#252: The Ever-Shifting Operating Environment of the Data Professional

Summary

The Analytics Power Hour - Episode #252: The Ever-Shifting Operating Environment of the Data Professional

Release Date: August 20, 2024

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Introduction and Episode Overview

In Episode #252 of The Analytics Power Hour, hosts Michael Helbling, Tim Wilson, and guest Katie Bauer delve into the evolving landscape of data professionals within organizations. The episode explores the nuanced roles that data teams play beyond their technical expertise, emphasizing the importance of strategic navigation to foster positive business impacts. Katie Bauer, Head of Data at Gloss Genius and former data science leader at Twitter and Reddit, brings valuable insights into how data roles are perceived, the challenges posed by emerging AI technologies, and strategies for data professionals to enhance their influence and partnership within their organizations.

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The Role of Data Professionals in Organizations

Michael Helbling sets the stage by highlighting a common struggle among data professionals: understanding how to fit into the broader organizational framework and effectively collaborate with various teams to drive meaningful outcomes. He states:

"Beyond the various hard skills that make up a great analyst or analytics engineer, there's sort of a hidden navigation that has to occur to achieve the outcomes that we all want to create, you know, positive impact on the business." (03:29)

Katie Bauer responds by affirming that data is indeed a distinct job category, composed of a blend of skills tailored to specific organizational needs. She challenges the notion that data roles are solely about technical tasks like writing SQL queries, emphasizing the multifaceted nature of data professionals' responsibilities.

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The Impact of AI on Data Jobs

A significant portion of the discussion centers on the rising influence of AI in the data field. Katie addresses concerns about AI-driven tools potentially automating aspects of data work:

"I do a lot more than [writing SQL]. And like that, in conjunction with all of the kind of doom and gloom posts that have been coming out recently about whether data needs to be a job, I just eventually got to a point where I had thought of what I wanted to say on this, which is just I do think it's a job." (05:00)

She acknowledges the apprehensions within the data community but reinforces the argument that data roles encompass much more than tasks that AI can easily replicate. Data professionals engage in high-level analytical thinking, strategic decision-making, and fostering cross-functional collaborations that are beyond the scope of current AI capabilities.

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Data Literacy and Its Misconceptions

Julie Hoyer brings up a crucial point about data literacy, noting that while many believe everyone should leverage data in their roles, the reality often requires specialized skills and support from data teams. Katie expands on this by discussing the misconception that data literacy equates to raw intelligence:

"It's being problem forward rather than like giving them a menu to pick from." (37:00)

She introduces the concept of "wearing the sloth," a metaphorical framework distinguishing different types of challenges stakeholders face when interacting with data. Katie emphasizes that data literacy should focus on clarifying the purpose and context of data use rather than just teaching technical skills.

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Types of "Sloth" in Stakeholder Interactions

Katie Bauer categorizes stakeholders into three distinct "sloth" types, each presenting unique challenges for data professionals:

1. Uncomfortable Sloth: Individuals who know they need to use data but feel unequipped or intimidated by it. Katie advises fostering a supportive environment where data professionals guide these stakeholders patiently.

2. Distrustful Sloth: Stakeholders who may misuse data to undermine others or pursue ulterior motives. Katie recommends understanding the underlying intentions and ensuring that data usage aligns with business objectives to prevent manipulation.

3. Dreaming Sloth: Enthusiastic but unrealistic stakeholders enamored with data-driven initiatives without practical feasibility. Katie cautions data professionals to remain skeptical of overly ambitious projects and prioritize initiatives that align with the company's strategic goals.

"You need someone to be over there, like steering and guiding or making decisions about what needs to happen." (09:22)

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Strategies for Data Teams to Enhance Partnership

The conversation shifts to actionable strategies for data teams to become more effective partners within their organizations. Katie emphasizes the importance of:

• Proactive Engagement: Instead of waiting for data requests, data professionals should actively participate in team meetings, understand business goals, and identify opportunities where data can drive outcomes.

• Clear Communication: Articulating data insights and recommendations in the language of the business stakeholders to ensure clarity and relevance.

• Setting Boundaries: Prioritizing high-impact projects and gently declining low-value requests to maintain focus on strategic objectives.

