#208: Charting Your Path into Data Leadership with Katie Bauer

You’ve got some solid experience under your belt, and you’re starting to feel like you’re ready to move into a data leadership role. What does that even mean? Shifting your keystrokes from SQL to slide decks? Maybe (but maybe not). Katie Bauer, Head of Data at GlossGenius, has held multiple data leadership roles over the course of her career, and she penned a thoughtful post on the various tactics she employed to find a role that is a good fit. She wrote the post so that she wouldn’t have to keep repeating herself when data folks in her network reached out for advice. But that didn’t stop this podcast from reaching out to record a lively discussion on the topic!

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Episode Transcript


0:00:06.2 Announcer: Welcome to the Analytics Power Hour. Analytics topics covered conversationally, and sometimes, with explicit language. Here are your hosts, Moe, Michael, and Tim.

0:00:16.9 Michael Helbling: Hey, everybody. It’s the Analytics Power Hour, and this is episode 208. I don’t know what they want from me. It’s like the more money we come across, the more problems we see. This is the chorus to the ageless classic Mo Money Mo Problems on Notorious BIG’s Life After Death album. Who knew these words could ring so true, even across the analytics industry. As your career grows, so do your opportunities for leadership, greater compensation and impact. But it’s sort of paved with problems, too. That’s why we talk about it. All right. Let me introduce you to my co-hosts. Moe Kiss, you’re the Head of Marketing Data and Analytics at Canva.

0:01:03.6 Moe Kiss: Not that exact title, but sure.

0:01:10.2 Tim Wilson: Again.

0:01:10.3 MH: Pretty close.

0:01:10.8 TW: Keeping it going with butchering her role.

0:01:11.6 MK: It’s all good.

0:01:11.7 MH: Well, you know. Pretty close.

0:01:15.7 TW: Keep the streak alive.

0:01:17.0 MH: But you probably, yeah. What is it actually? Let’s get it right.

0:01:17.3 TW: Marketing Data Lead, just to really muddy the water.

0:01:20.5 MH: Marketing Data Lead. Okay. But I think what I said was better. But yeah, Marketing Data Lead at Canva. And Tim, you’re Senior Director of Analytics at Search Discovery. Welcome.

0:01:33.6 TW: Happy to be here. Nailed the title. Good job. Since you gave it to me.

0:01:39.4 MH: I created those titles.

0:01:41.2 TW: You signed the offer letter. [laughter]

0:01:42.3 MH: I’m Michael Helbling, the managing partner at Stacked Analytics. But we wanted to bring on another voice into this conversation. Somebody who’s living this out in real time. Katie Bauer is the Head of Data at Gloss Genius. Prior to that, she ran the Infrastructure Data Science Analytics team at Twitter and led the Consumer Data Science team at Reddit. And today, she is our guest. Welcome to the show, Katie.

0:02:05.6 Katie Bauer: Thank you. I’m very excited to be here.

0:02:07.9 MH: I also see you post pretty regularly on the locally optimistic Slack group, too. So shout out to locally optimistic.

0:02:17.6 MK: Why is everyone good on that Slack group? And I just need to like get my shit together and get more involved with it.

0:02:21.6 MH: It’s an amazing group. It’s so good.

0:02:23.8 MK: Like every person that I like and admire is in that group.

0:02:24.6 MH: It’s a great Slack group.

0:02:26.7 KB: They have good discussions. Yeah.

0:02:29.7 TW: And Michael’s there, too.

0:02:33.3 MH: Yeah, but I don’t say anything. I just soak it in.

0:02:38.1 KB: It’s also a good place to ride on coattails. So that’s a valid strategy, too. Just go and get the good content.

0:02:42.6 MH: Yeah. I’m a big time lurker. I try to contribute here and there, but these people are so smart on that group. Alright. So Katie, welcome and talk to us a little bit about this because I think what maybe kicked this whole thing off was you were writing about some of your experiences as you were considering your role as you’re moving to Gloss Genius probably recently. And so the process of interviewing, considering options. So talk a little bit about that experience and then we’ll kick that off into the conversation and try to take it a little more broadly.

0:03:13.6 KB: Yeah, sure. So the origin of this conversation probably is mostly a post I wrote on Substack a couple months ago that I wrote right as I had just finished my most recent job hunt. And something that was interesting to me about job hunting when I was, was there was a lot of job opportunities, but not a lot that I actually wanted.


0:03:35.5 KB: And at first I started off being very broad in my approach. I just wanted to talk to a bunch of different hiring managers and see what they were looking for and what types of roles existed out there. And over time, I began to develop very strong opinions about what I thought was actually worth my time or worth my interest, I guess, based on where I would like to take my career. And as I started telling people and sharing the news, I started getting questions about like, “Well, how did you decide? How did you actually figure out that this was gonna be a good version of a Head of Data role?” ‘Cause I also know tons of people who have stepped into roles with big titles like Head of or Lead that they get there and they’re totally surprised by what they’re asked to do. So I thought it might be helpful to actually kind of write down my experiences and share them with people. And also, I wouldn’t have to keep telling the same story over and over again ’cause I could just send them a link.

0:04:25.4 TW: And now you end up on a podcast. You’re like, “Ah, that backfired.”

0:04:30.4 KB: No, it backfired.

0:04:30.4 TW: Just read the article. Come on.

0:04:33.0 MH: That’s right.

0:04:35.0 KB: Well, we can perhaps get into more color.

0:04:39.4 MK: Okay, I’m just gonna be clear. Like I can pummel Katie with questions. Like I could talk about this topic until the cows come home. So I just wanna make sure I’ve got the all clear to do that.

0:04:49.1 TW: [chuckle] Have at it. I mean, pummel, like that’s a nice intro.

0:04:53.9 KB: I prefer not to be pummeled. I’ll take questions.

0:05:01.2 MK: I wanna start with the topic of titles because you do talk a lot in your article about titles and it is something, especially for me, who works in a marketing space, it can be even trickier because often the data folks that work with marketing don’t have these fancy titles but the marketers themselves do. And that can be like a, feel a little unequal. I don’t even know how, like what is the right title for a data leader? I don’t even know. Yeah. Like you talk a lot about the title of head off and I agree it’s really vague. There was no question here. Just like me struggling with my…

0:05:42.6 KB: Yeah. Yeah. Just like what are my reactions? What are my thoughts?

0:05:42.8 MK: Yeah.

0:05:43.7 MH: Mikey, you caught a case of the Tim’s there though. That was, wow.

