of a great name. Failure and Nothing Else, with Kevin Wofsy. Not, "Failure is a Step in Innovation." No, failure, and then, end theme.
Kevin: Right, exactly. If ever it starts to warm up, then we have to cut immediately so that it can fail.
Let's start with some of the podcast names, maybe that'll help us riff on things. The name that we came up with, accidentally, the other day, was "Science and History for Grown-Up Smart Kids."
Kyle: That's really good. What would a podcast called Science and History...? First of all, what's the acronym? S-A-H-F-G-U-S-K.
Kevin: SAHFGUST. No, SAHFGUSK.
Kyle: SAHFGUSK, yeah. All right, that's good, that's something.
Kevin: Yes, SAHFGUSK, I mean, it is pronounceable.
Kyle: It is.
Kevin: And not every acronym is.
Kyle: Science and History for Grown-up Smart Kids. So, let's imagine, I'm just scrolling through iTunes. I'm like, "Oh, what podcast should I...?" And I'm checking out, you know, the library.
"What podcast should I...? Oh, this one, Science and History for Grown-up Smart Kids." I'm going to go ahead and subscribe to that podcast because it evokes a sense that it might have "X" content in it. What am I expecting?
Kyle: Not "What are you expecting," just you, the reader.
Kevin: The reader is expecting, I think, there's a certain degree of nerdery that comes through from that. I feel like I might learn about Stegosauruses. I might learn about the Magna Carta. It would mostly be Stegosauruses and the Magna Carta, I think.
Kyle: Yeah. I mean, maybe like 10 episodes each. Triceratops doesn't warrant nearly as much.
Kevin: I know.
Kyle: And I like that the audience is in the title, I do like that. Who's it for? Oh, it's for grown-up smart kids. It says it right there. Well, I'm a grown-up smart kid.
Kevin: Yeah, exactly. I mean, it has grown-up and kid. It's that sort of paradox.
Kyle: Aren't we all.
Kevin: So, are there topics beyond, I mean it's hard to imagine, but let's just push the envelope. Are there topics beyond Stegosauruses and the Magna Carta that we might want to talk about over the fullness of time?
Kyle: Science and history is interesting. One of my favorite subjects has always been the history of science. I'm reading this book right now, The Innovators, about the creation of the digital age starting from like the 1800s. So it's like Ada Lovelace and Charles Babbage figuring out mechanical computing contraptions, all the way through Steve Jobs. It's pretty cool.
So, the history of science as a whole, that is a rich area. Which is clearly about science and history, famous discoveries.
Kevin: Famous discoveries, now, that's getting onto something, too, that we've talked about a lot. I mean, you have a certain degree of expertise in the arena of entrepreneurship and startups.
Kyle: It is accurate to, say, a certain degree.
Kevin: I guess we all have a certain degree. I mean, there is a measurable amount.
Kyle: That's true, entrepreneurship seems to be pretty popular these days. Thinking cynically about our podcast and how we get it to traction and how do we make it into the thing that drives all of our Twitter followers. Thinking cynically, there's something to that.
Kevin: Yeah, I mean, I feel like if we're looking at the Venn diagram of what we want to do here, I feel like in one circle, we have shit we like to talk about. And then in another circle, shit that people would be interested in.
And then maybe there's a third circle, which is things that we have some degree of expertise in. So, things we like to talk about, things we have some expertise in, things other people might want to hear us talk about.
Kyle: Well, what about the things we like to talk about that we have no expertise in? Is there stuff there?
Kevin: There's a lot of stuff there.
Kyle: That's probably most of it, huh?
Kevin: That's too big for one podcast.
Kyle: Well, because there's expertise, perceived expertise, expertise we perceive that we have, and other people don't. There's expertise that other people may perceive that we don't perceive we have, and then somewhere there's like the true expertise we have, right?
Kyle: How do we sure all that up?
Kevin: You don't want to have too much expertise. Nobody wants to hear, you know, a know-it-all talk, so.
Kyle: That's true.
I learn the best from people who just learned something.
Kyle: They just learned it, so they remember when they didn't know it. So they can tell me, as someone who doesn't know it, how they learned it, right?
Kyle: You ask that same person 10 years later, they're like, "I don't know how I learned it. It's obvious, it's obvious."
Kevin: You just know it. You either know it or you don't know it.
Kyle: Novices are the best teachers.
Kevin: Right and so that's sort of why I feel like the areas in which we have just enough expertise is probably the sweet spot.
Kyle: So how about "Shit I Learned Last Week?" Is that a good podcast?
Kevin: It could be. Shit I Learned Last Week? Sure.
Kyle: Shit I Learned Last Year would be SILLY, the acronym for that. That's not too bad. Shit I Learned Last Year. But it sounds kind of dated, doesn't it? It's like, let's see, last year, geez I don't even...
Kevin: For somebody like you who describes yourself as a futurist, it is a little counter-intuitive.
Kyle: Shit I Learned Last Year, that's no good. We're back to the Venn diagram, though.
Kevin: Yeah, the Venn diagram.
Kyle: And you said, things that I have expertise in, and things I like to talk about, and things that people might want to listen to.
Kyle: So I've got my three circles on that. You've got your three circles. Future guests have their three circles. What's the overlap of all those?
Kevin: Can I throw some things into the pot? I know, as somebody who's talked to you a lot, that you get very excited when you talk about what you think of the future of business, what you think about the company that you started, why you think there are better ways to do things than the way people do them now. I like talking to you about that.
Kyle: Yeah, I like talking about that stuff.
Kevin: Yeah you do.
Kyle: I don't know if people like listening to it.
Kevin: I like it.
the cool thing about the future is that nobody has expertise.
So it really falls in the hands of people who think they have expertise.
Kevin: And that's where you come in.
Kyle: And I really think I do.
Kevin: Yeah, exactly. Maybe, really, it's not Shit You Learned Last Year, but... Shit, now I need a better acronym. Well, I still feel like we mastered the acronym for the one that ended with GUSK.
Kyle: I like GUSK. Elon Gusk.
Kevin: The Future for Grown Up Smart Kids, you know, that could be something. But that would be FGUSK.