"It's our job to look at what's happening and say, is this excellent? Like, is this actually good enough?" (17:51)

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Decision-Making Roles for Data Professionals

Julie Hoyer poses a thought experiment about elevating data professionals to decision-making roles within organizations. Katie discusses the feasibility and implications of such transitions:

"There are areas where it's more feasible than others. And they're probably something that's very quantitative, like maybe it's running a lift testing program in a marketing organization or driving pricing strategy or something." (19:10)

She acknowledges that while some data professionals may naturally transition into leadership or strategic roles, it requires embracing accountability and a willingness to influence business decisions proactively.

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Building Context and Aligning with Business Goals

Katie underscores the necessity for data teams to immerse themselves in the broader business context. This involves:

• Understanding Company Strategy: Aligning data initiatives with the organization's strategic goals to ensure relevance and impact.

• Building Relationships: Establishing strong, ongoing relationships with cross-functional stakeholders to foster collaboration and mutual understanding.

• Outcome-Focused Communication: Framing data projects around the tangible benefits and value they bring to different teams and the company as a whole.

"We always talk about an outcome. It's not like improve data quality, it's reduce overhead of queuing the eventing in the product or whatever so that engineers have more time to go and build XYZ thing." (37:31)

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Final Thoughts and Last Call Recommendations

As the episode concludes, the hosts and Katie share their final recommendations and resources for listeners:

• Katie Bauer recommends the book "The Strategy and Tactics of Pricing", highlighting its relevance for understanding quantitative business strategies.

• Michael Helbling suggests revisiting "The Effective Executive" by Peter Drucker to reinforce strategic thinking and business alignment.

• Julie Hoyer shares an intriguing BBC article on AI's role in decoding whale communication, illustrating the innovative applications of data science.

The episode wraps up with an encouragement for data professionals to continuously strive for excellence, seek meaningful partnerships within their organizations, and remain adaptable in the face of evolving data landscapes.

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Key Takeaways

• Data Roles Are Multifaceted: Beyond technical skills, data professionals must navigate organizational dynamics to drive impactful business decisions.

• AI Complements, Not Replaces: While AI tools can automate certain tasks, the strategic and relational aspects of data roles remain essential.

• Enhancing Data Literacy: Focus on contextual understanding and purposeful data usage rather than just technical proficiency.

• Proactive Partnership: Data teams should actively engage with stakeholders, align with business goals, and communicate effectively to maximize their value.

• Strategic Decision-Making: Empowering data professionals to take on decision-making roles can enhance accountability and business outcomes.

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Notable Quotes:

• "This frees up our time to get to the meatier things." — Katie Bauer (08:24)

• "The data professionals need to be insisting to be a part of those conversations more." — Josh Crowhurst (10:22)

• "We can tell people when we don't believe answers. We can be a thorn in someone's side until they actually figure out something that needs to be figured out." — Katie Bauer (09:22)

• "We always talk about an outcome. It's not like improve data quality, it's reduce overhead of queuing the eventing in the product or whatever so that engineers have more time to go and build XYZ thing." — Katie Bauer (37:31)

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For more insights and discussions, subscribe to The Analytics Power Hour and follow Katie Bauer's Substack for in-depth articles on data leadership and analytics strategies.

Transcript

[Michael Helbling]

Before we start the show, we have a special announcement. This fall, the Analytics Power Hour crew is headed to Measure Camp Chicago.

[Val Kroll]

That's right. Even your co host from all the way in Australia will be there on Saturday, September 7th to join in all the unconference Measure Camp.

[Katie Bauer]

Fun.

[Julie Hoyer]

I'm so excited that we're all going to be together. Well, except we'll be missing Josh, but we'll have him there in spirit. But I'm curious, I've never been to a Measure Camp.

[Katie Bauer]

What's it like?

[Tim Wilson]

What's it like? Okay, well, I've been to one of them in Europe and I've been to, I think, all of the ones that have been in person in the US and to me, kind of the most iconic feature is that the schedule is created on the day of the event and everyone who attends is encouraged to actually lead a session based on whatever they're finding most interesting or most useful or even maybe what's kind of vexing them the most of late. So it's really all about an exchange of ideas and having some really in depth and rich discussions with your peers.

[Val Kroll]

I've also been to quite a few and I've also helped with planning the one we run in Sydney. And the truth is, it's just phenomenal. It's better than Christmas Day, honestly. And one of my favorite parts of Measure Camp is that they're held on a Saturday so it doesn't interfere with your work and the tickets are always free.

[Michael Helbling]

Yeah. And I loved my experience at Measure Camp Austin earlier this year. I mean, it was so accessible to everybody and it was so fun. Okay, so what are we going to be doing there?