0:05:49.1 KB: Yeah. Well, I guess like kind of what I think you’re getting at partly is a phenomenon that I’ve seen a ton for people in data roles, which is that their title and scope is inappropriately broad like in terms of pairing. Like you’ll have someone who’s like hired in as like an entry level Data Analyst, but then their stakeholder is a Director or a VP or something, like this is something I’ve experienced many, many times throughout my career, where like maybe I’m a line manager, but like my customers are, are Directors or Senior Directors. People who have a lot more organizational authority than I did, which creates problems. Like people always tell you that you can influence without authority and like, yeah, you can. But there’s still like a lot of technicalities that make it hard to do your job if you don’t have a title that matches the level of seniority and scope that you actually have. Like I think that’s maybe partly what you’re getting at.

0:06:41.1 MK: The funny thing is I always just thought that was part of the job of being like a data person is that you might be in a lower level and you’re gonna be exposed to Senior Decision makers because that’s the job. So it’s really interesting to hear you say that.

0:06:57.0 KB: Yeah. I mean like I do think to a certain extent it is part of the job is that you should expect to work with people who are more senior than you. But I don’t know that that’s the way it should be. Like a lot of problems that data people encounter I think are a consequence of their job not being taken seriously enough or just not like people thinking about it as like a real profession or like having a real skillset that is like uniquely a part of an analytics skillset. Like so many people do data analysis, but analysts are better at analysis and like they should be seen as better at analysis than other people that they work with. Like maybe they’re faster at it. Maybe they have more nuance in the way they do it. Maybe they see things that other people wouldn’t be able to identify. Like I do think there are real skills and real things that make someone good at this job and titles should respect and reward that this is like a hard one skillset. And as much as possible, I do think leadership titles should also reflect that.

0:07:56.3 KB: I think, historically another big challenge because there’s not a lot of standardization across the industry is the job title a lot of times doesn’t necessarily reflect the role. And so a lot of times even when you’re interviewing someone and they’re like, “Oh, yeah. I’m a Director of Analytics,” and you press on that and find out they’re basically managing the Adobe analytics implementation. And you’re like, “Okay, so you’re not really a director are you?” And they’re like, “Well, no.” It’s hard because what do you do? And so it’s what you said, like there’s people who are doing way more, like maybe they’re being called a Senior Analyst, but they’re living at that Senior Director or Director level. And there’s people who’ve got these highly elevated titles that are doing very basic types of jobs. And so it’s tricky and we haven’t developed as an industry far enough, I think, to sort of give people sort of like your title means these things, like a lot of other functions maybe in corporate America might provide.

0:08:48.2 TW: Are there two separate issues? I think I feel like Katie, your article gets a little bit into, there’s one is like, what is the job and uncovering that. And then there is the title, ’cause it’s pretty… I mean, a lot of people are like, “I don’t care about the title. I don’t care about title. I just wanna do interesting work.” But you actually, I think make a pretty good case. Like, “Well, the title does matter.” Like, “Let’s not just like dismiss it as like,” right? I mean, can you talk a little bit about that?

0:09:19.1 KB: Yeah, definitely. No, I mean, I think that’s a good characterization of what I was trying to say, which is really that a title should give someone some meaningful indication of what you do. And like, I do think that there is an impulse in people who work in data to be overly specific. Like people really wanna hammer out, like, “What’s the difference between a Data Scientist and a Data Analyst? Like sometimes, the difference is a Data Scientist is an analyst who works for a big tech company, in my experience.

0:09:48.2 MH: Analyst who works in the Bay area, yeah, that’s the…

0:09:50.2 KB: Yeah. Or yeah, like a Quant is a statistician in New York and a Data Scientist is a statistician in California. Like there’s all kinds of quips about this. But like the thing about job titles is that like people don’t have a lot of time to process like all the specifics and all the like full like curve and trajectory of your career. So like a title is a useful shortcut. And people make a lot of judgments about what is narrow, what is broad. Like one of the reasons why people prefer data science is that it is a broader title. Like it’s, it gives you more optionality. Like people have kind of negative views of analyst titles a lot of times, I think, which is totally wrong. Like analysts are one of the most useful functions at any company in my opinion. But like people think it’s dashboards or they think it’s running ad hoc queries or running a help desk. They don’t ever think of analytics or analyst titles as people who do stats, for example. There’s no reason why that can’t be, but because people have those judgments, they start pigeon holing people before they’ve ever talked to them, make assumptions about like what they’re capable of doing.

0:10:54.3 KB: And for that reason, titles become important because they also help communicate kind of like, what is your full skillset? What the correct title is for someone, I don’t really know. Like I don’t know that we’ve ever come up with like a good generic title for people who do this type of work that people don’t have like a lot of loaded judgments about. But like you should still care about your title and making sure that it conveys things that will help you in the future. And I guess with people at your company who don’t know anything about you. Like the same thing works with seniority. Like if they don’t see senior in front of your title, they might not think that you should be in certain conversations, if they have no one to correct them.

0:11:33.0 MK: We actually were chatting about a situation recently, where like a pretty flat company that doesn’t have a lot of titles and there are pros and cons to both approaches, right? Like there and one of the situations that’s come up recently, which I was kind of unaware of and I was like, “Oh, that’s a doozy.” It’s like when you’re in a meeting room, you have an intern that started a month ago and you have a senior engineer or some companies would call them a principal engineer or whatever who’s been there since, over 15 years of experience, et cetera. And some of the senior engineers are kind of like, “Well, it’s great that our intern can like throw a thought bubble out into a meeting. The issue is that their contribution is given the same weighting as mine.” And I’m talking about like some significant risks or to the project getting delivered or complexity about how we’re gonna solve it that just mean that an option isn’t feasible. And like that makes my job really hard because how do I convey that actually my experience is worth more. And I was like, “I heard this the other day.” I was like, “Oh, that’s a tricky problem.”I feel like titles is one of the things that would help solve this.

0:12:47.8 KB: Yeah, definitely. And like anytime you start hearing about a really flat org structure, like maybe it’s working, but whenever I hear that I’m like, “Eh. I don’t know.” Like hierarchy does have some purpose, even if it’s only conveying who’s the person who makes the decision at the end of the day. Like you do need to have people who are accountable for driving things forward and making decisions and saying when something is done and when you have no job titles to help you with that, it can be hard.

0:13:16.3 MK: I think even as we’ve grown, I have a few friends that have joined the company that have reflected as well and said the bit that’s really hard is also knowing who to contact because often you will contact like the Lead or the Head of or whatever, and then they point you in the right direction. And instead you just have like 10 analysts and you tend to contact the one that you spoke to last. And so then that can also have flow on effects about how work comes into the team, how it’s prioritised, all that sort of stuff. So yeah, there are definitely pros and cons to different approaches.

0:13:51.4 MH: Let’s move away from title a little bit and talk a little bit about the process because I think you wrote really nicely. And probably in your experience and stuff, maybe you didn’t even put in your article about just sort of evaluating through the interview process what to expect from that role or that job and how to identify mismatches in terms of what they say they want versus what you think they’re probably gonna be asking you for. And talk a little bit about that process and how did you kind of… I don’t know. Did you have sort of like a strategy for that or did you sort of observe it happening and adapt in real time? How did that sort of come about for you?

