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0:00:00.3 Announcer: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

0:00:15.7 Tim Wilson: Oh, Hello there. Welcome to the Analytics Power Hour. This is episode 239. Thanks for taking a break from banging out some python as you deploy a mixture of LLM you just pulled down from hugging face to give this episode a listen. My name is Tim Wilson and I am sufficiently aware of my lack of technical prowess to know that those languages and platforms I just referenced may have been smashed together in a way that is total nonsense. But that’s why it’s great that I’m joined by a couple of co-hosts who will tell me as much Val Kroll, you’re an optimization director at Further am I pulling you away from any deeply technical work to do this recording.

0:00:53.4 Val Kroll: Not as technical as you just described. I’ll tell you that.

0:00:58.5 TW: It’s like I was making it up. You might have been doing real work though, but we’re also joined by Moe Kiss marketing data lead at Canva. Moe, you’ve now been a management for a while [laughter], so like do you even write any sequel at work these days?

0:01:16.2 Moe Kiss: No, and every time I do, I just feel really like rusty around the edges. [laughter], it’s definitely a skill that’s fading.

0:01:27.9 S1: The trappings of management.

0:01:28.3 MK: Yep.

0:01:33.9 TW: Alright, well I’ve been kind of clunkily pointing towards the topic for this episode already, but really it just, it seems like the pace of technological change, it’s so fast, I can’t even say the word, but the change in the world of data and analytics is accelerating at kind of just a crazy rate. And yet there are a lot of us with full-time data or analytics jobs who were drawn to the space sometime back and we love it. But now we have no small amount of trepidation that there’s a whole world of technical skills that we need to develop to actually stay in the space. And that’s what we’re gonna talk about. And the three of us each have our own kind of journeys and kind of how we got from where we were to where we are now. But we wanted to get a guest who really embraced that transition from a non-technical background all the way to the co-founding of a very technically oriented startup.

0:02:24.3 TW: Kirsten Lum started her career by getting a degree in English literature and then going to work at a grocery store in a position that was pretty far away from data science as you might imagine. But from there she went on to spend at a couple of different roles, but ultimately she spent like nine years going from Expedia and then to Amazon and is now the CTO of Storytellers.ai, which is a company focused on bringing the practical application of data science to mid to large-sized organizations in a wide range of industries, including nonprofits, which I think is actually really cool. So welcome to the show, Kirsten.

0:03:01.0 Kirsten Lum: Thank you so much for having me. Great to be here.

0:03:04.9 TW: Awesome. So usually, we don’t often, it’s not a normal thing for us to start out by asking a guest to give us kind of the rundown on their personal career journey. But given the topic of the show and giving that yours as I kind of set it up, sort of definitely fits with this topic. Could we maybe start with you giving that kind of the quick rundown on how you actually made that series of transitions from English literature to data science?

0:03:27.3 KL: Yeah Absolutely. I love telling this story because it seems so what’s the right word? Unlikely. It’s an unlikely story. It feels like when I was younger, like as a kid, I would’ve been absolutely appalled if you had told me that the job that I would do, I’d describe myself as a scientist. Like that would’ve been the worst fate I could imagine as a child. ’cause I was all in on like arts. I was like I would drawing and writing and reading and I did a lot of theater. I was in band, like that was my life was very much like the left brained as we used to call it creative pursuits. But it turned out, you can’t, I came from a really small place. I actually grew up in sort of farm country in the US and there’s not many opportunities to like do much even as a creative out there in rural areas.

0:04:25.9 KL: So I eventually ended up going to college and, through many twists and turns, ended up with an English degree with my aim of being a librarian. Like that was my dream was to be a librarian.

0:04:37.4 MK: Wow.

0:04:38.0 KL: Which, yeah, by the way is a master’s degree. You have to have a master’s degree to be a librarian. So I was like okay, get my bachelor’s in English, I’m gonna go get my master’s to be a librarian. But like at that moment, as you kind of pointed out, like many people at that station life, I was struggling so hard to just make ends meet in one of these places in the world that’s so high cost of living. So I was doing whatever I could to sort of get out of this. Like I’m negative $30 in my bank account at the end of every single month.

0:05:02.6 KL: So at the time I was working in a grocery store and in Seattle of course we have very little daylight. So I would walk to work in the dark work in a freezer. By the way, my role, I had to be in a walk-in freezer to cut the fruit that you see on the wall in Whole Foods and then walk home in the dark. And I was just like I can’t do this. I can’t do this for like another day. And it turned out my friend had just started at a startup doing marketing and like that’s one of the things that English majors kind of do with their degrees. They end up going into marketing. So despite having no reason to be hired at this place, like terrible resume and no experience, someone took a shot on me and I got a job doing marketing there.

0:05:39.1 KL: But what it turns out is I think a lot of you like experience based on your backgrounds is like once you start doing marketing, the step to doing data science is very small. Because once you figure out, here’s how I know if I’m a good marketer is by going and getting the data about what the outcomes were and trying things and seeing what works. And that’s what data science is all about. So that was where I got my first feeling of I get this data stuff and it’s really interesting and fascinating. And then from there, after one of my favorite parts of the story is that the founder of that startup he’s my husband now. So when we started dating.

0:06:19.5 MK: Oh Wow.

0:06:21.2 KL: I actually, I was like okay, I need to leave the startup. So, he and I started dating and that was when I… Yeah, exactly. There’s a whole thing to unpack there too, by the way, that doesn’t fit into this length of description, but that was where I ended up at Expedia and in a data analyst role. And one of the coolest things about that time was seeing how powerful picking up these technical skills like Python was and actually changing the way that the company operated, like a single person could change one part of this organization and really see it grow just by, putting in that work to learn those skills. So, and that for that is where the history starts of all the way down to becoming a CTO of an AI company at this point. So yeah, that’s the unlikely tale [laughter]

0:07:05.5 MK: I really love that point though, which I probably don’t reflect often enough on that, one data analyst or data scientist sitting in a company can do work that can like fundamentally change the direction of the company. Like that is such a powerful thing. I love that you called that out.

0:07:23.6 KL: Yeah, absolutely. I think that the fact that we’re called on to create the narratives that form people’s mindset around what they should do next and telling people how we’re doing. Like we’re the speedometer and the, oil change light and all of that. We’re that for the company as they’re trying to drive. And so, doing that well really does shape that point of view.