[Josh Crowhurst]

So we're gonna be doing a couple things. So the first is we're gonna have a room booked for us all day long where you can stop by and visit a couple of the co hosts and talk about what you've been talking about throughout the day or maybe one of the sessions you're presenting. And we're also going to have a couple questions posted up on the board day of and you can come in and give us your answer to those prompts. And then at the end of the day, during the happy hour, we're also going to do a short live show.

[Tim Wilson]

Will there be shots?

[Julie Hoyer]

So mark your calendars for Saturday, September 7th at 9am at the Leo Burnett Building, downtown Chicago, right on the river and just a couple of blocks from Michigan Avenue.

[Josh Crowhurst]

Get your free tickets now by heading to bit lyaph dash Chicago. And start thinking now about what you Might like to present or talk about.

[Michael Helbling]

Awesome. We're headed to Chicago, but now let's start the show.

[Ken Riverside]

Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

[Michael Helbling]

Hi, everybody. Welcome. It's the Analytics Power Hour. This is episode 252. You know, some of the hardest part is of the job in analytics is figuring out sort of how we fit into the bigger picture and interact with the people and teams that are supported by the data and analytics functions in a business. You know, beyond the various hard skills that make up a great analyst or analytics engineer, there's sort of a hidden navigation that has to occur to achieve the outcomes that we all want to create, you know, positive impact on the business. This episode, we might end up with more questions than answers, but it for sure affects all of us to the extent that we work within organizations and with other people. Speaking of other people, let me introduce my co hosts, Julie Hoyer, analytics lead it further. Welcome. Great to be on the show with you. And Val Kroll, head of delivery at Facts and Feelings.

[Josh Crowhurst]

Hi, friends.

[Michael Helbling]

And I'm Michael Helbling, managing partner at Stacked Analytics. But we needed a guest for this conversation, and who better than one of our favorite guests from before, Katie Bauer. She is the head of data at Gloss Genius. She previously held data science leadership roles at Twitter and Reddit, and today she is our guest again. Welcome back to the show, Katie.

[Katie Bauer]

Thank you. I'm glad to be here. I think the punch card that you gave me, if I get a third one, I get a free ice cream.

[Michael Helbling]

That's right.

[Katie Bauer]

I'll be angling.

[Michael Helbling]

Well, there's again, there has been a lot of discussion over the years of the Five Timers jacket a la snl. So, you know, you're now in the running for that as well.

[Katie Bauer]

Something to shoot for.

[Michael Helbling]

So, I mean, there's. The sky's the limit, really. But here's the reason you keep writing these articles that we keep passing around. And then we're like, we need to get Katie back on the show because she thinks about this stuff the right way. And so maybe as a starting point, let's just jump into a little bit of a conversation around what you were writing about recently, about the skills and the jobs and analytics and how you're feeling about it right now, because I think that was pretty poignant.

[Katie Bauer]

Yeah. So this is in reference to a substack post that I wrote recently that's really kind of a culmination of both seeing a bunch of thought leadership types of posts that have been Going out in the past couple of months about why do data teams exist? Do they need to exist as separate roles? Should they be disbanded, et cetera? Or like, is data a job or is it a skill that someone else should have? And it's also in reference to just conversations I've been having with friends with people I worked with previously and all sorts of different data professionals about like, what actually is going to happen to the job of data and AI is really top of mind for people right now. As you see all these AI to SQL companies coming out that like people get kind of perturbed by it. And I see them myself and I'm like, well, my job's not writing SQL, so I don't really care. I do a lot more than that. And like that, in conjunction with all of the kind of doom and gloom posts that have been coming out recently about whether data needs to be a job, I just eventually got to a point where I had thought of what I wanted to say on this, which is just I do think it's a job. And jobs are like usually an assemblage of skills that get applied to a particular situation. So this was kind of me working through it and really just thinking about things that had actually happened to me that were examples of someone thinks of my job as writing a SQL query or building a model for them or something. But really it's much more than that. And they fixate on the specific deliverable because that's the thing they interact with. But there's a lot that happens before that point comes up.

[Julie Hoyer]

One of the things that you said in that article too that I loved, you mentioned how everyone needs to use data in their position. But it's interesting that most of the time they can't generate it themselves. To your point, right? Like there's someone they always have to rely on to get the data. And so if those skills took too much time of their original position and were spun out to this job, I do find it shocking that so many people are trying to claim then that the job will go away and that it's not necessary. Like where do you think that actually comes from though?