Kyle: I always wanted to write a book called "The Future of History."
Kyle: I don't know, I just like that name of it. I've wanted to write a book called that for years.
Kevin: I'm going to push you there. When you say you've always wanted to write a book called the The Future of History, define "always."
Kyle: Maybe two or three years, since I decided I wanted to write a book someday that was not a fiction book. I got all kinds of weird Sci-Fi shit, so.
Kevin: But you define always as two or three years ago?
Kyle: Yeah, pretty much.
Kevin: Okay. That's a futurist for you.
Kyle: I guess I don't really... Now that you say it, I guess I don't. But apparently, that's what I mean when I say it. For a while now. How about this?
For a while now I've wanted to write a book called The Future of History and then fill in what that means later. I like to name things.
Kevin: Okay, so the book right now has a cover and it has a title on it, by Kyle Wild. But the pages...
Kyle: It doesn't have a cover.
Kevin: I mean, presumably, it would have a cover.
Kevin: Books mostly do.
Kyle: Maybe just black and white, Comic Sans.
Kyle: "Histories" in Courier New. Maybe it's all emoji by that point. The Future of History. It's really more about how we record history now and how we will record history later.So, the future of the practice of history.
But I also just thought the term was an interesting term. I don't know if anyone would listen to a podcast called that, or if anyone would tap on it when they saw that title. We need to be cynical here.
Kyle: This book will be judged by its cover.
Kevin: And if it's in Comic Sans and Courier New, it's not going to get a lot of buyers.
Kyle: Science and History for Grown Up Smart Kids. I really like that.
Kevin: Yeah, we do like it. It's the one to beat. You know, we have the new mission statement that we came up with: "Turn explorers into discoverers." We could do something like, "Explorers and Discoverers," you know, since discoverers is hard to say.
Kevin: We could do something around data.
Kyle: What's data? I always hear this word. I don't know it. I really don't understand.
Kevin: Informations, duh. Informations? Numbers, facts, figures.
Kyle: Yeah, that's data.
Kevin: That's data?
Kyle: Yeah, that's good.
Kevin: Here's a couple of titles I riffed on, here: "Data Discovery, Data and Culture, Data Science Storytime, Cat Shirt Studios..."
Kyle: Ooh, wait, hold on. What was Data Science Storytime?
Kevin: Data Science Storytime.
Kyle: That's... that's adorable.
Kevin: Because we like stories, but we like science.
Kyle: In a way, Data Science Storytime is the same as Science and History for Grown Up Smart Kids, in that it evokes storytime. So that might be a distillation of the--
Kevin: That was, I mean, that's my job. That's what I was trying to do. So, Data Science Storytime. Okay, I like that you twigged on that one.
Kyle: I can tell you used to work in advertising. You give a pretty good idea, then a few bad ones, and then the one you really want the client to jump on.
Kyle: Question is, which one are we on right now?
Kevin: I mean, you stopped me right before I said, "Cat Shirt Studios," so.
Kyle: And that one's put in so the client feels good about putting a red pen to draw a line through something and saying, "No, no, no."
Kevin: You know all my tricks. You know all my tricks.
Kyle: "Well, you know, Cat Shirt Studios, I don't know how to put this to you, Kevin. It sounds terrible."
Kevin: So, maybe we'll go with Data Science Storytime.
Kyle: That's pretty good, I like that.
Kevin: All right.
Kyle: Okay, so that's like that's from an ethos-wise and also just shelf appeal, curb appeal.
Kyle: It passes those tests. Now, does it pass the test of your Venn diagrams? I can't remember.
Kevin: I think so. Look, let's talk about where we have expertise. I mean, we know a lot about data science.
Kyle: That's true.
Kevin: We work at a company that is all about data science.
Kyle: That's a good point.
Kevin: So, storytime, we love telling stories. I tell stories up on stage sometimes. You know, so stories is something that we love.
Data is something we think people care about. Stories are something that people like.
Kyle: And "data science," which I've always thought is a weird term.
Kyle: And when I say "always," I mean for the last few years, since the term existed. Well, because it implies that there's some kind of science that doesn't use data.
We've got to be more specific. "This is data science, not that science over there with no data." Like, have you ever been to the science lab? There's data.
I never understood that term "data science." I mean, it's buzzy, and maybe we need to use that, because we're cynics here. We're trying to design a title that will trick people into listening.
Kevin: But I would think you would like it because it's super meta. I mean, sure, all science uses data. But it might be the science of Stegosauruses. You know, it's really talking about the Stegosaurus and is using data to help us understand the Stegosaurus. But data science is the data. We're doing the data about the data. You know? We're... no?
Kyle: Sort of, but anything I'm thinking, okay, so I think about Data Science Storytime, potential stories popped in my head. And they're all not about the data.
Kyle: It's about--
Kevin: The Stegosaurus.
Kyle: Right, how I used data science techniques to do wind and solar energy optimization. That's actually energy science, and data is part of it, because data is just part of every science. So even though I kind of take issue with the term, I'm not a prescriptivist, you know.
Terms are what they are. People use it to mean a certain thing. I just always thought that was odd. I'm like, "It's just science." It's just science. It usually just means you were trying to do science, and your logbook wasn't big enough so you had to build a bigger logbook.
Kevin: So, where do you think that term came from, then? You say it's only a few years old, why does it exist?
Kyle: I think it exists... I don't know, I can't tell you, I wasn't there. I mean, I first heard it from a guy named DJ Patil who led the data team at LinkedIn. And now years later, he's the chief data scientist of the White House, or of the country or something. And he was the first person I heard say "data science." Maybe we should ask him, we should get him on this podcast here.
Kevin: Do you think he came up with that term in the similar way that we're having this conversation right now?
Kyle: No, I don't think so at all. No, I don't think so. He's like, "You know, science isn't specific enough. I'm only interested in the data kind, not that gut science, AKA not-science." I don't know, maybe we should do some googling. Maybe he came up with it.
Kevin: Where did you meet him?
Kyle: He had left LinkedIn and was working at a venture firm, Battery Ventures. He gave a talk, right when we started this, right around, had to be 2012, because it was when we were just starting this company. His talk was about data science, and I had followed him on Twitter for a while and seen him talk about data science.