0:14:28.9 KB: Yeah, I mean, there’s definitely some table stakes things that I would want to know for any management position I step into, which is what am I managing? Like what people are there? Like what’s their experience? What’s their level of seniority? Who did they work with? Like those are just very basic things that you should in any position you’re going into try to get a sense of. ‘Cause your job’s gonna be super different. Like some people have not had experiences managing super junior teams or super senior teams, but it’s very different and knowing what you’re getting into is helpful.

0:15:00.9 TW: It’s truly, yeah.

0:15:01.0 KB: Just so you can figure out like, “Is this something that I am ready for at this point in my career?” Or like maybe you’re ready for it, but you don’t have the energy or the enthusiasm, or maybe it’s just not aligned with how you personally want to grow. Like getting some baseline understanding of what your day-to-day is gonna be like is kind of important. But there’s a lot of other things that are really important, too, about getting to know a company that you’re going to go into. And for me, like one of the most important things as I was looking was does this company have like executive level representation for their data team? And if they don’t, can I be it?

0:15:44.1 MK: That is bloody brilliant. O.

0:15:46.5 KB: I mean it was pretty hard to find honestly. At least like at the level that I wanted, there were a lot of companies where there was some other functional lead who essentially was going to be able to be the voice of the data team in an executive conversation, but they didn’t really seem to me like someone who would do that well. Like there were all kinds of different small things and different conversations that would like kind of catch my interest and tell me like this company’s vision for a data team isn’t aligned with what I want to do or what I think works. And like looking out for those things was one of the most critical things for me in deciding where I wanted to go next.

0:16:29.3 MK: I think that’s because people are so good at being like, “Oh, we’re like trying to enhance maybe what their data literacy or their data practice is in an interview,” as being further along maybe than it is. And particularly because often the people who are interviewing for a senior data hire aren’t actually data people themselves. So they don’t actually understand the maturity of where they’re at. And everyone wants to be like, “Oh, we’re doing so good in this space.” I feel like figuring out where they’re actually at, like some of the questions you recommended in your article are fantastic at doing that because it’s hard to do. Like it’s actually really hard to figure out where they’re really at versus where they think they’re at.

0:17:11.0 KB: Yeah. Well, like another thing that I think is maybe implicit in what you’re saying is for the people who are interviewing you, who are not data leaders themselves, like from their perspective, they might be very data savvy or they might like have a really good idea of how they’re going to work with data. And like it probably is good from their perspective, but like to be the person on the other side of that who has to make sense of what they ask for, who has to make sure a team feels good about doing that type of work. Like that’s like a whole other separate issue that you definitely wanna get at. And like it’s also kind of related to what I was saying earlier, where analysts should be the best people at analysis at the company.

0:17:51.1 KB: Like if you’re gonna go in as a head of data, like you have to be the standard bearer of quality for data analysis at that company. And like you have to feel comfortable and the people you’re talking to have to also be comfortable with this, but you have to feel comfortable like having opinions about what is good analysis or not. And they have to be comfortable with you having those opinions and trying to enforce them on the rest of the organization.

0:18:14.9 TW: So like how many… Like Ballpark, how many with the, maybe the current role. So you’ve been through several, so it sounds like you really kind of knew at least intuitively what you were looking for. So how many, how many interviews, how many different companies did you talk to and did you have any specific ones where you were like, “I don’t know. I almost got into a fight with them because they didn’t see.” I don’t know. I mean like there are those times where you’re like, “Wow, they’re saying they’re just dropping buzzwords and don’t know their ass from a hole in the ground. And this is not a fit, but I’ve got to… I’ve got to get through this.”

0:18:51.0 MK: She’s not gonna name them on a podcast, Tim.

0:18:51.1 TW: Well, I’m not saying…

0:18:52.4 MH: No, never name the specifics.

0:18:54.8 TW: But I’m like, like, what company specifically?

0:18:57.8 MK: Yeah. Who do I need to blacklist forever?

0:19:00.9 TW: And what was the name of the… Yeah. I mean, along those lines, like it feels like sometimes you could pick that up right away. Like, yeah. How many times, how many companies did you talk to before you said, “Ah, this one feels like it’s the right fit on that front?”

0:19:14.7 KB: Yeah. I mean, interestingly, the company that I ended up going to is the first one I talked to. It was a high quality referral from a friend. So that made a big difference. But like in terms of who I talked to, I probably at least like intro hiring manager call, talked to like 20 companies or something. And I really only went through process with four that actually seemed like they were good fits, whether it was like a right match of like the level of seniority, the maturity of the team, the types of roles they were hiring for. I do remember, specifically, one conversation talking to the hiring manager and we had very strong disagreements about whether analysts should write code. And that was a hard no for me.

0:19:58.3 MK: Wait, sorry. They thought they shouldn’t.

0:20:01.5 TW: Wait.

0:20:01.6 KB: They didn’t think of SQL as code for one, but like it was mostly just like, “This is a reporting function.”

0:20:08.3 MK: Wait, was this person a head of engineering?

0:20:11.6 TW: Yeah, it was an engineering leader.

0:20:16.6 KB: This company also had data scientists and I asked what the difference was between data scientists and analysts and if they would ever sit in the same org and they gave me a hard no, which is… I mean, I only talked to this hiring manager once, so it wasn’t a big deal.

0:20:27.9 MH: But that’s like kind of liberating, right? Like you immediately are like, “Not a fit, not a battle I wanna fight.” I will just be miserable. So good. I got good information and I can move on to the next. Yeah.

0:20:39.0 KB: Yeah. I mean like hiring conversations are a process of mutual selection. Like if it doesn’t sound like it’s going to work for you, you don’t have to go through the process. You don’t need that job unless you’re like in a very dire situation, I guess. Even then, don’t do something that’s gonna make you miserable.

0:20:54.2 MH: But I do think sometimes there is a temptation, especially as you’re rising in your career, to wanna go to, even if you sense that misalignment, like, but… It’s Facebook or it’s Google or like, “I’ve got to work there. Like why wouldn’t I?” And like, it’s hard to talk people out of that, you know? Like it’s, even though it’s like, you’re gonna be unhappy for the next two to three years until you basically give up on this idea. And I don’t know, was it… Oh, yeah. Tim and I used to kvetch to each other back in the day about our jobs. And Tim was like, “Are you sick of your job yet?” And I’d be like, “No, I’m still trying to make it work,” you know? But eventually I would go back and be like, “Yeah, Tim, you were right. This totally sucks.” But yeah, like it’s a real temptation. I don’t know. How do you think analytics people… How do you think analytics people try to avoid that trap?

0:21:44.2 KB: What trap specifically? Like just trying to chase prestige? Is that what you’re saying?