[Katie Bauer]

Yeah, I mean, I think with a lot of the thought leadership types of conversations that happen in the data world, partly it's that it's data people saying this and they're kind of assuming that the cross functional people that they work with enjoy the data work as much as they do. And because it's getting easier for them to do, they're like, oh well, other people are going to be doing it at some point too. And maybe it also comes from you start having cross functional people working in your tools. Like I. The company that I work for now is a very data hungry and data savvy company overall. And there are people working in tools that I would never expect them to have worked in previously. Partly because the barrier for entry is so much lower, but partly because I think it's just considered more expected. But I still find myself constantly having to go in and advise around it or get them started or I don't know, like sometimes I almost feel a bit like a lifeguard where someone's swimming in the data lake and you have to help them when they get too far deep in because they maybe can't swim against the current or they've just gotten a little in over their head.

[Julie Hoyer]

Do you feel like that's a good thing though? Because I feel like half the time we talk about feeling like we're bogged down by these requests for different breakdowns, different numbers. Build a dashboard. Kind of like the light the shallow end, like let's keep that analogy going. The shallow end of the pool and we want to be swimming in the deep end. So do you think that maybe some people are being too negative about it and that people being able to self serve a little bit is actually going to free us up to do more of the work we'd rather be doing?

[Katie Bauer]

Yeah, I mean, I don't think it's good or bad necessarily. It kind of depends on how you interact with it. But I do think you're right that this frees up our time to get to the meatier things. Like maybe another version of this is. Every SaaS tool you buy these days has a dashboard feature in it and the salespeople will be like, wow, isn't it great? You know how to talk to a data person to like learn about this tool and like that's true. But the questions never end there. And figuring out how to stitch that all together tell a bigger story like that is really valuable work. It may be painful or annoying for us to do, but treating it as something that's an opportunity to influence, an opportunity to say how things are working, whether that's how we expect or not, like that's a huge opportunity. And partly I think the problem is data teams are not insisting on being involved in valuable things. A lot of times we want to have requirements dictated to us because it's a lot easier to just do what someone asks us to do and it's harder to know what to do in this difficult situation. And like, this is a thought I'm having in real time. I haven't thought this in depth, but it's almost like you're getting a little bit of a switcheroo with cross functional people wading into data tools is like they're kind of going into this thing that's maybe a little bit better specified sometimes and abdicating their responsibility for knowing how things are supposed to work and what the data is supposed to be reflecting. And because they're going back to the shallow end, no one's in the deep end. And suddenly, like, someone's out there doing something that they shouldn't be. Or this metaphor is maybe getting overextended. But, like, you need someone to be over there, like steering and guiding or making decisions about what needs to happen. And if people want to go into the shallow end and splash around and they are leaving that other stuff on the table, I think it's an opportunity for data professionals to get into that space and drive conversations that need to be driven.

[Josh Crowhurst]

I love the way you put that. The data professionals need to be insisting to be a part of those conversations more. I'm curious, and this is for anyone, if we're talking about other people on these other, you know, stakeholder teams dabbling in our role in data, do you think anyone on the business side would ever say, like, whoa, whoa, whoa, you're getting too businessy on me. Stop asking all those questions about how things work around here. I just, when you were talking to Sal's thinking, like, I don't know if it would happen in reverse. It would be healthy.

[Katie Bauer]

Yeah, no, I mean, that's a great point. Like, why shouldn't we wade into territory where we're not explicitly invited? Like, I don't think people are gonna shoo us away.

[Michael Helbling]

Yeah, I've definitely been told to stay in my lane before, but that's because I'm just probably. My approach was poor.

[Josh Crowhurst]

Yeah, it sounds like a you problem, Michael. I don't know.

[Michael Helbling]

Yeah, no, I'm fully willing to admit that.

[Katie Bauer]

Yeah. Well, I mean, I guess like a version of it might be you provide someone a recommendation and they. They're like, I don't need that from you, but thank you.

[Michael Helbling]

It's time to step away from the show for a quick word about Piwick Pro. Tim, tell us about it.

[Tim Wilson]

Well, Piwick Pro has really exploded in popularity and keeps adding new functionality.

[Michael Helbling]

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.

[Tim Wilson]

We love running Pivot Pro's free plan on the podcast website, but they also have a paid plan that adds scale and some additional features.

[Michael Helbling]

Yeah, head over to Piwick Pro and check them out for yourself. You can get started with their free plan. That's Piwick Pro. And now let's get back to the show.