I heard him say this, or I read it somewhere before I met him. I met him maybe a year later, and his talk was awesome. I still remember specific slides from it. And then I was supposed to meet him the next week, and then he had to cancel. And then I met him the following week.
He said, "I'm sorry, I just got a call from, you know, someone on some giant mountain and they needed me to come use data science to find a lost kid on a mountain. So I did that, and that's why I had to miss last week." And I was like, "That's the best excuse for missing that. You're like a data superhero." He's got stories. We should bring him on here.
Kevin: I like it.
Kyle: He's in D.C. now, but, you know, it's election year. We'll probably get him at some point.
Kevin: Do you know how he used data science to get the kid off the mountain?
Kyle: I could bullshit about it. I mean, vaguely, but no I don't know. I have no idea.
Kevin: I mean, I agree, it's the best excuse I've ever heard.
Kyle: Yeah, it's like data forensics, like Dexter. He just came in. He's like, I don't know. I imagine, so here's how it went down in my mind when he told me this...
Kevin: Okay, good.
Kyle: I usually don't share what's in my mind.
Kevin: That's what I want.
Kyle: He got a text that morning from, like, I'm going to say the president or something. He's saying, "It's very important. It's an election year." Because this is 2012, it's an election year. "So it's very important that we save this kid, through data." Maybe the president said to one of his advisors, they're like, "Well, there's this new thing called data science. Maybe we could use some of that to save this kid."
I must be butchering some... I don't even... I must be butchering so many details, even in my imagined memory. But then a helicopter picks him up, and they're like, "I know you've got this meeting with Kyle Wild, because we're in your email and everything. And you've got to pause that meeting, you've got to kick that to next week. Get in the helicopter, DJ. We need you."
And then he gets in the helicopter. They've got some time to kill, and they're like, "Well, are you actually a DJ? Because we've got this turntable..." He's like, "No, I'm not a DJ. Just get me to the, give me the data."
By the time the helicopter lands at Mount St. Helens, or wherever, this mammoth mountain, wherever it is, he's dug through the data. And the data has stuff like, how much gasoline is in all the snow mobiles, and the kind of stuff you'd see in a court case.
"When's the last time this little," I'm going to say, "little girl, was seen? And how many calories had she eaten? How far could she have walked? Maybe that'll help us figure out what radius we should be doing the search in."
Maybe it's actually, they've actually done a bunch of searches and he pulled in the data on the search parties and found that there were places they had missed. Or maybe he had some telephony data and he was going to do some triangulation on her cell signal before it faded. In what direction was it moving before it faded? These are all things I've made up. I didn't actually ask him for the details. I was like, "Oh yeah, obviously use data to find a kid. So, back to our meeting."
Kevin: But that's why we call this Data Science Storytime. We have data science and we have stories.
Kyle: Oh, we should just make them up. It could be like a sci-fi book. But we could say it with a certain authority.
Kyle: That's what I think, in terms of where the term came from. And you know, I'm pretty sure the next path, the next step in the path, is to ask DJ Patil where the term came from. Maybe he knows.
Fast-forwarding four years, LinkedIn still has one of the best data science teams in the world and is known for it. He's not there anymore, but they have some enviable talent over there on that team, I think. And some of the stuff we build at our company is built on one of their technologies, and it's all his stuff. I need to catch up with him anyway.
Anyways, semantics about whether data science makes sense as a term aside, I think it makes great sense as a title for a podcast.
Kevin: And it comes back. Okay, well, I like that. So, continuing to riff with you here, if we were adding another circle, now that we've mastered the first three circles of the Venn diagram, things we like to talk about, things we have expertise, things people might want to hear about, should there be another circle that relates to Keen?
Kyle: To come in because we both work there?
Kyle: I mean, we want them to pay us for this time. I mean this is, we want it to be work.
Kyle: We want it to be work.
Kevin: Right, we want to get paid for it.
Kyle: I mean, Keen is a data science company.
Kyle: And, you know, we get to choose the guests, which means we can bring in Keen people and Keen customers a little more frequently than we would otherwise do.
Kyle: To subtly push the message of the company.
Kyle: because we have to be very cynical about this. We are strategic, calculating people, Kevin. We have to do this with very specific business outcomes in mind.
Kevin: Listen, I mean, first of all, of course. Second of all, I did ask, should we include the fourth circle? The first circle, listen, I may be cynical, but the first circle I mentioned is "things we like to talk about." That will always be my number one circle, is things we like to talk about. I'm saying, should the fourth circle down relate to our cynical interests?
Kyle: Well, let's see. It's data science, it's got you and me, or whichever one of us can make it a given airing or a given recording. We got you and me, we, who work at that company.
Kyle: We have things people want to listen to, which means this has got a sort of growth mindset, inherently. Traction is kind of implicit in the design, right? We want to make something that the people will love. Or at least occasionally retweet.
Kevin: I mean, entertainment is, we want to entertain.
We want to inform, but we want to entertain. I want to.
Kyle: I mean, inform is, the interesting thing is, entertaining is how you inform. The brain becomes more plastic when the attention that entertainment draws out of the brain. It's focused on something.
Kyle: If we were to inform, well, we could just list a bunch of facts, and then no one would listen to it.
Kevin: Right, exactly.
Kyle: Or we could entertain, and then and then sprinkle in the information, like Full House, right? You think it's just a slapstick comedy, but there's real life lessons, morals of parenthood and family life sprinkled throughout it. This is like The Brothers Grimm.
We're The Brothers Grimm of data science.
Kevin: That could be an alternate title. The Brothers Grimm of Data Science.
Kyle: Yeah, or the byline just says "The Brothers Grimm." It's like, "That's weird, The Brothers Grimm are making that? That's weird. And they work at this Keen company."
So, I guess the point I was getting to, is that the first three circle already have so much implicit stuff, I mean, I think. And also, looking around for interesting data science stories, the first group of people I'd turn to would be our coworkers.
Kyle: Even if I didn't work there, they just have a bunch of interesting data science stories. So I think it'll probably get enough Keen value that they'll rubber stamp it and let us punch our time cards and count this for work.