0:07:51.8 TW: But is that, I mean, on the marketing front, I feel like… I sort of came in through a technical writing and then wound up in marketing. But that first time I got access to Webtrends or SPSS and thought, oh, I have the data and now I have the data and it’s telling me something. I feel like that’s the entry point for a lot of… For some non-trivial number of people. But then they’re like so maybe I get good at Excel, maybe I become a real jockey with Tableau or with Adobe Analytics, which that feels like there’s a ceiling to that ’cause one, you’re maybe prone to misinterpret the data because maybe you’re not understanding incrementality or causality or something like that. But then there’s also limited ability to drive efficiency with a Python or R or SQL. So like that feels like where some people say, oh, I’m an analyst, and all of a sudden people are talking about Python or SQL or R and saying, do I have to do that? Or, can I just live in Tableau and let somebody else do the work?

0:09:10.5 KL: Yeah. Yeah. I think it’s a great question. And that I think you’re totally right there. People kind of stratify, like you’ve got the marketing folks that actually would love to never touch a spreadsheet. And then you’ve got the marketing folks that have got one foot in each camp. They’re asking you for extracts all the time and like they’re, trying to piece their things together. And then there’s the ones that have put both feet in and they’re, to your point, they’ve got access to the, DB or whatever and they’re pulling their own stuff. And then there’s the folks that go like this next layer deeper, which almost starts to touch software engineering. Like you start thinking about, that was actually where when I ended up going really hard technical in my career when I was at Amazon, one of the projects was around instrumenting devices, IOT devices, and like what analytics do we need to get back from these edge devices that have a very low battery life?

0:10:02.9 KL: They have very small processes on them, very low memory, intermittent connection to the internet. What analytics do we need back from them and can you make it work within this proprietary operating system? And that’s the other end of suddenly now you’re touching software engineering and your skills as an analyst, you’re pulling the thread all the way through, not just pulling data out of the database, but like how does data get into the database? Like what does it actually mean? What does it, because data in a sense, or in my view, it’s our pixelated view of reality. Like that’s what data is meant to represent. It’s a pixelated view of reality. And so can you make that pixelated picture a little more high fidelity and do you understand the artifacts in that picture well enough to be able to analyze it properly? Yeah, it’s blurry, but like yeah, I know where I’m going even though this picture’s a little blurry, so you’re totally right. And I went all the way. I’m like go all the way to the ground. Let’s go all the way to the ground on this. ’cause it’s just, the most fascinating part in my opinion.

0:11:03.6 S1: It is time to step away from the show for a quick word about Piwik Pro. Tim, tell us about it.

0:11:12.7 TW: Well, Piwik Pro has really exploded in popularity and keeps adding new functionality.

0:11:17.2 S1: They Sure have. They’ve got an easy to use interface, a full set of features with capabilities like custom reports, enhanced e-commerce tracking and a customer data platform.

0:11:28.1 TW: We love running Piwik Pro’s free plan on the podcast website, but they also have a paid plan that adds scale and some additional features.

0:11:36.6 S1: Yeah, head over to piwik.pro and check them out for yourself. You can get started with their free plan. That’s piwik.pro. And now let’s get back to the show.

0:11:47.7 MK: Can I just Draw on something that you’ve brought up there? It sounds like you did go very deep to the, I guess like the technical side. I’m interested to understand like what caused… It sounds like there’s something there that interests you about problem solving, but like what is it that made you go really deep on the technical versus, I find lots of people that transition into data from other disciplines, like a lot of the time their strength is more in like the soft skills is like what makes them so phenomenal. And that’s not to say they’re good or bad at the technical stuff, but it’s like that’s what drew them in is like oh, there’s actually this whole component about soft skills that’s so important in this job and that’s why I’m interested in it. But it sounds like you’ve had a kind of different interest maybe.

0:12:35.4 KL: Yeah, I am formulating my answer because I have, again, a short answer to this and a long answer to this [laughter] And so I’m gonna try and thread the needles on that one because one of the things I hear a lot about people that get drawn to analytics is they’re like I’m a truth teller. I wanna know what’s true. I wanna know what’s real. I wanna know what the causal relationships are in this organization that I’m part of, and how my actions or our company’s actions are affecting, our customers and things like that. Like that really draws a lot of people into analytics. And what I think a lot of folks find when they go deep enough on that thread, they pull that thread for long enough is that it’s subjective all the way down until we get to the instrumentation.

0:13:18.7 MK: [laughter], I wondered if that’s what you were going with that.

0:13:18.8 KL: And so.

0:13:19.6 S1: I was like ooh, this could go in two directions.

0:13:21.9 KL: We could.

0:13:24.7 S1: We could be in a brawl here momentarily. [laughter], okay.

[laughter]

0:13:29.3 KL: It’s like you go… And as long as you have that drive in you and people who I think end up going really technical understand that if you really wanna unwind that, like if that motivation keeps you going every single day as it does for a lot of people, you’re eventually gonna be talking to the software development team. Like you’re eventually gonna be there and you’re eventually gonna be automating these very complex things that you wanna do. You’re gonna be getting into things like causal analytics. You’re doing, ML or you’re doing, more complex statistics or you’re Evasion, which is not me, and you’re like doing your whatever vision whatever, like you’re doing that kind of stuff and you realize suddenly that that’s necessary to get closer to the truth you’re looking for. And so, for some people to your point, it’s like that’s less them, they’re less about like I want to understand what’s true and they’re more about, I want to sort of translate for people, I want to help them, connect their problems and their solutions more effectively. And so they’re motivated by some of those other tasks that are, again, very valid and needed in the space. But that’s my rough guess is it’s those folks that are like no, I need to know. I need to know, you know.

0:14:37.7 VK: So I know that you said that you got the first role in the marketing because someone took a chance on you, but I doubt that that’s been the pattern all the way down, as you say, [laughter] So like how did you make some of those transitions? Like how much of this is self-taught versus things that you were able to like ramp up on because of projects within the role? Like I’m really curious like how you got there.

0:15:02.6 KL: Yeah, I will put it in the following orders. One is I had a lot of help in these very particular, what I’ll call leverage points in making career changes. And then number two is like really flat out like hard work and learning things and demonstrating them. And then number three is focusing on outcomes. So the number one thing is my husband is an absolute master at writing resumes. So he spent like three years every weekend working with people on improving their resumes and helping them get jobs. And like there is a real pattern to how resumes can be written such that it’s more likely that someone’s gonna read that resume. Like yeah, you’re a great, you’ll be a great fit. And I didn’t have those skills coming in like a resume was totally foreign to me. That resume for my first job that I got as a marketer it was so bad, it was so bad, [laughter], but like my next resume, because he and I were dating at that time that he helped me prepare for going to my job at Expedia was much better [laughter] and like there is this whole… Like I can’t understate that the ability to summarize your own abilities into the resume artifact is one of the highest leverage points that folks have.