0:21:51.8 MH: Yeah, to a certain extent, but it could also be title. It could be money. It could be prestige.

0:21:56.0 KB: But how some people care about prestige.

0:21:56.4 MH: Fair, but then maybe they don’t care about analytics.

0:22:00.3 MK: I have a friend who chases titles because they want to be a CDO before they’re, well, I think it was 40 and I think it now has been reset to 50. Like that’s the thing that matters to them. And like, I’ve had this conversation with friends that are at similar levels of other companies. Some people are like really motivated by what they’re actually doing. Some people are really motivated by like the team or the company values. Some people are motivated by pay. Some people are motivated by title or the prestige of the company they’re working for. And I don’t agree with them. I think they’re making some shitty decisions, but like that’s their choice. And I just kind of go, “Cool, what can I do to help you if that’s the thing that matters to you?”

0:22:43.9 KB: Yeah. Well, I mean, like what that is partly getting at is people want different things at different points in their careers as well. Like going to a big company can be a great thing, whether it’s money or prestige or like being a part of a large organization and seeing how things work. Like the lessons you learn at a big company are valuable and you should think about whether that’s what you actually want. Like if you’re going to one to learn those things, that’s great. If you’re going there because you believe in the mission of that big company or you wanna work with a particular person there, those are all great reasons to go work for a big company. But you don’t have to work for a big company if you don’t want to. ‘Cause there are also a lot of things that suck about big companies. They’re slow, they’re bureaucratic. A lot of times their technology is actually, at least in the data space, older. There’s not really a chance to use new industry modern tools. So you need to think about what is important to you. Like you might not be able to get everything you want at a new job, but being careful about which of the things are like must haves versus nice to haves, making sure those are aligned with the opportunities you pursue is a pretty important thing.

0:23:44.1 MK: So just to kind of circle back, and I hate people that say circle back and I find myself saying it all the time, to that comment you made about like interviewing being, like I’m the same. I see it as like a two-way street and I’m constantly like counselling friends sometimes when they don’t get a job to be like, “It wasn’t the right job for you.” Like, “This is a win. You figured out it wasn’t the right thing. And even if they said no, you were saying no, too. So that’s okay.” But one of the things I noticed is that you do have a lot of questions that you recommend or like areas worth probing. Now I have this problem when I interview because I again could pummel my interviewee with questions.

0:24:26.0 TW: Okay when you’re interviewing someone else. Okay. Not when you’re being interviewed.

0:24:26.0 MK: No, when I’m being interviewed, I normally have a lot of questions I want to ask the company because I consider it like I’m interviewing them, too. But it’s also a fine balance of not just like pummelling them with questions and actually having a conversation. Like is there, are there, like do you need to prioritize your questions to make sure you just ask the most important ones? Like how do you get through actually sussing out if that company is right for you without totally overwhelming them with 50,000 questions?

0:24:57.4 KB: Yeah. Well, I mean, I do think there’s probably some prioritization that you want. There are probably questions that can be hard filters that you should ask in your very first conversation with the hiring manager. Like for some people it’s comp requirements. For some people it’s seniority. Like you should think about what’s the thing that is like if they say yes or no on this, I have no interest. For me, a lot of that was just kind of like where does data sit in the company? Like is it actually considered important? And a lot of that was like oriented around like why do you want a data leader in the first place?

0:25:31.2 KB: Like what are the data people doing right now? Like how is it gonna be different? How will I play a role in making that different? Those were the types of things that I was pretty aggressive about from the start. I guess like maybe a useful framework is first you might wanna filter on is this a good job like that you would be interested in actually interviewing for? Like is this job aligned with what you’re interested in, what you wanna learn, what you wanna do with your career? And then as you get further along, you might wanna start thinking harsher about the hiring manager, about the stakeholders as you have a chance to meet them. But usually like one of the first things that you get to do is talk to someone on the recruiting team and sticking with hard filter questions that they can answer is probably helpful for filtering a lot of people early on.

0:26:19.2 MK: I feel like I’ve been doing this all wrong. My motto the last few times I’ve gone for a job has been to be like, “Am I going to be around smart people and do I like the company?” Like, “Do they actually appear from an external perspective to live their values?” Like I’ve noticed that like values alignment is really, really important to me. And then I’m always like, “And the job doesn’t matter so much. I’ll figure it out when I get there,” and I’ll like make my way to the role that I’m most interested in. And maybe that’s an awful strategy because now I’m like, “Oh, this sounds like a much better idea.”

0:26:55.1 KB: Yeah. I mean, I do feel like data leaders often have to figure out what their job actually is once they get there or play a big role in shaping it. And the more specific it can be from the start, the easier it will be to actually shape it into something you want. Like you wanna start somewhere close to where you wanna end up if possible.

0:27:12.4 MK: That’s true.

0:27:13.2 MH: Well, that’s like one of the… I mean, I love the question where like, and it’s one of those that just seems obvious in hindsight that in the article is like asking like, “Why does this data leader position exist?” And it’s like, it’s not like there’s a right or wrong answer. Like if it’s backfilling someone, well, then you may be moving in and they may be expecting you to do things the same way that person did and that person had just quit. If it doesn’t exist, they may have completely unrealistic expectations of what you can do with your magic, magic wand. I guess like how… Actually like in your current role, if you can share, like did that position exist before? Like what was the answer to that question with when you wound up at Gloss Genius?

0:28:13.3 KB: There was not an equivalent role to what I was doing. The answer to the question of why a data leader role was being created was that the company’s planning on growing a lot next year and measurement is a big part of making sure that you’re doing that in a financially responsible way. They need someone who knows how to run a data team to help build that function. There were people doing data at the company before they were doing an excellent job, which also makes my job a lot easier. Like it’s not turning around a bad scenario or anything. So that’s something that was appealing to me about the company for sure.

0:28:28.8 TW: So what about… No now if it’s shareable or if you remember if you asked it or what the answer was, but when the, the should analyst code interview, like why was that data leader position open?

0:28:43.5 KB: They had hired analysts in a bunch of random places across the organization and they weren’t talking to each other, they weren’t collaborating and they needed someone.

0:28:50.9 MK: Some of them kept writing code and they needed somebody to come smack them down?

0:28:51.0 KB: Yeah, basically. I mean, it’s like, how are they going to collaborate? Like, “Oh, maybe code is a good layer of information. Maybe this would help solve your problem.” But it was like a lot more of like, “We don’t know what we’re doing and we need someone to fix it up, but we’re still very opinionated about what we want this to look like.”