Kevin: Okay, I think that's about the right--
Kyle: Oh, and I'm the CEO. So I'm pretty sure they'll let us.
Kyle: Or they'll cut me, and then they'll be like, "The new CEO doesn't like podcasts."
Kevin: I know. I mean, we have a strange organization sometimes, you know, so.
Kyle: Now, there's all this stuff around entrepreneurship and around startup life and our day-to-day, even unrelated to the market. The technology, the customers...
Kevin: But I think we can tell those stories, too. Those are stories.
Kyle: They are. They're not exactly data science stories all the time, but they can be. We're very scientific, so.
Kevin: In as much as they relate to where we work, which has a lot to do with data science.
Kyle: That's true. And the way we work, right. I was thinking, well yeah, but there's some things, sure, the product and the market are data-science related.
But if we were to do something about recruiting, which is something we think a lot about and work really hard on and do in an interesting way, if we were to talk about that, it's not really related to data science. Unless we were recruiting a data scientist. Or we use data science in our recruiting, which we actually do quite a bit. So, never mind, we do.
Kevin: We do.
Kyle: Yeah, that's the thing. We need to be cynical about this design here, but we don't need to telegraph it. Even though I'm saying it right here on the microphone.
Kevin: I know.
A lot of good designs, part of what you like about them is the dissonance about the edges, where it's a little hazy.
It's a little... you know.
Kyle: Yeah, it's the blurry stuff.
Kevin: It's the blurry stuff.
L- This is American culture. I mean, even our language is just so blurry.
Kevin: I just think it's pretty awesome that the first story, that you frankly made up, about data science, involved saving a kid on a mountain.
Kyle: Oh yeah, let's be clear, I'm going to make up pretty much all the ones that I'm talking. I won't even know I'm making it up. I'm not going to even tell sometimes.
Kevin: It's generous of you to acknowledge that they are false.
Kyle: Oh, and I want to say it, I won't always even acknowledge that they're false. So don't believe anything I say on this.
Kyle: Don't disbelieve it either, out of hand. But you know. You need to evaluate. The audience should evaluate. Always think critically. Never believe at face value anything you hear, AKA, part of the scientific method.
Kevin: Right. So I feel like we're, as I knew we would be, we're on the same page about this. I think our three circles, the fourth circle is merely implied.
Kyle: Is there some other fourth circle we should have? If we could get to seven, the seventh circle is, you know, the Seventh Circle of Hell.
Kevin: I mean, I think that that would not be a great selling point for our podcast. I recommend not calling it "The Seventh Circle."
Kyle: Well no, it'd be like a Wilco album. It's like really great, and there's just like a screeching sound like, hell on earth. Good luck editing this out. Forget about how you were riding your bike, and now you heard that. No, I don't think we should do that.
Kyle: Is there a fourth circle, just to tie a bow on it, is there a fourth circle that we should have? We've got the marketing appeal, if people like to listen to it. The authenticity of we have some expertise, the motivational factor, if it's something we like to talk about, because if we don't, let's be honest, there'll only be like three episodes if we don't like it.
Kevin: Well I think, I mean, I feel like, I enjoy listening to things when people seem to be having fun talking about them.
Kyle: Me too.
Kevin: If they just seem like they're going through the motions like, "Well, I guess I have expertise about that, so I guess I could talk about that," then, fine. But I don't want to listen to that.
Kyle: Nobody does. We have these mirror neurons, you know. We listen to something, we start to feel the way they felt when they made it. And if they felt--
Kyle: Like, "Oh man, I got to do this damn podcast again," then yeah, good luck.
Kyle: Yeah, I'm going to love retweeting that. Okay, we've got those three, is there anything else? I don't know. I guess what gets to me is what about all, so we do this, we've got these three circles. We look at the overlap, and now we're thinking, "Oh, Data Science Storytime," That's the name, right?
Kevin: Data Science, that's the one to beat.
Kyle: Data Science Storytime, it's in that circle, for sure. It's in the overlap of those three circles, but what about all the other stuff in the overlap of those three circles? Right? Like, you and I have some expertise in linguistics and like to talk about it.
Kyle: And people may enjoy listening to it. We had a great conversation yesterday about linguistics, about some kind of word choice thing. I don't remember what it was.
Kevin: Oh, "biweekly."
Kyle: Biweekly, yeah.
Kevin: And "bimonthly."
Kyle: And I was thinking, "Man if we ever have a podcast..." I didn't know what was the next thing. "If we ever have a podcast, we should talk about biweekly and bimonthly and how those words are meaningless and it doesn't make any sense."
Kevin: I think that we could throw that in, and I'll tell you why. Because think about it this way: 60 Minutes, they have this new show and they cover news stories. And at the end, I mean, I think he's gone now, but Andy Rooney, I mean, they throw in sort of like, the miscellany. You know, and I think often
the miscellany is kind of like the seasoning on top. It gives it its spice.
Kyle: Okay, I'll buy that.
Kevin: I mean, we could do a whole episode of miscellany. We could do a whole season of miscellany, probably.
Kyle: I'll buy that. I think that makes sense.
Kevin: I think it should be allowed to digress and have the opportunity to edit it out, if the digression, as will often be the case, turns out to be garbage.
Kyle: I concur.
Kyle: I will buy that.
Kevin: All right.
Kyle: I will "buy monthly."
Kevin: I think I dreamt about it, come to think of it. Biweekly and bimonthly, what? Who thought of that? What was the design of those words? They're so obviously flawed. I mean, for the sake of our imaginary listeners, biweekly; it can mean twice a week, or once every two weeks. Why was that word ever allowed to be created?
Kyle: Allowed? That's interesting. That's passive voice, let's go with active voice. Why did some authority figure allow it to be created? So who's the authority figure, Merriam-Webster? They are complicit. I looked that up. It says biweekly and bimonthly can mean the same thing.
Kevin: Yeah, exactly.
Kyle: Which is crazy.
Kevin: Yeah, no, Merriam and Webster are both in bed with this whole problem.
Kevin: But I don't think that Merriam and Webster are on the cutting edge. I think they are trailing indicators. I think the dictionary is just trying to catch up with what people are already doing.