0:16:28.2 TW: Some people like go to movies and dinner when they like court. I mean this is… I’m just envisioning the… Like what should we do? I don’t know.

0:16:35.8 KL: Work on my resume.

0:16:37.8 TW: I think I’ve got some skills that need to be summarized.

0:16:40.0 MK: No, but Kirsten, I’m the same. I believe resumes are like so undervalued and anytime a friend is like looking for a job or they… Like especially if someone wants a referral at my work or whatever, I’m like one of the first things I do is like let’s sit down and have a glass of wine and you tell me all the good things you’ve done in your last job and I’ll just take notes. Because if someone’s telling you like over a wine or something, they tend to like be a little bit more relaxed than when you’re like write down your top achievements, you know? But I actually, I think it’s such an art form and I love that you see it as like such a leverage point.

0:17:15.2 KL: Oh for sure.

0:17:18.5 TW: I didn’t mean to derail you on that [laughter] Yeah. You had multiple points but couldn’t not swing at that one.

0:17:21.3 KL: That’s right. Well, I mean, funny enough like he’s my co-founder too. I don’t know if I mentioned that yet, but like storytellers, I’m CTO, he’s CEO. So this is like our basis for our relationships. You’re actually right on the money in that one. And that’s number one is like finding the people, one, finding people who know what are the leverage points in career change and that can actually help you do it because you don’t actually get any points for doing it all by yourself. It’s harder and you don’t get any extra credit for it. So finding a way to find people that are experts that can help you identify these leverage points and then like listening to their feedback and doing what they suggest. [laughter] is like this like something that I find is very undervalued in people. So that’s one. Two is it is hard work. Like at the end of the day you do actually need to have the skills for the job that you wanna transition into. And for me that was coming in as a marketer with an English degree and I learned a lot of stuff about process and a lot of stuff about marketing.

0:18:28.0 KL: But then for this job, I needed to know SQL and I needed to know Excel. And so I like had to sit down and learn it. And this was before, there was like Coursera, so I was literally reading like SQL docs on W3 schools was like how I learned SQL is like really a grind, but like you got to do it, you got to like sit down and learn it and like commit. And then the third one, this is actually goes back sort of to the resume point. But you have to demonstrate that you can do those things. And a lot of times, like back in the day when I was doing it, there was certifications is one way. That was the route that I took. ’cause I found some SQL and Excel certification that I could get and I just got that certification just to demonstrate, like someone out there says, I meet the bar for being able to use these tools.

0:19:09.4 KL: But like nowadays you might have a GitHub, repo or you might have projects that you’ve worked on. You have a website where you can put up the work that you’ve done. So like just demonstrating, not only do I know like the leverage points and this career change, I’ve learned the skills that I need for the job I’m looking for. Which by the way, read job descriptions is weirdly like the fastest way to understand what is required in a job. Read 10 job descriptions for the job that you want and like summarize the skills and then go get those skills and demonstrate that you have them. And like that’s the weirdly simple formula to like, here’s where I wanna go. Check, checklists, go do it. And do that thing.

0:19:49.5 MK: Simple formula [laughter]

0:19:50.5 TW: I feel like the demonstrating the skills like well, finding something that like you care about and this is, I’ve done it and then I’ve talked to other people about doing it. Like find something where you can demonstrate the skill with something that you care about. ’cause people are like, well I don’t, I can’t get hired. I can’t demonstrate it ’cause I’m not hired. It’s like, well no, figure out what you’re doing or you would like to apply it, make it a little more public. And then that kind of does two things. It shows that like you actually are demonstrating the skill, but it’s also probably giving you a project that’s a lot more interesting to talk about than, oh, I went on to Kaggle and I guess what my model on the Titanic survivability did. Yes. And people are like, oh great.

0:20:31.4 KL: 100%.

0:20:31.6 TW: Congratulations. Whereas opposed to, I did this ’cause I was really trying to figure out where we should move when I moved to a new neighborhood and wanted to do some sort of analysis to see if I could figure out where we should be looking for a house. And I had to learn Python along the way.

0:20:46.4 KL: Yeah, that’s so true. And it goes back to your question, I think Val, about like, was it projects or is it, how did you, is it just self-taught and like that project element. And exactly what Tim just said of like threading the two things together. One is like a lot of, for me at the beginning it was just independent projects I was doing outside of work. But once I was at Amazon or once I was at Expedia and Amazon both actually, but especially at Amazon, like people don’t say no to free work [laughter] So like, it’s really rare, especially free data work. Like everyone’s like short on someone who’s gonna do like their pet analysis. And so you can take on the, one of the ways to get to demonstrate the skills that you have is to seek out responsibility and take on a responsibility that allows you to like, demonstrate that.

0:21:41.6 KL: So that was what I did. I ended up being, how I ended up with like a 14 person data science and like engineering statistician team was because there was all these people who had, like, by the way, this is a whole tangent we can talk about. They had their one analyst, like their analyst on an island in like a few different teams. And they were all like, we can’t figure out what this person should be doing and like helping us, like how to help them help us with what we need. Can you manage them? So, and like help us figure this out. And that was how I got, became managing all these people and also how I got this very broad purview of the organization. ’cause there was like six different teams that all were like, we need you to help us help our analysts understand our business. And so then I had to understand all six other businesses [laughter] So it’s like, it really, and now I have this broad range, like I’ve done retail, I’ve done streaming, I’ve done all these random businesses that I wouldn’t have gotten to touch because I just took on that responsibility. So, being on the lookout for responsibility is like so, such a hack for getting new skills.

0:22:47.4 MK: On that though, think the thing sometimes I struggle with is like, I’m going to wager a guess that if you asked any of the four of us about these like career shifts or moments where we’ve changed course, I’m guessing all four of us would say it also took a lot of personal time and effort of like being so interested and driven by it that we put our evenings and weekends and like, it is beyond just expecting that these big changes in your career are gonna just happen only at work. Like was that a similar experience for you? Am I guesstimating right?

0:23:28.5 KL: You’re right. The actual, the first project, the one that really I consider like an inflection point into like deep data science and like technical skill was I was working in like basically an SEM type channel. I was doing paid bidding for what’s called vertical search within travel. So it’s sites like Kayak, like who’s on the top rank for this particular hotel. And I had that bidding process was being run with Excel spreadsheets and the marketer that was doing that, they left their role and that team asked me to fill in ’cause like I have Excel skills so I can do it right. So they asked me to fill in while they were back filling that role. And I was doing it, it was taking like, it would like 10 hours, 10 hours of just clicking in Excel because the sheet was so huge.