0:29:11.0 MH: Yeah. Moe going back to something you said about sort of like the criteria you’ve been selecting and thinking about, I think your criteria gets additive. And it’s sort of like, some of the stuff you said and some of the stuff Katie, you said, I think if you connect those two, you start getting the… Because as you go into leadership roles, you now have to own a strategy and a set of outcomes and you need to know, this is my path. One thing I found in the interview process, I don’t know Katie, if you did this at all, was walking through like a 90 day plan of like, “When I come in, this is what I think I’m gonna do over the first 90 days,” and getting into a discussion of like, “These are the decisions I think I’m gonna make. Do you see any challenges with me having the authority to make those decisions, these activities, does that align with… And sometimes that can scare up some of those issues of like, “Well, we want to bring you into this title, but we don’t wanna give you any authority to do the things you need to do to do this role.”

0:30:09.3 MH: So like, sometimes I feel like that conversation is helped by stuff like that. I don’t know if you found anything similar or you might not have needed to do something that formal. I’m not sure.

0:30:18.9 KB: Yeah, I didn’t do anything that formal, but something that was useful is getting a sense of like, what resourcing is available to this team? Who are we gonna be able to hire? Like, are you open to hiring me a different function that maybe doesn’t exist right now? What tools are you willing to buy to support this team? Like financial support for the team is definitely a really important thing to poke at, even if you’re not getting a specific 90-day plan. Like if you’re coming in expecting to have to hire a bunch of people, like that’s gonna eat up a ton of your first couple of months at the company, as well as just kind of diagnosing what’s going on. Like, I do think there’s some extent to which, depending on what level of leadership you’re coming in on, you’re expected to kind of drive your own first 90-day plan. Like there should be a purpose for you to exist in the organization, but like you also should feel comfortable imposing your perspective on the organization of what a data team should look like because that is your job. You know it better than anyone else.

0:31:11.1 MK: And one of the comments you made in your article, which I just was revelling in, was about the fact that like if the person that you’re reporting to is unfamiliar with the data space, and I love the word you used, energy. Like, do you have the energy to convince this person about the resources you’re gonna need, get their buy in, and also you’re going to have to educate them on what a data team should be and do. And you know, do you have the energy for that? And that was something that I thought was really important about considering a future role is, and I talk about this with my team leads as well, of like, “You need to think about the stakeholders you’re working with and do you have the energy to get to where you want to get to? Because like. The next six months could be really hard because, I don’t know,” like there are various reasons that a relationship with a stakeholder might be harder. And it’s the same thing, right? Like are you gonna have to go into bat or do you have someone I guess that already understands at least a bit about the data team? How do you flesh that out a bit? Or how have you in the interview process?

0:32:10.9 KB: Yeah. I mean, getting into the types of things that they would ask the data team for, like what types of deliverables do they talk about is a pretty interesting signal. Like I’m not like talking about anyone specific and saying this, but like something I see a lot when people talk about what they want from a data team, they’ll give some very vague question of like, “Oh, how are the metrics? Or like, “How, like, can you like add this filter to this dashboard?” Like if they say that type of stuff and that’s the only thing they talk about, that’s not really a good signal that they have a lot of understanding of how to work with you. Like if they are able to speak to successful collaborations with data scientists or analysts and like actually digging into the specifics of that story, that can be a really interesting signal. And that’s one that I used a lot was just telling me about a time you successfully collaborated or supported or advocated for someone in this function. The types of things that they talk about are really interesting.

0:33:10.1 KB: Like they might have a positive story, but it might be something that is just kind of like, “Okay,” like that’s not that big of a deal. But like talking about like advocating for head count, for example, for this function or talking about like a really good strategic analysis that you partnered with someone on and like how that changed an outcome for the business. Like those types of stories are a lot more interesting because they tell you about value exchange. Like there’s an actual thought of like what they want to receive from the data analyst or data scientist or whatever title. And it like, it’s something that speaks to respect for the function because like they are giving in order to get something.

0:33:54.0 TW: So just, is that typically discussions where they’ve got the candidate interviewing with like stakeholders who will be their stake? Do you get that more from that from like, “Oh, we want you to talk to, these are your stakeholders?” More so than you can get from like the hiring manager or is it both?

0:34:07.4 KB: Yeah, it depends. I think it works for both. I used it a lot in conversations with stakeholders, particularly stakeholders who would be kind of like upstream or downstream of what I was doing. It’s like product and engineering teams are very top of mind for the types of roles I’ve had in the past that were focused on product analytics primarily. Like if an engineer does not understand what role they play in creating data that you will use, that’s bad. If a product manager does not understand like how to actually like ask good questions for analysis or build upon or make requests of a data team, that’s bad. A very common thing you see for product managers in particular is to dictate something extremely specific that they want. And like this happens with all functions, but it’s salient to me with product that they’ll ask for specific diagrams or some kind of like very specific field be added to a report that like… It’s just so prescriptive that it’s telling someone how to do their job. And I wouldn’t do that to a product manager. I don’t want a product manager to do it to me.

0:35:13.7 MH: I feel like there are a lot of analysts who haven’t, cause I feel like you’ve just nailed like one of the challenges. Like analysts will say, if somebody gives them a requirement like that, they’re like, “Oh, good. I can go do that,” as opposed to the analyst taking a pause and saying, “Can I understand what you’re really trying to do? That may be the right answer but I need the additional context.” But I feel like it’s kind of a problem in the industry is like if they worked with an analyst who needed that level of specificity, then they’ve been conditioned to think that’s the way to work with an analyst. And it’s like, it’s just this vicious cycle that repeats that. And then some other data lead has to come in and be like, “Can we not just become the order takers?” Like, “Could we step back and are you going to be receptive to giving me more context so we can have a discussion?” So I love that framing of like where, I don’t know, do you have the stakeholders who say, “Yeah, I need the data team to just, they give me data but they don’t give me actionable insights,” which is just like sort of spewing.

0:36:16.3 MK: Or the other end of the spectrum which is like, “I want you to build cool AI shit.”

0:36:26.5 KB: Yeah, I mean both of them are buzzwords definitely.

0:36:30.1 TW: We’ve talked about the… So the stakeholders, there’s the hiring manager. I just have no idea how often when you’re going into some sort of data lead role and Moe, you and Katie both may know, like how often, how much of a norm and how useful is it to actually talk to people who are going to report into that role?

0:36:52.0 MK: Oh, I love that. Like, I mean, I think…

0:36:54.4 MH: Wait a minute, but you love this as a candidate or you love this as a…

0:37:00.1 MK: Both. I actually… Like I’ve been pretty open with the fact that I think in my team, I think it still holds true. I have yet to interview a single person who has joined my team and my husband thinks I’m bark raving mad. We do like peer interviews and so…

0:37:16.4 MH: Just in general or?

0:37:16.5 MK: Oh, just in general. Yeah, probably.

0:37:18.6 MH: Specifically or in this… Okay.