[Josh Crowhurst]

One of the other things I loved in your piece, Katie, is where you're talking about data literacy. And it was so well put. And I had never thought about it that way, about how it could be mistaken for raw intelligence and how it can make people just. Even the premise of how that's framed can make people feel intimidated or even stupid. But would love to hear you talk a little bit about that. That's been one of the ideas or the solutions of how we can bring some of this together. It's a really common, you know, programs being spun up to conquer it that way. But we'd love to hear you just talk a little bit more about what you've seen in that space and how effective or not that's been.

[Katie Bauer]

Yeah, I mean, this one is a big ball of wax. But I guess this is from another piece that I wrote, I think you're referencing called the Wear the Sloth, with the sloth being kind of a tortured backronym on my part. But it's a statistical, logical and overthinking hesitator. And that's proposed as an alternative to the hippo, who's someone who just kind of does whatever they want, doesn't care about the data. The sloth is someone who is so obsessed with the data that it's actually a problem. And there's a lot of different versions of it, but one that I feel like I see a lot is someone who, they know that they're supposed to use data, but they're kind of uncomfortable doing it. And they would never tell you that because the expectation is that everyone is going to use data. And that's just how business works. And if you're not able to think analytically, then maybe you're just not that smart or not cut out for business. And, like, part of this comes from people telling me in private that I've worked with, like, I don't feel comfortable doing this. I would never admit that I can't use the BI tool in public because it would be embarrassing, but I can't. And I need help on this. And it also comes from, like, I've had this happen across many different jobs or some cross functional leader that I partner with comes to me and says, I want my team to be more quantitative. I want them to use more data. And then I'm like, okay, great. How? And they can't actually tell me. Like, they want numbers and things.

[Josh Crowhurst]

Numbers and things.

[Michael Helbling]

Yeah, we need more numbers.

[Katie Bauer]

Yeah. Like, they don't know what the numbers should actually do. It's almost like they want the numbers to help them make a case, which maybe this is kind of like a hippo, where they just kind of want to do something, but they need numbers to justify it.

[Josh Crowhurst]

Yeah.

[Katie Bauer]

So they're trying to drag you in and help you or help themselves make a point that maybe it doesn't have anything to do with you. Maybe you don't have context. Maybe you don't even actually agree with it. But they just want a number so that they can say that they looked into it and they did the homework on it.

[Michael Helbling]

Yeah. It's almost like the data will tell me what to do if I just somehow ask it the right thing.

[Katie Bauer]

Usually that's very common.

[Michael Helbling]

That's never. Usually you have to think about what to do and then let the data form how you steer it, not give you all your ideas.

[Katie Bauer]

Yeah, well, and like, a bigger problem related to that, too. Like, the term data science maybe is falling out of fashion, but it's one that I really like because science has theories and hypotheses and, like, there's like, an actual model of the world that you're trying to build. It's not just, here's a spreadsheet of all these observations of our telescope. Like, you wouldn't do that. You would have a question about, like, okay, like, what objects are in the sky that you're looking at with this telescope? And what does it tell us to study data? What questions do we have? But a lot of times people have this idea that if they just have a dashboard that they can slice and dice in all the right ways, eventually something's going to come up and it'll be obvious what to. And obviously I don't need to tell any of you. That is kind of a troublesome path.

[Julie Hoyer]

So I keep getting stuck on the part of your article because I hadn't thought about this before, how you kept saying data was being asked of people in their roles, and it became too much, and so they spun it out and said, it's taking up too much time and effort. I need someone else to own it. But I find it so interesting because everything we've just been talking about, like, the data professional doesn't always Feel like we're empowered to own the numbers. We are requested and asked and told. Like, go fetch XYZ most of the time, right? Like, how come it is not more common to be looked at as a partner? And then the thing back to like the pool, right? Like, why aren't we allowed to dip our toes in the business side as much? It's so hard to get that context. And we're kind of siloed off at like, look at the numbers. But you don't get to be in the conversations that are driving the, like, why they even want those. And if we had that, we could be so much more helpful to them. We'd probably feel less cynical about our roles. You know, like, it's just this crazy, like, cycle I keep thinking through. So, like, why, how did we get there? Why is it like that? Have you found a place where that is less the case? There's a more ideal way of working?