0:24:15.6 KL: It was like refresh the pivot table and then calculate fields and then copy and paste and like, and it was just, and then it would crash and you just like, it was mind and numb. It was mind numbing 10 hours. And I had talked to one of my mentors who now actually helps with our company who is like, OG data scientist who has been doing this forever. He’s like, that sounds like it should be a Python script. And I was like, it seems reasonable. Like I hadn’t ever touched a lot, I hadn’t written any code besides SQL at that point. I was like, sure, sounds sounds about right. So I asked my boss like, Hey, can I like try turning this into a Python script? And he was like, no, I won’t be able to understand it. So like, no, you can’t turn it into a Python script. So on my weekends and nights I was like, great, I won’t do it during work hours, I’ll do it the way they ask me to during work hours. But on weekends and nights I just like turned this thing into a Python script and it ran in eight seconds. Eight seconds. It was like, I was so…

0:25:05.4 MK: Oh, that must have been so good.

0:25:07.4 KL: Yes, it’s the best feeling. That is the one that I feel like gets people hooked. They have that moment of like, wow, like this lever is so big. And then of course I show my boss, I’m like, look, everything that you see in this Excel spreadsheet, I am producing every single field that you see in your Excel spreadsheet, but I’m doing it with code. You can review it the exact same way and it runs in eight seconds. And he’s like, okay, okay. And then actually, so one that was my moment of like nights and weekends really was the lever. Like what started the flywheel for me becoming technical, but also that change when I showed them that when you switch this thing from Excel to Python, that it saves, I mean this, there was a team of like 15 people doing this. So you can imagine how many hours were being spent.

0:25:52.5 KL: So then you say, okay, we’ve now eliminated all those hours and it changed the composition of their team, like what time they were spending in doing different things. They could now think about strategy way more than they were able to before. ’cause they weren’t spending 10 hours on the Excel spreadsheet. So like that kind of thing. Like the nights and… I always encourage for people when you’re following a passion, following a hunch, like do that without making it the norm. Of course, like this should be an exceptional event. But sometimes those exceptional events are really the inflection point.

0:26:26.4 TW: So can we, I want to use, so those 15 people who at least three or four of them were perfectly happy with their mind numbing, [laughter] clicking around and then because of you, all of a sudden they now had capacity where they were expected to push themselves and think. So yeah, I’m sure those people are happy with you. But I mean that actually triggered for me, we’re talking as though there’s a presumption that making that sort of leap is a good thing. I would even say it’s necessary. How do we, can you make the case that, you know what, I’m good with what I’m doing. There were 15 people who it didn’t, they didn’t have the right incidental conversation with somebody who said a script might be good for that. It didn’t occur to them. They were moving along. And I know I’ve worked with people who are like, it’s very, very frustrating ’cause they’re not intrinsically motivated to grow. And I feel like it’s like the shark has to be moving or it dies and it feels like, I mean, what do we think about, I feel like you can’t stand still anywhere in the data analytics space today. Like you literally will be moving backwards. But I haven’t figured out how to really make that case effectively.

0:27:51.4 KL: Clarifying question, Tim, when you’re talking about that example, are you talking about data people who just wanna keep doing what they’re doing without like improving or wanting to problem solve? Or are you talking about like marketers, stakeholders, product managers, whatever it is? If you say both, I’m gonna cry.

0:28:09.6 TW: No, no, no. More of the data and the analysts, I would say, I mean, and this is, I mean on my rant about data translators, I feel like, and I’ve got very, very clear pictures of specific people in my mind who have been in analytics and all of a sudden they’ve lived in whatever tool or two that’s drag and drop and doing their reports and they want to keep pushing out their weekly reports, monthly reports. And I’m like, you got to, you should explore these other things. And it’s not just the, I think a lot, some of them will get dragged into the data collection technical side. If it’s digital analytics, it’s JavaScript or the… Sure. There’s plenty of that. I think there’s plenty of people that get pulled into that that’s very deterministic specific. I’m gonna wire up the data. It’s trying to get people to recognize the efficiencies. Like the example, Kirsten, you just had like massive efficiencies to free up your time to not do mindless stuff but also start to try to grapple with these hard concepts. And there are people who are like, no, no, no, that scares me. That’s gonna be hard work. I just listened to this podcast that said it’s gonna be evenings and weekends. I’m gonna have to be internally motivated to do this. Why can’t I just be a data translator? I just need to know enough.

0:29:24.5 VK: Tim, the people who listen to our podcast are not the people that are not going to do the extra work because they’re already, like, we’ve already self-selected to a group name that are trying to expand their knowledge.

0:29:36.5 TW: I could name names, I could name names of the people ’cause they’re never gonna listen too.

0:29:41.3 MK: Probably.

0:29:41.5 TW: But I think our listeners, like, they’ve got to be running into those people too. Like if, ’cause I think Kirsten, if I heard you, I think I heard you tell that story another time. Well, even the way you just told it, like we might have glossed over it. Like, we should do this in Python. It’s like, no, no, no, that’s then a whole team has to learn Python. We shouldn’t, like you got told no first, so you had to say, screw that you don’t have my time, my personal time. I’ll show it to you. And that like, so making that case that like, hey, maybe it’s time that we all try to increase our skills or maybe I’m just imagining that’s an issue and I’ve had a few interactions with people who really infuriate with me with their lack of interest in doing that.

0:30:30.5 KL: Yeah. I think, to your point actually there’s two thoughts that come to mind with that. The first is that to most point, the team I was subbing for was a marketing team and there was a very, that’s that team, there’s not a super strong push for those teams to be way more technical than they are. But when after we moved to a more Python based process, some of them did quit ’cause they were very happy with like their Excel driven world and weren’t really interested in, I mean that freed up time for things like AB testing, which for some marketers is just like, no thanks, like that’s just too far. Like that’s too much statistics, that’s two inch math. Like I’ll just run my campaigns. Like, and that’ll be fine.

0:31:19.5 KL: And so that for them, that was just too far. And so some of them did end up leaving because the role was no longer something they were interested in doing. Which I think about that a lot. Like it actually makes me feel like I needed to do this responsibly. Where I wouldn’t have thought of that when I was in that, doing that at the time, my first job in a corporate setting. Like I didn’t really think about like that that could be an outcome. But in terms of like the data side, one of the things that I think is really true is that shark analogy that you use for, like, if you’re not moving forward, you’re kind of moving backwards. It’s like, putting your money in a savings account, like inflation just eats it, right? It’s not like you can put it in a jar and it stays the same value.