0:37:19.4 MK: No, but he’s like, “If they’re joining my team, I wanna interview them.” And I’m like, “Yeah, but I’m really biased. I tend to choose people similar to me, who are stronger on communication, less strong on technical skills and like that isn’t always what we need, right?” And so I get the team to interview potential candidates. It’s actually like how Canvas is set up is like you have peer interviews. And over the course, you’ll meet 3-5 different people who do the same job. And I’m a really big believer in that because I think that it… Yeah, it stops me from just choosing a bunch of people that are similar to me. And I think the same is true of a manager. Like I would love to get to interview my manager and I would love if people in my team got to interview me. So, but I have really like different views on this stuff.

0:38:11.0 KB: Yeah. I’ll say, I think it’s good to meet people on the team if you can. First of all, your success is gonna be really dependent on the people on your team as a leader. So you should get to know them and get to know what they’re about, if you have the ability to. I also think it’s really valuable as like a second perspective because like if you don’t meet anyone that you would be managing, like you won’t actually hear what it’s like to work with those stakeholders as a data person probably. And they often have things to say about what they want. And it’s worth thinking about whether you can be that for them too.

0:38:41.3 MK: Totally. So what are your thoughts about like really clicking with the hiring manager or like… So the last time I went for a job, I actually, I had a few roles going at the time and I actually had a matrix that I have shared with many people over the years now of scoring. It’s very like nerdy, but it is also totally biased of like, what were the different things that matter to me? And then I would score like the company, about how they related to that particular criteria. So it might’ve been like ease of getting to work or likeability of boss and all that sort of stuff. And the one I really struggled with, I hope my boss never listens to this, was likeability of boss.

0:39:20.2 S1: Like I actually really didn’t click with him that much in the interview process. And the funny thing is now he’s been my coach for three years and I freaking adore the man. He’s one of the best coaches I’ve ever had. And we’ve developed like the most incredible relationship. And I trust him immensely. But in that first interview, because I only had one interview with him, like we didn’t really click, but I feel like it’s something that can really bias you in the interview process. Like how did you navigate that?

0:39:48.5 KB: Yeah. Well, one thing I’ll say is I’m not sure what likeability means. I don’t necessarily want a friend as my boss. Like I want someone who I can communicate with, who I share some sort of like value or form of success. Like for a data leader, you probably wanna work with someone who cares about numbers or cares about data technology or something. Like you need to have some kind of shared language of communication. Like, or if you report into product, for example, like you’d probably wanna have like some sort of strong connection on that lens of shared values. Like there needs to be some vector along which you work and you communicate and you get things done.

0:40:28.7 KB: Beyond that, I don’t think you necessarily need to like them. Like you can have bosses who are huge pains in the butt, but they push you and make you better. And like that can be really great, too. In terms of like what I was thinking about as I met potential hiring managers, if I disagreed with them, how did they handle it? That was something like the person who said analysts shouldn’t code, for example. I knew that one wasn’t gonna work pretty quickly, but like, I guess like more broadly like what kind of relationship do you think you’re gonna have in situations of disagreement is really interesting for someone that you report to because you probably will. And if it can be professional and cordial and constructive, and if they can change your mind, like that’s also interesting, too. How you actually do that can be a little bit difficult in an interview process, but getting a sense of like where limits of where they might want the role to go is an interesting place to prod, for example. Like if you suggest that like some function should be a part of the role of your team as a data leader that’s not there right now, like how receptive are they to that? Like will they work with you? Will they have a real conversation? Do they want to be a partner in brainstorming how it might actually feasibly work? That kind of thing is pretty interesting to poke at.

0:41:47.9 TW: I don’t know that I’ve ever really tried to introspect about it, but it’s kind of like, “Oh, I really connected with say the hiring manager and asking was I connecting on, you know substance related to the role, how to work with stakeholders, code or analysts should code or not code, or was I connecting with them because they’re like me from a, which like… I mean, that’s the problem. I suspect that it’s the middle-aged white guys who are least likely to be asking themselves like, “That was fun. That was great. You know, we both like the Browns or whatever. And that seems like a good,” like, I mean, I don’t, I was referring to Michael there.

0:42:33.5 MH: Thank you.

0:42:34.7 TW: But I think like…

0:42:36.3 KB: Liking the Browns is an important form of bonding.

0:42:39.3 MH: Exactly correct. Thank you very much.

0:42:40.4 MK: I don’t know who the Browns are.

0:42:42.0 TW: But it seems like… The Cleveland Browns, the…

0:42:47.8 MK: Oh, that’s okay. Yeah. That’s familiar. I’m with you.

0:42:50.9 TW: The All Blacks? I don’t know. Sorry. Can I throw something into your hemisphere?

0:42:55.3 MK: Yeah, All Blacks.

0:42:55.2 MH: Yeah, there you go.

0:42:55.3 TW: Okay. So I mean, but I feel like that seems like a really good, like introspecting, like. “That was a great conversation.” Well, why was it great? Cause it does seem like it’d be easy to connect on things that aren’t, as you just kind of articulated, like the things that you really are gonna matter for doing the role.

0:43:20.0 KB: Yeah. I mean, I think that mindset also really helps with people you’re interviewing to join your team too, is just, do you connect over the substance of the role or is it just kind of adjacent things? Like something that can be kind of distracting. I think some… Well, I mean like take this with a grain of salt, but sometimes I think depending on what your company is, people can like be very focused on like the mission sounding good. And then they like don’t actually care about like the meat and potatoes of the role. And that’s like an interesting thing to think about too, both in hiring managers and people you would hire is like great that they’re bought into the mission. They should be, that’s kind of the table stakes, but like what specifically will they do as a data practitioner in that role? Or I guess your boss, what will they do to support the data practitioners at the company?

0:44:06.4 MH: So I love how systematically you have kind of worked through this process. So one question I wanna ask you is what skills did you find yourself kind of developing or working on as you went through this process? Like, was there anything that stood out to you, I guess?

0:44:23.2 KB: Yeah, that’s a good question. I think maybe something that I became increasingly comfortable with over the course of many interviews was just being very honest about what I was looking for and not feeling bad about telling people like, “I don’t think this is the role for me.” Most recruiters took that very well. I did…

0:44:40.6 S1: Most. Oh. Let’s talk about the other one.

0:44:47.2 KB: Well, I guess recruiters always took it. Well, I did have a conversation with a hiring manager where once they started describing the role, I told them it seemed more junior than what I was looking for. And then it was like a switch flipped. They were suddenly like, “Well, like, I don’t know if you have the right background either. So it’s fine.”

0:45:01.6 MK: Oh, wow.

0:45:03.2 KB: It was like, “Okay.”

0:45:05.2 TW: Everybody needs the psychological protection here. Sure, sure. Whatever you need.

0:45:08.8 MK: Also true colors coming out that quickly in the interview. You’re like, “And now I’m really sure I don’t want to work here.”

0:45:11.9 MH: Yeah. Thanks for the confirmation.