Kyle: Well, what about Wiktionary or Urban Dictionary? Those are pretty fast.
Kevin: Yeah, I mean, frankly, I've seen more interesting things in Urban Dictionary than "biweekly" and "bimonthly." I've learned much, much more colorful things.
Kyle: Even just thing that start with "bi," really fascinating.
Kevin: Yeah, exactly. I mean yesterday, Kyle, you said that biweekly sounded like a magazine for bisexual people.
Kyle: It is an alternative lifestyle's periodical. It's called "Bimonthly," actually.
Kevin: Oh, okay.
Kyle: Oh, well, "Biweekly" too, because it's like Us Weekly.
Kevin: Right, I thought it was Biweekly. See?
Kyle: Biweekly, so good.
Kevin: You just can't get away from the problem.
Kyle: Oh, it's so good.
Kevin: You can't get away from the problem.
Kyle: You should now subscribe to Biweekly, which is a weekly magazine. If you want us to be less frequently in your inbox, you should consider subscribing to Biweek, bi-biweekly. Or bi-bimonthly.
Kevin: I feel like what we're doing now, see, I look over at Ted, who's, you know, recording all this for us. And I just feel like, what is he thinking? You know, is he thinking, "What did I sign myself up for?"
Kyle: "These guys are fuckin' idiots."
Kyle: He's thinking, "I should of taken them up on that beer they offered me.
Kevin: Yeah, exactly. I mean, right now, I feel like what we're trying to do, just to go meta, I feel like we're trying to establish the rapport, the chemistry, the feel, the tone, you know?
Kyle: We've only known each other for nine years, so we need to establish it here.
Kevin: We don't always have microphones in front of us and sitting in a room with Ted, you know.
Kyle: That's true.
Kevin: So we need to get that level of comfort.
Kyle: I feel like my comfort level is pretty good.
Kevin: Yeah, pretty good, pretty good. You know, I think it is within our power to choose an actual topic and really explore it. I do think that's within our power.
Kyle: Or we could talk about what process should one follow to choose a topic, and be out of time.
Kevin: Right. I mean, just to be clear, I know you're like me, Kyle. And Alexa asked me--
Kyle: In some ways.
Kevin: In some, but in this way. In some ways.
But Alexa asked me, "What is this? What is the topic going to be?" And I said, "Well, you know what I kind of picture? Is that we're going to need to just kind of have a meta episode where we just talk about what should it be called, and what kind of thing should it be."
And Alexa said, "What's the point of that?" And I said, "It's what I think we need to do." She said, "I think we should talk about, 'Why analytics? Why an API? Why?'" And, I mean, we can get to that. Those are great questions.
Kyle: We might get to that, it'll be, but it'll be sprinkled in.
Kyle: Right? No kid wants to watch, "Oh, here's a sitcom about family values." And I don't mean the talking point and the political platform. I just mean, when the high violin comes on, and Danny Tanner's like... Is this well-known enough that I can actually reference Full House? Do people know what Full House is? I don't know.
Kevin: Oh, people who are right at your age group do. I'm a little too old. But I know it.
Kyle: There's a single violin note. And then Danny Tanner's like, "You know, Stephanie, sometimes DJ just needs her space." Things like little things. I learned how to be a human from that show, in part. But had they called it, had they put it on ABC Family and said, "Oh this is a 'how to be in a family' show," no one would watch it.
Kyle:We might sprinkle some of that in, but no one will know it's coming. It will be kind of like burying a vitamin.
It'll be like when they put a little bit of kale in your orange, grapefruit, banana smoothie with strawberries, and they're like, "Oh it's a kale smoothie."
Kyle: "Yeah I had some kale today, kale and quinoa. I'm going to live forever." And it's mostly apple juice. The apple juice is what gets them drinking the smoothie.
Kevin: We got to know how to make the apple juice. You can always sprinkle some kale in, but if you don't know how to make the apple juice... You could talk about the API all day long. People aren't obligated to listen.
Kyle: And they're not, I mean, obligated is interesting. So how do make them obligated? Speaking of being strategic, how do we make them obligated to listen? Nope, can't do that. It's got to be entertaining.
Kyle: Okay so we've done all that. We've had this, we've had, we're in the middle of this meta--
Kevin: Yeah, we're in the middle of the meta. We've actually accomplished, it seems to me, we've accomplished quite a bit. We've landed on a working title.
Kyle: I think it's the title.
Kevin: Yeah, I mean, it's a hard working title.
Kyle: It's hard working.
Kyle: Hard workin'.
Kevin: We're havin' some fun.
Kevin: I mean, I have a list of topics already.
Kyle: For this title?
Kevin: That will fit with it, sure.
Kyle: So why don't we spend the remainder of the time talking about what episode two... or maybe this is episode zero?
Kevin: That's, you and I are mind-meld. I always thought of this as episode zero, not episode one.
I always knew I wanted to do an episode zero. It made no sense to me to start with episode one.
How can you start with episode one?
Kyle: You know our Myers Briggs are exact inverses. I'm an INTP, you're an ESFJ.
Kyle: Isn't that weird?
Kevin: Really we're exact inverse?
Kyle: Yeah, although, in the summertime, I'm extroverted and a little more feeling than thinking. But we're about most exact opposites. How are we mind-meld? How's that possible? We must be on to something if we agree. That's what I'm trying to say here.
Kevin: Yeah, but what about yin and yang? Isn't that a thing?
Kyle: That is a thing.
Kevin: That's ancient, it's more ancient than data science. Data science is like two or three years. Yin-yang is thousands of years.
Kyle: Yeah it's super old, but so is like, "Don't eat cloven hooved," and you know, that's old, too.
Kevin: I thought of that title and crossed it off.
Kyle: No offense to anyone who doesn't do that.
Kevin: Every week we explore another good reason not to eat cloven hooved.
Kyle: Nothing but kale. Or at least it has to have kale in it. Oh, okay, we'll make a 21st-generation kosher, where you just have to put kale on it. It's like, "Oh, I had a really healthy lunch. It was, what did I have? It was a pork belly burger with bacon, and a little kale."