0:32:00.5 KL: It’s degrading in value over time. So you actually have to invest in order to get that to even maintain the level of value that it has. And I think technical skills are the same way. One of my favorite examples, and I rant on this all the time, ’cause you know, on Twitter people love to do those little like, here’s five tips that you need to know and to improve your ML model performance or whatever. And they always are like, make sure you’re filling your nulls, which was totally true like 10 years ago and it’s just repeated and repeated and repeated. But if you’re using tabular data, there is no reason to fill your nulls with a mean anymore. That’s not the way you do it. You’re not gonna get the best performance if you do it that way. But it’s just been repeated so many times that people still think that’s true.

0:32:39.5 KL: And so that’s like, that’s one of my triggers, [laughter], like you have not updated your ML skills in the last like 10 years. So, and to your point, it’s like that kind of stuff is all over an ML because it feels like magic. Like you’re doing this incantation and you have to do things in like this certain order and I’ve always done it this way. This is the way that I like do my magic spells and whatever [laughter] So like I [laughter], I just, but like when you turn it into a science, you see like, oh yeah, I actually need to drop this practice of filling my nulls with the mean because gradient boosted trees do that automatically under the hood and they get a better performance when I do. And so that kind of thing, that’s like one example of what I see happen all over in data. Things like how do I manage my data in a Cloud environment instead of this local like cluster that we were running under my desk. What’s the difference? And so like being able to uplevel this, why am I using Redshift instead of using X new tech instead of using Snowflake? Or why am I using, those kinds of questions you have to continually be understanding. Now there’s one last point. I don’t know, I’m monologuing, this is my thing because I just monologue.

0:33:46.4 TW: Oh, you’re fine.

0:33:46.6 VK: I was like, Tim is the king of monologuing. You’re fine. [laughter]

0:33:50.3 TW: Except mine’s not useful. So [laughter]

0:33:55.5 KL: Here is the counterbalance to that point though, which is data, what people call the modern data stack suffers from this fragmentation of technology and it’s very easy to get sucked into like learning the new tool that’s like VC backed and has a bunch of marketing dollars behind it. And like then you’re just whittling away your time on like 100 different things instead of focusing on like what are the skills that actually move the needle on the day to day. It’s probably not that new SaaS. You think about what are the core things that I need to know and what is evolving in those core areas that’s gonna help me do my job better? What’s making things simpler? What’s making things more scalable? What’s making things like faster and cheaper and without increasing the operational burn of adding a whole bunch of new tools into our stack. So it’s this tension between keeping your skills relevant and like, fending off the inflation [laughter] code of sort of phenomenon, but at the same time not getting distracted by all of the things that are trying to pull your attention.

0:34:56.4 MK: God, I love that so much. I feel like, yeah, I feel like I could talk to you about this topic forever. I do have a weird off script direction to go in which I am known for [laughter] You are incredibly smart and confident and I like, and yeah, very, very impressed as someone who’s had a similar, I guess, transition from like a non maths sort of stats background. And I also have talked to it like other people that are in a similar position. I know I’ve struggled a lot with, I don’t know if you wanna call it imposter syndrome or just like the lack of confidence when it does come to the technical stuff. And that’s not to say I haven’t done it. I’ve definitely done it, but there is, I feel like the doubt is maybe more significant than for people that do have a computer science or a math or a stats background. Is that something, ’cause like meeting you, it definitely doesn’t seem like that’s something you struggle with, but I know that that often isn’t like the truth. Right?

0:36:04.5 KL: Right. Yeah. I love talking about imposter syndrome so much because the advice that I tend to hear about imposter syndrome and actually before I get there I’m gonna talk about the first thing, which is so real to not have a degree in this space, not be connected to the academic part of this discipline. It’s so real that that’s a headwind. Like I think about all the things that I didn’t know, I didn’t know even now, sometimes I’ll be like, even now I find myself, despite having spent a lot of time reading white papers on things like, transformers and whatever, AI technology, I still have this moment every once in a while when someone’s like, new white paper dropped from so-and-so about dah, dah, dah. And I’m like, oh, can’t read that. ’cause I’m not an academic. Right?

0:36:55.0 KL: It still has. There’s a little bit of a rut in your brain that’s like, white papers aren’t for me because I’m an English major. There’s no way I’d understand all of that crazy math that’s in there, right? So it’s like this rut that I can tell, it’s in my brain. But what I find so interesting about imposter syndrome is the advice that I hear most often has to do with trying to increase people’s confidence in themselves. And what worked for me, that didn’t work for me.

[laughter]

0:37:22.3 KL: What worked for me was not taking myself so seriously. So instead of being like, no, I really am great, I really can do all this stuff. And it’s actually like, actually, it’s not that big of a deal if you fail, you’re not that big of a deal to where failure should not be something that you do, right? You’re a person, you’re a normal person, and it’s actually, even when you embarrass yourself, no one’s gonna remember.

0:37:52.9 KL: And that sounds almost sad for a moment. And then it’s also freeing, right? I’m just not that big of a deal. So that to me was really the key was like, oh, actually I can go into this meeting with a bunch… When I was doing that instrumentation work, these are software engineers, very senior software engineers. I’m putting documents together for the VP or SVP of a device manufacturing company. Having never studied software engineering in my life and having to make decisions where that decision comes right back to me. Kirsten made that decision for how this is gonna work for better or for worse, but knowing even if I go in that meeting and I bomb, I’m just not that big of a deal. They’re not even gonna remember.

0:38:42.7 KL: So what’s the alternative? Right? I don’t do it. And then the only one that suffers is me, right? I’m the only one that suffers if I don’t try, because if I try and I fail, no one else cares. And so if you look at it that way, it’s an obvious choice. It’s an obvious choice that you actually just take the swing and you keep taking the swings, and then suddenly you get to a place where people are like, wow, you know a whole bunch about whatever. And you’re like, yeah, but I’m still not that big a deal. [laughter]

0:39:14.6 TW: You start to learn that you’re like, I took a bunch of swings. I could have failed on 10 of them. I did it anyway. I did fail on two of them. Nobody cared, and I hit eight of them. ‘Cause it does seem like we tell these stories. I did a Google trend search on the word catastrophizing a few weeks ago. I just felt like I was hearing it more, and it has gone up and I don’t know, because I feel like I’m like, oh, I’ve hit an age where I just don’t give a shit. If I know something or I don’t.

0:39:42.6 KL: As opposed to me who literally did Google catastrophizing the other day.

0:39:46.6 TW: ‘Cause somebody used it and?

0:39:48.6 KL: No, ’cause I definitely do it. And I was like, I wanna under… Anyway that’s… So I’m part of your Google search trend data.

0:39:53.9 TW: Yeah.

0:39:55.6 MK: Yeah.

0:39:55.7 VK: You saw me.