0:45:13.0 KB: Yeah, pretty much. It’s a blessing in disguise.

0:45:16.6 MH: Any other ones stand out or that one just being honest and transparent with people? I think it’s a really good one.

0:45:24.3 KB: I mean, I guess I also just started building a better mental model of what was valuable to me or what I wanted to do and what I was interested in. As I had more conversations with… I talked to so many different types of companies and types of people. Some of the roles were basically a developer advocacy role that was called head of data. Some of the roles were like kind of middle manager in an existing larger data organization. It really helped me get a sense of what were the important dimensions of my next role to me and what would be energizing for me and just actually thinking about, “Is this something I think I could do for a couple of years,” or like, “What would this add to my skills as a data leader or data practitioner or just what could I take from this job that I think would help me be better?” I got a lot better at thinking about those things, if that makes sense.

0:46:20.6 MH: So replaying back to each of your job changes over the years, like you’d said earlier, what you want is gonna evolve. What you’re able to do is going to evolve. The kind of having a structured or disciplined way to think about that, like with this latest time, were you like, “Wow, I am really gonna nail this because now I really know what to look for and how to look for it?” Was that kind of progressing? Every time you were going through the process, were you kind of conscious that you were getting better and more formalized about it?

0:47:03.0 KB: Yeah. I mean, like maybe a helpful example of how this has changed for me. When I was job hunting before my most recent job hunt, the job hunt that brought me to Twitter, the thing that I was most interested in at that time was working somewhere where there was a lot of other data leaders. Because I wanted to have a larger peer group to learn from. This time around, I was fairly certain I would be okay with not having that. Like I thought that might be nice ’cause having a good peer group is always important, but I also have a lot more of that support structure independent of my job now. So it became less important for me. I just know more people in industry who are in similar positions. I have more relationships from previous jobs. It made it easier for that not to be an important dimension for me. I wasn’t fully sure what parts of it were the absolute must haves for me, but just talking to a couple of different hiring managers started, like I would talk to them, kind of sit with my emotional reaction to what I learned and that helped me refine my idea.

0:48:02.5 KB: Like one thing that I did think about at one point was would I want to go somewhere and be an individual contributor first and then start hiring a bunch of people. And over time, I started to realize, no, I really do want to manage people still. Coaching is one of my favorite parts of leadership and growing and building teams. So that was something that I just over time realized I didn’t want to compromise on, for example.

0:48:26.5 MK: Yeah, that’s a really interesting one. I feel like that one comes up a lot, especially with these like head of data type roles, where it’s like often they wanna hire that person first and then have them build the team. But then in some cases they’ve done the opposite where they build the team and now been like, “Oh shit! We kind of need someone to steer the ship, essentially.” In your experience, like do you think there are like pros and cons to the different styles or is it more like either way it’s like an evolution of their data team and what they’re doing?

0:49:00.5 KB: Yeah. Well, I mean, I think it’s appealing to different styles of leadership perhaps. Like there are some leaders who like they are way more operators. Like they really like want to run a tight ship and like have every process in place and make sure things are like trains are running on time. There are other people who wanna lead more from like a craftsmanship perspective. Like they really want to lead by example and do some of the work themselves and know a lot of the details. And like neither is a better style necessarily. It may be better suited for different situations and for different people. So like if you’re thinking about a particular data role and you’re someone who really loves the craftsmanship aspects of the role and that’s how you want to lead, like looking for one of those positions, where you come in and you set everything up yourself, like that might be a really good fit for what you’re looking for at that time.

0:49:47.0 MK: Oh, this is so good. I can’t wait to call one of my friends…

0:49:53.3 MH: Yeah, it’s very insightful.

0:49:53.4 MK: After this who’s been job hunting and just be like, here’s all the stuff I learned.

0:50:02.4 TW: One of your friends, like Moe is this? Is there something you’re not telling us about your…

0:50:06.8 MK: Yeah. One of my friends.

0:50:07.5 TW: I have a friend.

0:50:08.7 MK: I have contacts.

0:50:12.5 S1: All right. We do have to start to wrap up. Unfortunately, this is awesome. Katie, thank you so much for sharing some of these insights that you’ve learned. One of the ones that stood out to me was how you’re asking stakeholders to tell about a time they successfully collaborated with data and analytics folks. And what’s amazing is I’ve always advised analysts to tell the stories about how they’ve impacted or narratives around how they’ve created business impact through their work, but I’ve never connected the dot to go find out if the other side is thinking the same way. And that turned a real light switch for me. So that was awesome. Thank you.

0:50:49.2 TW: Alright. One of the things we love to do is go around the horn and share a last call, something that might be of interest to our audience. Katie, you’re our guest. Do you have a last call you’d like to share?

0:50:58.9 KB: Sure. I kind of have a passive interest in the history of statistics and a bunch of like other professions that maybe ended up becoming data scientists over time, like marketing science, for example. Like such a weird term, but like what they do is recognizable as data science today. But a fun resource in just kind of starting to think about like history of statistics and where all these different like applications of it have come from is simply the founders of statistics Wikipedia page. It’s really cool to like just go and click through and like see the timeline of like when certain things were invented. I feel like I’ve been surprised by most of them.

0:51:36.0 MH: Surprised that you hadn’t heard of them or surprised and that you’re like, “Oh, I hadn’t really thought about that. And when they were operating?”

0:51:41.5 KB: Kind of like the timeline of stuff. Like there’s some stuff that it’s like I just never really thought that there was a first person to come up with that concept, like exploratory data analysis. Like someone came up with the concept for that because it used to be the case that when you had data, you collected it yourself. So doing an exploration didn’t make as much sense, but like someone had to come up with that concept at some point kindling right now.

0:52:04.1 MK: Oh, this is fascinating right now. I love this.

0:52:07.1 MH: Yeah, I know. We’re gonna be like, “Let’s just pause the show for a few minutes.” So yeah, so that’s a good one. All right. Tim, what about you? What’s your last call?

0:52:20.2 MK: I’ve got a really short one. Just a post that Bill Franks from the IIA International Institute of Analytics wrote called the link between analytics maturity and social media influencers. And the basic, it just kind of eloquently talks about how it’s very easy to get like pulled, drawn into the allure of all these cool things that people are doing and feeling like your data team is behind. And maybe it ties to this episode that there sometimes the hiring managers have been reading too many of the articles of all these really cool things. And they’re like, “We wanna become the Amazon of Big Pharma,” or something like that. So it’s a nice little analogy. It’s like starts with a picture of Gary Vaynerchuk who like stresses me out as an individual. So it’s a cool little article of a nice little analogy a little good way to reset. Like don’t just read the trades and think that you’re way, way, way behind. So it’s a fun little read.

0:53:20.5 MH: All right, Beau, what about you?