Kyle: That's not a great title either. We're going off the tracks. Why don't we use the rest of episode zero to try and figure out what we're going to do for episode one? Tee it up, you know? This is the, "Next time, tune back in a week from now," or whatever, you know.
Kevin: I mean, they're obligated to come back, so.
Kyle: What should episode one be, you know?
Kevin: Do you want to hear some ideas? Or do you want to just--
Kyle: I don't know, you have ideas? Let's do it.
Kyle: What do you got?
Kevin: I mean, if I thought of episode zero being the meta, "What is the thing? How should we do the thing? Why does the things exist?"
Kyle: Navel gazing and all that.
Kevin: "Navel Gazing" actually was one of the titles for the podcast--
Kyle: We are mind-meld.
Kevin: Yeah, that I came up with. I got it from you, but I thought it richly describes what we're doing right now, anyway. I did feel sort of like, an origin story, you know, "Why did you start a company?" is something that I am curious about.
You know, for somebody who says, "I always wanted to write a book called this." I bet you always wanted to start a company.
Kyle: That I always really wanted to do. That's been a long time.
Kevin: So that is definitely a story I want us to tell, whether it is episode one, I don't know. But it seems like, if there's the circle that is what is your expertise, and part of what your expertise is is that you're the CEO of a company, how did you go from not ever having a company to being the CEO of a company? That's something that I, frankly, am curious about, myself.
Kyle: Oh I had a company when I was 11, so I didn't go from not ever. But at 10, I had not--
Kevin: At 10, you hadn't had one, so.
Kyle: That's true.
Kevin: I mean, but these are the details.
Kyle: Well if you go back far enough, we're all made of star particles, so.
Kevin: Right. I mean, we probably won't go back quite that far.
Kevin: So, that's a story I would like to hear, and I think that's a story that would make sense to do relatively early on, between you and me.
Kyle: What about other ideas?
Kyle: File that one away.
Kevin: Yeah, I mean they're filed. This is one from our greatest hits: "What is the upside of failure?" Talk about a greatest failure, you know, let's rekindle that. That would be another idea that we could do, because it's a classic.
And then I thought in the yin-yang category, "What are the downsides of success?" You know, "What's good about failure, what's bad about success? What are some of the unexpected hardships of success?" was another idea I had.
Here's one that you might not want to talk about right now, but what have you learned about pitching investors? How to pitch.
Kyle: Some expertise.
Kyle: I think these topics... I know you came up with these topics before we had done all of our important work here this afternoon, settling on the framing.
Kyle: These topics, you really have to squint to make them fit inside of data science storytelling. Right? And I know you said the first circle, the number one circle, by far, is stuff we want to talk about.
Kyle: Anyway, but no one would listen to something called "Stuff Two Guys You've Never Met or Heard of Want to Talk About." So we have to trick them into listening, like we've been working on.
But if I were to subscribe to something called Data Science Storytelling, I don't know if those would make sense as episodes. Those might make sense as part of the sprinkling, maybe across an entire season.
If you want to hear the story about starting a company, and why my story, or any of our network's stories, we should sprinkle them in. I don't think that is an episode, first episode, of Data Science Storytelling, is about how to fundraise.
Kevin: Okay, look, you can rain on my parade if you want. I've got a lot more.
Kyle: Oh, I'm not raining at all. I'm just criticizing for no reason.
Kevin: Okay. I'm not raining on your parade, that's a metaphor. What I'm actually doing is criticizing your ideas.
I'm not raining on your parade. I'm not the accumulation of moisture in the cloud layer. I can't rain. But what I can do is criticize your ideas.
Kevin: I mean, there's two sides.
Kyle: So, just skim through your list. Go head.
Kevin: There's two sides of the coin. We found a title that we kind of like, but on the other hand, what stories do we want to tell? Do we have enough stories there, and are these that we would ever want to tell?
You know, it's true that a lot of the topics that came to me were sort of areas that we had looked at for the blog and things like that. So it would be safe to say that pure data science stories might be a little limiting, unless we interpret it as we choose.
And I frankly think that interpreting it as we choose isn't such a bad thing to do. One of my favorite podcasts to listen to is Planet Money. Do you ever listen to Planet Money?
Kyle: I do.
Kevin: And how often are they really talking about money? You know, they often go very adjacent, or I mean, sort of money, sort of economics, but it's really sort of whatever they want to talk about.
Kyle: No it's true. The cover of a book is about getting it open. Does it get enough people to open it that they're going to tweet wildly and share and give us all sorts of free promotion and ego points, and all kinds of all that?
We'll make our Twitter bio for the podcast, I'll get a million followers and all that. But also, once they open the book, is the cover so misleading that they attracted the wrong group of those people? So we can make a podcast called "The End of Religion," and people would probably follow it. If we're not actually talking about that they'll be like, "What is this podcast?"
Kevin: Right, well, the end of religion started before this podcast began. That's why we never talk about religion or how it ended.
Kyle: See, that's controversial, if you said that. That would actually fit in the title, right? Have you ever read this book, or heard of this book by Tim Ferriss, called The 4-Hour Workweek?
Kevin: Yeah, I've heard of it.
Kyle: The 4-Hour Workweek is one of maybe 50 or 100 concepts in the book. The book should be called, "A Whole Bunch of Ideas from Tim Ferris, Some of Which Are Great, Some of Which Are Just Reprehensible, and One of Which is Four-Hour Workweek."
But another of his topics was how to name something, and he's like, "Oh, I put up a Google ad page, a bunch of Google ads, and picked a bunch of titles I had in my mind. And I saw which people clicked on the most. And then I named my book that. And I tricked you into reading it, by naming my book The Four-Hour Workweek, which it's not really about. That's one of the topics."
So it should be called, "A Bunch of Ideas from Tim Ferriss, and Some of Them Are Good, and One of Them is Four-Hour Workweek," but it's like four percent of the book.
He named it very, very cynically and strategically to make it into a bestseller, which I thought was brilliant. And also you respect the gamesmanship.
Kevin: Yeah, I do.
Kyle: So maybe we should do it that way. I think Data Science Storytime would get a lot of people to check it out, but we don't want to hit them over the head with, "This isn't what you were signing up for," first.