0:39:57.1 TW: That was it. I had searched it for Australia, and sure enough, it went a much more recent spike. But I love that. I mean, you’re also doing good for the world by taking swings and fail. Like now you don’t want everybody doing stuff. And it’s like I don’t know, it’s that distinction between if you’re trying to do the right thing and you fail, you don’t matter that much and sometimes you won’t fail. And other people can see that it’s okay. So they can realize that it’s okay to try things and fail, but.

0:40:30.8 KL: Yeah. That’s true. And that is, especially in a discipline like data where I don’t know how many times I’ve had this light bulb moment where, oh, I could do something like this. And then someone’s like, you know that’s bootstrapping, right? And I’m like, no, I had no idea. I didn’t know what I heard people talking about bootstrapping. I had no idea what it was. Right.

0:40:49.2 TW: Okay. Can you literally, in 30 seconds explain to me what bootstrapping is? ‘Cause that is literally, I’ve even tried to Google it and I get confused if there’s a Bootstrap…

0:40:56.7 VK: Maybe you’re doing it too.

0:40:57.0 TW: No, I’m pretty sure I’m not. We can cut this out. I’m just like, if you can actually explain, ’cause it is a very difficult term for me to successfully Google.

0:41:10.0 KL: Honestly, all it is is imagine you have a dataset and you want to fit a model to it. If you fit your model over the whole dataset, you risk over fitting. So instead, you take a subset of that dataset multiple times and then average. And that’s pulling the subset is bootstrapping. That’s what bootstrapping is.

0:41:28.9 TW: So it’s kind of like cross validation, like fivefold, cross validation ish.

0:41:35.2 KL: Cross validation uses sub sampling as its technique.

0:41:38.1 TW: Oh.

0:41:39.8 MK: It’s so weird. When I heard bootstrapping, I was just thinking of funding startups, bootstrapping. That’s what I jumped to. It was like.

0:41:49.3 KL: Well, that our company is bootstrapped too, which just means we didn’t raise VC funding for it, which we’ve been profitable since our first day. That’s a whole segue into how data science is actually a very valuable skill that a lot of people are looking for. It honestly comes down to this idea. Data is really being able to manipulate is very valuable, and we built a company that’s bootstrapped because of that reality. So also bootstrapped.

0:42:15.0 TW: And just to round that out, there’s a whole JavaScript very popular library that is bootstrapped that also is completely unrelated, I think, and therefore my confusion, you got to know the context.

0:42:25.1 KL: Yeah. [laughter] That’s so funny. Can you explain the bootstrapping and JavaScript one?

0:42:31.8 TW: No, I just know that someone downloads JavaScript, I just know it’s a library that people are like, oh, I do bootstrap. My son could probably explain it to me, but no. Yeah, sorry. I guess I just put myself out there and revealed, didn’t worry about my imposter syndrome. I’m like…

0:42:50.7 MK: There you go. See.

0:42:51.4 TW: I hear that, and I don’t know what it means.

0:42:51.6 KL: And everything’s okay.

0:42:55.9 VK: And everything’s okay.

0:42:55.9 KL: Yeah, Tim, you’re not that big of a deal.

[laughter]

0:42:57.0 MK: Get over yourself, Wilson.

0:43:03.5 TW: Oh, well, and on that note, I hate to say that we are getting close to where we need to start to head to a wrap.

0:43:13.2 VK: Whoa. That came fast.

0:43:16.6 TW: That’s like bro, you know what, I’m done. I’m familiar with my shame, we’re ending this now, but we’re also kind of getting to the time where we would Val disagrees that we’re at the time.

0:43:29.4 VK: No, no, no, no. I would just love to ask a question that I’ve been sitting on, ’cause I didn’t wanna derail the part, but I’m gonna sneak it in Tim, if that’s okay.

0:43:34.9 TW: Okay.

0:43:36.9 MK: Sneak, sneak.

0:43:38.4 TW: Yeah, like I was gonna say we have time for one more question from Val. Got one?

0:43:45.8 VK: So I do. Yeah. So I also had a very non-traditional background getting into analytics, but I am not as technical as anyone on this call. And I find…

0:44:00.0 MK: But Val you’re literally one of the most technical people I know. You’re, hysterical.

0:44:02.3 VK: Oh my God, you’re ridiculous. Anyways, so I find that the perspective that I gained through some of my early training, I was a psychology major before I switched into data, and I find that skill to be very helpful in the way that I approach my analysis and my work. And so I’m very interested, especially because you’re working with, you’re talking about super technical people, especially on some of these teams at some of the places that you’ve worked here since. So do you notice that your experience and your ability to write and tell a story and a narrative has really helped you a lot with the way that you’ve been able to deploy some of your super technical skills, ’cause you’re kind of balancing both worlds in a lot of ways, but I’m just curious how you feel like that’s had an influence on some of your successes.

0:44:49.7 KL: Yeah, I mean, resounding yes, that my English skills have helped so much in my career as a technical person. And there’s even a couple layers to it that there’s some that are situational. I went to Amazon where it’s a document culture for meeting with your boss, you’re writing a six page paper every time you’re meeting with your boss, you’re writing pages and pages of narrative. So in that sense, but actually why I liked working at Amazon so much, that was part of why, because they really value being able to clearly explain your reasoning and taking a stance and then arguing why that stance is correct.

0:45:29.7 KL: And that is what English literature majors do. That’s what they teach you, is go into this actually unstructured data, which is a bunch of texts like books and poetry and all that. Go into this unstructured data, find a pattern, explain why that pattern is significant culturally, and defend yourself, defend that hypothesis that you have. And so that skill of constructing an argument and then defending it in using words is something that I think is actually one of the skills I wish more data scientists really paid attention to.

0:46:12.6 KL: ‘Cause that’s like I don’t know how many times I’ve seen that deer in the headlights look from someone when they show you the analysis they did and you’re okay, so what’s this, so what here, why are we looking at this? What are you trying to convince us of by showing? And they just like, don’t know, this is just what they asked me to pull. So that look. And so that’s where you really get down to like, okay, here’s how you structure an argument. By the way, side note book called Pyramid Principle was really foundational to how I learned that skill of structuring an argument. Pyramid principle and that’s what it does. It looks at logic from a philosophical standpoint. Here’s logical argumentation, here’s how you develop that. Here’s how you write it in words that are clear. Don’t worry about things like use parallelism, make things repetitive because people are gonna actually understand it better, even though you feel like they’re gonna get bored. No, they’re going to be interested because they understand. So those kinds of things, learning how to communicate those things. Yeah, 100%. Something I use every day.

0:47:13.8 VK: I’m literally googling that book right now to be like, oh my God, can I buy it for everybody in the team?