0:53:21.9 MK: Well, I know I’m gonna get one eye roll and one like, “Oh,” but I did recently listen to a two-part series on Brené Brown’s podcast Dare to Lead. They are apparently going to rotate being guests on each other’s podcast, but Simon Sinek and Adam Grant were guests. Although apparently you mean to say Adam Grant. I’m not… I say Adam Grant, which sounds more English than American, but anyway. And it was just, it was…

0:53:49.6 TW: Do they talk about data at all?

0:53:52.3 MK: Yeah, data comes up a lot. Sometimes data, too. But it was just a really good listen because it wasn’t heavily scripted or anything. It was a lot of. Brené Brown kind of being like, “So this is what I think is going on in workplaces at the moment. What are you guys seeing, like hearing?” So there was obviously discussions about like quite quitting, about working with brilliant assholes or jerks is the more polite term apparently. And I just got a lot out of it. And I know that I am like such a sucker for all three of their work. I love it. But it just, it actually left me…

0:54:27.3 MH: Me, too.

0:54:27.9 MK: Like I listened to it on my vacation and kind of like gave me space to think about what they’d said as well, which is really nice. So don’t listen to it just like on a busy commute, like make sure you have time to kind of process and reflect on what you want to take away from it.

0:54:43.0 TW: Michael, you did not squeal. I think you were… I think we’re pretty clear that I was not supposed to be the one who squeal delightedly.

0:54:52.3 MH: It might be that my last call Moe may even exceed yours in terms of patchy feelingness.

0:54:57.4 TW: Oh, good Lord.

0:54:57.9 MH: So it’s a little bit of a departure, but recently I watched a documentary called the Cave of Adelam, which is about a guy in Detroit who started, I think he started a nonprofit to help young people coming like young men, especially growing up in Detroit. And he was at first starting it. And I don’t want to tell his whole story, but basically he first started it as an idea of like, “Well, we’ve got to train people, young people to be successful. So they need more discipline.” But as he did it, what he realized was they didn’t need more discipline. They needed more love. And this documentary is about that story, which was very deeply personal and touching to me. So I’m sharing it with all of you. So there you go. Alright.

0:55:39.4 TW: Michael was actually given presentations at analytics conferences purely about the subject topic of love, right? Yep.

0:55:48.1 MH: Occasionally.

0:55:50.1 TW: It’s happened.

0:55:50.2 MH: Sometimes.

0:55:52.6 MK: It wasn’t love.

0:55:52.8 MH: I did one about love specifically once.

0:55:55.1 MK: Really?

0:55:55.1 MH: Yeah. Yeah. Like years ago in San Francisco, for five minutes.

0:55:57.7 MK: Oh, I love this.

0:56:01.3 TW: Yeah. Yeah. San Francisco. Come on.

0:56:03.5 MH: What did I say?

0:56:05.6 KB: Very, very lovey city.

0:56:07.1 MH: No. Yeah, exactly. You got to talk about love. Anyways.

0:56:09.3 KB: They had a whole summer about.

0:56:11.5 TW: Queue the…


0:56:16.5 MH: Okay. Let’s talk about our awesome producer, Josh Crowhurst.

0:56:20.4 TW: We love him.

0:56:20.5 MH: Who we heartily thank, even though he was laughing at me on the one chip challenge. And we do appreciate everything you do, Josh. Thank you very much. And if you’ve been listening and you’re in the career transition or you’re interviewing, we’d love to hear from you. There’s a number of places you can reach out to us. Twitter, also on the measure Slack group. Now you know that at least I’m on the locally optimistic Slack group as well as our LinkedIn page. Katie, are you active on the social medias or where do people find what you’re writing or thinking about?

0:56:56.0 KB: Twitter is always good. I’m @imightbemary. I’m sometimes on LinkedIn. Although I don’t really like LinkedIn, but I’m there.

0:57:03.8 MH: I’m liking it less and less all the time these days. Signal to noise is getting out of hand on that platform.

0:57:13.5 KB: Yeah. I suppose also the Substack that sponsored or like spurred this conversation on is wrong but useful at substack.com.

0:57:18.8 MH: Oh, yes. I love that title by the way. Wrong but useful. I like it. Okay. So thanks for that. And thanks for being on the show. This has been awesome. Thanks so much.

0:57:25.6 TW: This was great.

0:57:26.6 KB: Yeah, this was fun.

0:57:31.5 TW: I like the one where it’s like, I’m not sure where it’s likability means.

0:57:34.7 MK: I know. And then the funny thing is I was like, “Oh, I think I’ve revised that and likability is a shitty term.”

0:57:43.5 TW: I was like, “Wow, you kicked ass.” I was like, “That’s awesome.” ‘Cause I worry about likability all the time.

0:57:52.8 KB: Yeah. I mean, it’s also like a big thing for women leaders that I feel somewhat triggered by when it comes to conversation.

0:57:55.5 MK: Yeah, that’s so fair. That is so fair.

0:57:57.8 MH: I’m putting my fist up in the air and being like, “Yes,” because we all need to fight it wherever we’re feeling it. So that’s awesome. Alright. So we’ll get this show wrapped up ’cause now I’m just a huge fan as usual. But I know that no matter where you are in your job search, I know I speak for both my co-hosts, Tim and Moe, when I say keep analyzing.

0:58:22.2 Announcer: Thanks for listening. Let’s keep the conversation going with your comments, suggestions and questions on Twitter at at Analytics Hour on the web at analyticshour.io our LinkedIn group and the measure chat Slack group. Music for the podcast by Josh Crowhurst. So smart guys want to fit in so they made up a term called analytics. Analytics don’t work.

[background conversation]

0:58:47.1 Kamala Harris: I love Venn diagrams. It’s just something about those three circles and the analysis about where there is the intersection, right?

0:59:09.4 MK: Everyone really needs to keep me in check today ’cause I feel like I could go on like a massive personal tangent and like talk about my own lived experience about this topic. And it would not be remotely professional. So like we really need to like, be careful of what I’m saying and stop me if I’m saying inappropriate shit.

0:59:17.2 KB: Do we need like a safe word?

0:59:18.9 TW: Yeah. Broccoli, broccoli, broccoli.

0:59:27.8 KB: Broccoli is a safe word, right? Like surely.

0:59:32.5 MH: We pause just for a second because Moe, like I had missed your note. Like when does your heart stop?

0:59:36.2 MK: Oh no, I’m good. I need to leave in 45 minutes. So I’m good.

0:59:38.5 MH: Oh, okay. Yeah. When you put a quarter till I know we’re in different times zones. What do you think we were gonna do?

0:59:45.1 MK: No, I would have been like doing these ones if it was problematic.

0:59:51.0 KB: Okay. Shouting broccoli into the wind.

0:59:54.9 MH: Broccoli. [laughter]

0:59:54.9 TW: Throwing Broccoli into the wind.


1:00:03.2 TW: Rock flag and SQL is code.

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