We want to do it in episode, like, four. We would need a few good ideas on tap that are on the straight and narrow. They're kind of, you think you're getting what you asked for.
Kyle: You know what I mean?
Kevin: What's so great about analytics?
Kyle: That's interesting.
Kevin: That sounds like it's in the nature of Data Science Storytime, does it not?
Kyle: Oh, it definitely does.
Kevin: I mean, in a way, those are the two origin stories. One is why start a company, period. But another is, if you're going to start a company, you could start it for lots of things. You could start it for a new type of pizza delivery, you know? But instead, analytics.
Kyle: That's riff-able.
Kyle: That's a way to frame it. I see what you're doing, you're taking the same concept and just thinking about different titles for it.
Kevin: Well, not necessarily, no. I mean, I genuinely think there are two related stories. One of them is, "Why be an entrepreneur? Why start a company? Why not just ride on someone else's train? Why make your own train?"
Kyle: Fuck those other trains, Kevin.
Kevin: Well, I know. But I want you to riff on "fuck those other trains" for an episode.
Kyle: This train rules.
Kevin: But, because I think, my guess, knowing you, is that there are two threads here. One thread is "Why do I want to have a company, period? Why do I want to start a company?" And another thread is, "What kind of company do I want to start?"
I bet you thought, "I want to start a company," before you thought, "I want to start a company that makes analytics."
Kyle: Oh, yeah.
Kevin: Exactly. Now, in chronological order, the "why do I want to start a company at all" comes first in your life. And "why am I going to choose analytics as the thing" comes second.
But maybe, in the context of Data Science Storytime, "Why analytics? Why am I jazzed about analytics? Why do I think analytics is cool?" comes first, and then let's go back even further. Let's take the time machine and see, how did we even get to the point of choosing a thing?
Kyle: That's interesting.
Kevin: That's sort of in medias res.
Kyle: It's interesting, I have a sense of, it puts me, and my life and career thus far, very much in the spotlight for the first episode. And I'd much rather slowly eke out pieces of the spotlight over many seasons.
I think, but I don't know, I think this makes complete sense, and you and I are very on the same page, and that this could make a lot of sense. To choose a first episode, I think we want to capture, it's very theoretical, it doesn't sound like it's storytime.
It is a little bit, but the thing is if I'm the special guest on episode one, it's not going to really be storytime. I'm not a great story-time guy. I'm much more of a--
Kevin: I disagree.
Kyle: Theory time. I can go theory time all day long. I'll talk about analytics and theory.
Kevin: I mean, we did that discussion and later made a blog post about failure.
Kyle: Ooh, but you were storyteller supreme, actually. You can pull the story.
Kevin: I help.
Kyle: And make it make sense.
Kevin: Didn't I help you? I mean that was a great story that came out of there. I didn't say very much, but I prompted once or twice.
Kyle: That was great. It was great because afterwards, all the reasons, that's when we realized all the reasons I don't write blog posts or give enough talks. And they're all the reasons you don't. We compliment each other in that way.
Kevin: Right, we do compliment. I mean, it's yin-yang.
Kevin: That's what's going on here.
Kyle: Yeah. So that's viable. An interesting consideration might be to bring in another founder to talk about that, someone who also started something in the data science world or analytics world, as a way to divert the attention away from me. So that I can be comfortable.
Kevin: I don't want to divert the attention away from you, and obviously I'm making that pretty clear. I'm going to be blunt. I think, my gut tells me, because you and I, we've had a lot of conversations over the years, I think a lot of people can be involved in this project and should be and I want them to be.
But the reason that you and I are sitting here together for episode zero is because there's no single other person that I think is as well suited to be in the duo of people who kind of help create the momentum of the thing.
Kyle: Suffice to say, I as well am very picky.
Kevin: All right.
Kevin: Cheers. So, to me, therefore, it seems like, if we're going to try to create the momentum of the thing, and if we already have good chemistry together and we have demonstrated that, then I think we should try to find some things to talk about when we're establishing product viability, where we create some of that momentum before we bring in another person that we don't have as much.
I mean, obviously there's people that we know super well: your co-founder and other friends who could come in. And I understand why you say you want to deflect the spotlight away from yourself. But I want to push it back onto you.
Kyle: One thing that is possible is, instead of being a co-host, I could just be a special guest and you could be the host, and from that lens--
Kevin: Yes, I think I should be the host.
Kyle: Oh, maybe we should do it that way.
Kevin: I think you should, there are many shows that have a host and a very frequent guest, but the role is still that that person is a very frequent guest. And that, I think, probably would work well. If technically I'm the host, which means, you know--
Kyle: Which means I'm just here as a guest. Why am I helping you design this? You know what? Guest. I like guest. Let's edit out all of me talking in this one so that we can make people think that... I'm just a guest.
Kevin: I think you're over-correcting now.
Kyle: Fair enough.
Kevin: I think you're over-correcting. I mean, a podcast that I think about, that has, sort of that format, is Freakonomics. Do you ever listen to Freakonomics?
Kyle: I do. Stephen J. Dubner
Kevin: Dubner and Levitt.
Kyle: Levitt is the dorky-like professor, and Dubner is sort of the--
Kyle: The journalist.
Kevin: Wait a minute, what are you implying?
Kyle: I chose my words un-strategically. Nonetheless, I think, even though they both are in most episodes, you get the sense that Dubner is really the one who's the host. And when he's going to go out in the field and interviews other people, he's going to be likely the one who's going to go interview other people. And there often are other people.
Nonetheless, Levitt, he's an anchor tenant. You know, he is there in most episodes, and they have a special rapport. But if there are going to be interviews with other people, it's going to be Dubner who's probably going to be the host of those conversations. And the onus will be taken off Levitt, who you can tell really doesn't want to do that.
Kevin: And among Dubner's duties is to try to pin down Levitt once in a while.
Kyle: Oh, yeah.
Kevin: It took you months to pin me down for this, so clearly, if you were required to pin me down, and a guest who may be even more dorky-professor-like, and should be more dorky-professor-like many times, how can you pin both of us down? That's never going to happen.
Kyle: That sounds terrible, Kevin. Why don't we just make you the host?