0:47:20.3 KL: I did. In fact, my husband, he also worked at Amazon for a while, and that was one of the things he did was he took his entire team through The Pyramid Principle and he said, we are going to study this book and then we’re going to use what we learn in every single paper that we write. For anyone that reads it, we’re gonna use all these principles for how to do structured argumentation. And it was transformative for a lot of people. Yeah.

0:47:43.6 TW: That’s just a plug for group book groups, team book groups to force people to read. I’ve done that two or three times where like if this really seems to matter, let’s all be in this together and hold each other accountable.

0:47:56.8 KL: Yeah.

0:47:58.0 TW: Okay, well now we’re gonna have to talk fast. No, we’re fine. Well, that’s been a great discussion, but we need to head to wrap. And before we close out, we like to do a thing we call the last call where we go around and everybody shares something that is interesting to them, might be of interest to the audience. And Kirsten, you’re our guest. So do you have a last call to share?

0:48:23.7 KL: Yes. And this is gonna be complete left field from anything we’ve been talking about except I guess maybe coming back to this idea of working nights and weekends and how that can be kind of straining and how, I don’t recommend that be the norm, but it’s an exceptional case where you’ve really got this need and motivation. But one of the things is running a company, and I have two little girls, and so one of the things that has become more important to me is taking care of my health. And as a data person, I am so annoyed with how little, I feel like I’m missing the analytics for my body and my health. I always feel like I’m not making data-driven decisions with my health and which is drives me nuts.

0:49:07.5 VK: Me too.

0:49:08.1 KL: Yeah. And you can’t find anyone who will help you either. That’s what it feels like. Honestly, I’m being hyperbolic, but I cannot find someone who will help me. And so it’s been this journey. My husband and I have been going on this journey of really digging into the science and the data around health. And there’s three things that I’ve learned that I think have changed my perspective on being able to manage my own health myself. Number one is that genetic testing is becoming much more accessible to folks, and that your genes actually have a reliable relationship to some of your experiences that I think it’s becoming much more accessible to just a consumer to be able to do. And I’ve done a few genetic things. You’ve probably heard 23andMe or I’ve done 23andMe, but they don’t test all your genome, especially a few parts of your genome that can be really impactful to health. And so there’s a company called sequencing.com that will do whole genome sequencing. That wait list for how long it takes them to do it just keeps getting longer and longer and longer and longer. So everyone that I know that’s even partially interested in that, I’m like, Hey, you should probably do it ASAP. So you can like, it’s still gonna take ours months to get back.

0:50:17.3 TW: They’re probably using Excel and they need a Python script, ’cause…

0:50:21.8 KL: Yes. What are they doing that needs to be automated? I do think about that actually. But as a data person, it’s like, what better than this massive data set about yourself that you can go analyze and learn about? And related to that is there’s a part of your genome that’s responsible for basically your metabolism. It’s called the methylation cycle. I’ve learned. And the methylation cycle has to do with how your body processes all of these inputs and turns them into the outputs of energy and things like that. And getting your genome tested or getting your gene sequenced allows you to say, are there parts of my methylation cycle that aren’t efficiently working? And so I found out that I have certain genetic markers for, I don’t process certain kinds of B12, so that means if I’m not supplementing with a specific kind of B12, then I’ll probably be have low energy. So it’s like those kinds of very actionable things that you can actually take. And it’s changed my health massively. And here’s the last one. I know I’m again doing my monologue last one, but this is the biggest.

0:51:25.2 TW: You’re good, this is fascinating.

[overlapping conversation]
[laughter]

0:51:25.8 KL: My biggest health change in the last year. My husband found something called exercise with oxygen therapy, EWOT. And it’s basically a pump that draws oxygen out of the air and then fills a reservoir with it. Imagine a big balloon, and you use this, you put on a face mask and it pumps in 94% oxygen into this face mask. And you do that while you do zone three cardio. So I do it while I’m on an assault bike, basically. And as long as you can stay in zone three for 15 minutes, okay, 15 minutes is all you need with this. It’s like have all of these correlations with better health and better recovery and better oxidization of your body and things like that. And here’s the kicker. I have exercise-induced asthma, which means I haven’t done cardio in 15 years, none. The only thing I’ve done is push my kids in strollers, and I got on the exercise bike and did 15 minutes flat the first time using EWOT.

0:52:22.7 KL: So from zero to 15 minutes in zone three cardio and I didn’t have any pain. Exercise induced asthma gives you just burning pain and zero pain. I was floored. And that’s one of the things we were like, why did all these doctors that knew I had this problem, not Know and or suggest this intervention? You’re just like, why? Who’s supposed to be responsible for knowing these things? It turns out it’s us. We’re supposed to be responsible for knowing these things. So anyway, those are the three things that have been really changing my life in the last year or so.

0:53:00.1 TW: Wow. Those are wild.

0:53:01.1 MK: Yeah.

0:53:01.4 VK: Yeah. Someone asked. I was gonna say, I don’t wanna follow that up.

0:53:02.3 MK: I don’t wanna follow that.

0:53:03.9 TW: Eeny meeny, miny, Moe.

0:53:06.5 VK: Damn it.

0:53:08.2 TW: What’s your last call?

0:53:10.1 MK: Yeah, mine keeps really lame. So, well, actually, I shouldn’t do Adam Grant a disservice ’cause we all know I love him and I’m obsessed with him, and I follow him on every platform imaginable. I also receive his email newsletter Granted, and this particular latest one has really piqued my interest because the title of the email is Stop Serving the Compliment Sandwich, which is something I had been known to do. And someone in particular on my team is always like, Moe, don’t give me the compliment sandwich. Just give it to me straight. I’m fine. And so I no longer do it, but it’s just made me kind of reflect on like, oh yeah, that makes a lot of sense. So he talks about when you give a compliment sandwich, you mean to give someone a genuine compliment and then constructive criticism and then follow it up with a genuine compliment.

0:54:03.1 MK: But that’s not actually what they hear, right? Because they know they’re getting feedback. So they tend to hear obligatory compliment, criticism of who I am, and then another obligatory compliment. And so they’re probably not going to receive it in the way that you hoped they would. And I’ve been seeing Adam Grant, obviously, like I said, I see a lot of him in all of my feeds, and he keeps sharing this line that just is really like, I don’t know, I keep hearing it and being like, oh, damn, that’s good. This is why I like you so much. I’m giving you these comments because I have very high expectations of you and I know that you can reach them. You start that off as your feedback conversation. And anyway, I did test it out the other day. I’ll report back as I keep testing it and let you know how I go. But I just love that shift of how we give feedback.

0:55:00.5 TW: Oh, I love how engaged you’ve been in this episode. I really wish you’d stopped referencing Adam Grant, but…

0:55:07.8 MK: Wait, what?

0:55:10.1 TW: Shit, I’m trying to do a compliment sandwich.

0:55:10.8 VK: He’s giving you a compliment sandwich.

0:55:12.3 TW: And like I’m the shit out of it.

0:55:15.5 MK: Oh, man. I did not follow that. You look so confused.

0:55:16.5 TW: I couldn’t finish the term. Yeah.

0:55:19.9 MK: You look so confused.

0:55:21.7 TW: Apparently I don’t need to stop doing the compliment sandwich ’cause that felt really unnatural. So, oh, good God. Val, what’s your last call?

[laughter]

0:55:32.6 VK: So I, every now and again, listen to some of the TED Talks daily podcast, and if you did your full blown measurement plan of your New Year’s resolutions and you’re checking in and wondering, oh man, why am I not meeting my goals? I might recommend a listen to this one. I listened to it right at the end of the year, and it’s called, this episode in particular is called The Science of Happiness with Laurie Santos and How to Be a Better Human. And she is, first of all, super fun, super engaging, super interesting, and has constructed all these experiments to understand what actually causes happiness in humans. And did things like set up a system amongst monkeys where they had payment and had money, and to see if money actually can drive happiness, or do they make poor decisions with money based on risk like humans do? So super interesting. I love the way she deconstructed how we think about self-care. And I also have taken on some of her recommendations for the things that she has diagnosed that truly drive happiness around connection and people. And so far so good. So highly recommend that. She also has her own podcast, The Happiness Lab, which I haven’t checked out yet, but she is…

0:56:45.8 TW: I was gonna say, I was thinking of The Happiness Project. I’m like, I know, yeah, the Happiness Lab. Yeah.

0:56:49.4 VK: Yeah. I can’t believe I haven’t come across it yet, but it was, she’s a Yale professor and she’s just super engaging and interesting, and it was a nice little concise, here’s some things you might think about if you wanna inject a little more happiness in your life. So that’s my last call for today. So what about you, Tim?

0:57:08.9 TW: I was like, Laurie Santos. That’s The Happiness Project. The Happiness Project, and that wasn’t it. So as I was Googling, I was, I was so close, it was The Happiness Lab. So my last call is just straight up a read from past guests, always entertaining read, Katie Bauer, the Wrong But Useful. Substack. And she always has just smart things to say, but she wrote a while back, she had a post called Beware the Sloth, so it’s kind of like there’s the hippo, and she was like, yeah, well, the sloth is somebody who’s much more, they really, really wanna use the data, but they kind of get in the way in other ways. And she comes up with an acronym and kind of pokes funded herself for trying to force it. But it just made me think about having sometimes the people who come and say, I’m really data driven, and then it somehow winds up being really, really frustrating to work with them. And I was like, oh, she kind of broke down different types of those people and made recommendations on how to work with them. So it was a good read. So wow, these are some bangers I got some follow up reading to do from this. So thanks. And Kirsten, thanks again for coming on the show. This was every bit as fun and engaging and interesting as we were hoping it would be.

0:58:30.6 KL: Thank you.

0:58:32.1 TW: If people want to find you or connect with you, I know you’re on Twitter at the, @machsci, it’s like machine sci, M-A-C-H-S-C-I.

0:58:41.0 KL: Yes.

0:58:43.3 TW: How do you pronounce that?

0:58:43.4 KL: M-A-C-H-S-C… I say machsci.

0:58:44.1 TW: Oh, machsci.

0:58:46.4 KL: Like Mach, like Mach Five. You know I’m going so fast.

0:58:49.7 TW: Oh okay.

0:58:50.7 KL: Yeah. But machine learning and science, M-A-C-H-S-C-I on Twitter/X. And then you can also hear more about storytellers @storytellers.ai and would love to, as you can tell, we talk a lot about how data science can impact these maybe far-flung parts of organizations. So always happy to have a conversation about that.

0:59:14.9 TW: Awesome. So if you, dear listener, would like to reach out to Kirsten, if you’d like to reach out to the podcast, you can find us on LinkedIn, on the Measure Slack, on Twitter @analyticshour. You can also, if you’re in the US or willing to travel to the US, those of us who are based in the US are actually gonna be at Marketing Analytics Summit in Phoenix from June 4th to 7th. So marketing analytics summit.com, I think Val and I are both moderating tracks. And then we’re also gonna be recording an episode of the show while we’re there, as we’ve done in the past. I’ll also put a plugin, ’cause we don’t do it often enough, that if you want free stickers, you can go to bitly/aph-stickers and you can get yourself some free stickers of the podcast if you wanna slap one of those on your phone or your laptop.

1:00:09.1 TW: We’d also always love to get ratings or reviews on whatever platform you listen to if it supports that sort of thing. And no show would be complete without thanking our illustrious producer, Josh Crowhurst, who makes all of the little hiccups we had throughout this. If you listen to the outtakes, you’ll realize that that was stuff Josh had to pull out of the middle. So always fun for him, and we appreciate Josh and the work that he does. So regardless of how technical you are or how technical you’re going to be, for Moe and for Val and for our guest, Kirsten, keep analyzing.

[music]

1:00:55.8 Announcer: Thanks for listening. Let’s keep the conversation going with your comments, suggestions, and questions on Twitter at, @analyticshour, on the web @analyticshour.io, our LinkedIn group and the Measure Chat Slack group, music for the podcast by Josh Crowhurst.

1:01:12.4 Charles Barkley: So smart guys want to fit in. So they made up a term called analytics. Analytics don’t work.

1:01:19.0 Kamala Harris: But, I love Venn diagrams. It’s just something about those three circles and the analysis about where there is the intersection, right?

1:01:32.9 TW: But we need to head to wrap, and as we always do on this show, Kirsten just looked really excited about something.

1:01:39.8 KL: Do you know why?

1:01:40.0 TW: No.

1:01:44.9 MK: And he was not wearing the red checked pajama pants.

[laughter]

1:01:50.3 MK: Sorry. I was trying to control it but I could not.

1:01:55.4 TW: Come on over.

1:01:55.8 VK: We just wanna check out your pants.

[laughter]

1:02:00.5 MK: Oh, the poor guy. The poor guy.

1:02:03.8 TW: I’ve been on multiple calls where he just passes through.

1:02:12.8 VK: Sorry.

1:02:15.4 TW: I missed that. Apparently I was just blind. I completely missed. I’ve gotten to where I tuned it out. Okay.

1:02:18.0 MK: It’s like the gorilla thing.

1:02:20.2 VK: Exactly. I can’t wait to tell him since I have my headphones in. What a part of this episode he’s been. Rock flag and pixelated view of reality.

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