Kevin: Okay, accepted. I am the host, but I would like you to be, I mean, you are way more charismatic than Levitt. And yet, you even know, we all know, who he is.
Kyle: I appreciate that. I do agree. I'm probably more charismatic than Levitt, perhaps a smidge less professorly, because he's Levitt. I read everything that guy writes. He's brilliant.
Kyle: He's incredible. Or he probably doesn't even write most of it. It seems like, maybe he just has a beer and talks and disappears, and Dubner writes it. Even their first book together was, they're still monetizing that book for a podcast called Freakonomics Podcast.
Kevin: Yeah, I've never even read the book. I just listen to the podcast.
Kyle: Well, speaking of Data Science Storytime, there are a lot of things in the book.
Kevin: Oh, yeah.
Kyle: And of course, the podcast.
Kevin: I mean, there really are. There's a lot of data science.
Kyle: They could actually call it Data Science Storytime, but it's too bad: trademarked. Kevin Wofsy Data Science Storytime.
Kevin: Yeah, exactly.
Kyle: Good luck.
Kevin: Freakonomics is pretty good, too.
Kyle: It's pretty good. Data science is so interesting. Economics is a data science. Actuary is a data science. Physics is a data science. So it was one thing for episode one, I don't think I'll come on and just shit on the title of your show.
Kyle: But if you ask me to, I'll riff on that, assuming we'll edit any of the riffing here.
Kevin: Yeah, what exactly will happen with episode zero? I don't know, that's why "zero." I mean, zero in many mathematical computations voids the entire equation.
Kevin: But I think that--
L - We could zero it out.
Kevin: We could, but we've got to save it. I think there could be bits here that could be sprinkled in later. I think that it would be hard for a listener, to listen to episode zero as their first experience with us.
Kyle: Yeah, it might be kind of like the B-side, where you go, "If you like it, you go back to the creation of..."
Kyle: Navel gazing.
Kevin: To me, if I could make an analogy to what it is we do at work, where we let people track a bunch of data that they might need later.
This is like data that we might need later. We don't necessarily need it right this second.
Kyle: So, get some traction, and then, "Announcing episode zero."
Kevin: Right, exactly.
Kyle: Behind the scenes.
Kevin: "You thought you were here in the beginning."
Kyle: "You loved it in the theaters."
Kevin: "You thought you were here since the beginning; go before the beginning. Do you want to hear what there was before the beginning? Before the big bang?"
Kyle: Before they got that record deal. They actually did a not-super-well-known album. It released on cassette, and guess what? It's on iTunes now.
Kyle: Buy it now. For the band Sublime, that album's called "40 Ounces to Freedom." Nobody knew about it, and then, all the fans...
Okay, so, announcing episode zero. "The making of. You enjoyed it in the theaters, now get the director comments."
Kyle: On DVD. And give us 12 more dollars or however many dollars--
I think it takes a certain degree of arrogance, and I have this degree.
Kyle: Oh, me too, whatever you're about to say.
Kevin: So to be fantasizing about future success, that you fantasize people will want to know what was the birth of the idea that grew to be so compelling. We better save it now so that people can access it later.
Kyle: Yeah, I possess the same trait. Delusions of grandeur, I think is what it's called.
Kevin: And it's paid off for me. It's paid off for me with your help. I mean, I perform in Mortified. You're the one who found that show for me, my teenage journals that I kept, because I thought they were going to be necessary some day. I was right.
Kyle: You were right. This is the thing about delusions of grandeur, they can be self-fulfilling, I think.
Kyle: Most of the time they're not.
Kyle: There's not enough grandeur to go around.
Kevin: But luckily,
I have enough delusions of grandeur that if even only a few of them pay off, it was worth the investment.
Kyle: Oh, that makes sense. So, we haven't really come to a conclusion.
Kevin: Yeah, we haven't.
Kyle: But the thing is, we don't have to come to a conclusion on episode one. You're the host, boss. If you'd like me to be on as a guest, I'll think about it. I'll probably be on as a guest. I mean, I'm not going to be Andy Richter. I'm not going to be on every show, but not the host. That sounds terrible.
Kevin: Yeah, no, I don't want you every show.
Kyle: And Andy Richter, I mean, that's a different role.
Kevin: No, that's not the role at all.
Kyle: I'm not going to be Max Weinberg in the Max Weinberg 7. Speaking of things people from the nineties know...
Kevin: I mean, I really do think the Dubner and Levitt thing--
Kyle: Oh yeah, I like the Levitt, I like that.
Kevin: Levitt is where the expertise is. Without Levitt, there's no meat on the bone. I mean, Dubner is just a clown, really. He's just there. I mean, I like clowns.
Kyle: I hear you fishing for compliments there, Kevin. I'm not going to give in to that.
Kevin: Okay, that's fine. I'm just giving you a compliment, purely. I can't do a show, Data Science Storytime, without access to a Levitt, you know?
Kyle: You have access to tons of them.
Kevin: I have loads of Levitts. Yeah, exactly.
Kyle: I'm happy to be Levitt primary.
Kevin: Yeah exactly, Levitt number one.
Kyle: You should get Mr. Larimer on this show.
Kevin: Yeah, oh, for sure.
Kyle: That guy's a Levitt. We got a bunch of those. I think that makes sense. And if you do decide that episode one makes sense, maybe just don't tell me that's going to be episode one. You can figure that out in post.
Kevin: Yeah I don't--
Kyle: It's a big burden to bear.
Kevin: Oh no, it's too much of a burden. And my feeling is, since there have to be three before we even launch, we can always switch the order. So I think it's too much pressure to say, "This is for sure episode one." It's too much pressure, that's my feeling. It's an early episode.
Kyle: I do have a concept.
Kyle: It's based on a pun, and I sometimes go pun first and then try to fill in the details. The idea is the data of follower-ship. So this is like Twitter followers, Instagram followers, the following of a digital brand or person, you know.
The marketing platform for Ashton Kutcher, right? The digital follower-ship and how hard it is to do the data science around it, which actually, it's quite hard. And the idea is that you could call this "Episode One: The Fandom Menace." Got to go.
Kevin: I like it, I like it. Okay, episode zero complete.