[Katie Bauer]

Yeah. I mean, I don't know that anyone's totally figured this out, but a thing that I tell my team a lot, like, this is a company value we have at Gloss Genius, which is to strive for excellence and expect it. And that's something I really hone in on with my team is it's our job to look at what's happening and say, is this excellent? Like, is this actually good enough? And data is often a way to answer that question or to ask that question. Like, it's maybe the start of a conversation where we're not the ultimate decision makers, but we can certainly create the conversations that need to happen. We can tell people when we don't believe answers. We can be a thorn in someone's side until they actually figure out something that needs to be figured out. I think that's one way to start. Like, truly. I think one of the biggest issues with data teams is that we do not act like partners. We want people to tell us the questions. And even the way that people like ask follow up questions when they get asked to do something can be very passive. Like, there can be a lot of, like someone asks you for something, you're like, okay, well like, what's the value of this? You shouldn't have to ask them that. Like, you should be trying to figure, like, well, I mean, you can't ask them that, of course.

[Josh Crowhurst]

Good point.

[Katie Bauer]

Like a team that is a partner to another team should already have context. You should be engaged with the metrics of the business broadly. You should understand the strategy. Your priorities should be the same priorities as the company generally. And that is something that helps you step forward and be more of an active player. It's wanting to achieve the results that the company needs to achieve. And your role in it might be to drive others towards them or help them figure out what's going wrong or what they need to do to get ahead. Even if you're not the person who makes the decision, ultimately, it's sort of like being an opinionated and trusted advisor.

[Julie Hoyer]

So can I ask you a thought experiment that I have some conversation took me down this path, and I would love your thoughts. How come the data professionals can't be the decision makers? Like, if we're the ones that are supposed to dig around, look at the data, understand how to use it to answer these important business questions, like, what would happen if I think value had put it at the time, like, we were given the keys to the castle, to say, like, yeah, I'm gonna be the one that gets to have the power to say, go left, go right, or like, we're gonna do this, we're not gonna do that, because the data told me, is that feasible? Could it ever happen? Should it happen? Should it not?

[Katie Bauer]

Yeah, I mean, I think it does happen sometimes. And it just gets called something else. Like it might happen in an operations role. It might get called growth. There's definitely a school of thought people have that eventually, if you reach a certain point in your career as a data professional where you want to call the shots, you need to go to a different role. And maybe you don't call yourself data anymore, even though you're doing the same things. I don't necessarily agree with it, but I do think maybe a reason why people aren't gravitating towards it as much is that it's kind of a scary thing to sign up for. Because part of calling the shots means you can also be at fault if something doesn't go well. And not everyone wants that. Even if they may want to be the authority, they want to call the shots. They don't want to be the one who's held accountable when it doesn't go through. That's something more data professionals should be comfortable with, I think. And there are probably areas where it's more feasible than others. And they're probably something that's very quantitative, like maybe it's running a lift testing program in a marketing organization or driving pricing strategy or something. There are definitely a lot of areas that I encourage people who work in data, if they want to be more decision makers, to sign up to drive an initiative. And that means Signing up for the consequences as well.

[Josh Crowhurst]

That accountability piece, some people really drawn to it, but it can be intimidating. I know your thought experiment is coming from Julie, because especially spending time, you know, more recently myself as well, on the consulting side, supporting an extension of an analytics team, starting a call by saying, so did they, did they do what we recommended or did they just do whatever they wanted? And like, that's just, you know, what, I should go back and check. And you're like, what? Like, how do you not already know? Yeah, very common.

[Katie Bauer]

Yeah, well, and like that, that impulse being missing is an issue with a lot of data teams. Like, you should follow through. You should not consider what you did a success unless you actually see the result of you made a recommendation and they followed it. And ideally the recommendation also went well. But you can only ask for so much at one time.

[Michael Helbling]

And I think, Julie, one of the other observations I have about that is when you take on a role that's not a data role, like a leadership role or an operational role or something like that. For me, it was also sort of a journey of figuring out how I bring my data skills into that role effectively. And it was actually pretty cool as I got into it to understand how they helped me. And so as I made that transition, it was actually like a benefit. And actually that helped me perform better than other people in certain areas because I was able to break down the numbers and understand the data much better and understand why the data was the way it was and explain that much better because I've had that experience. So I do think there's a quite a lot of talent as being a data professional that serves you extremely well in other roles should you choose to take them on. Yeah, some two cents there.

[Josh Crowhurst]

So I know that I accidentally already jumped us to talking about your sloth article, which is also amazing, but could we talk about each of them a little bit? Because when I was reading that, I mean, I fully need to forward that to my therapist because you help me process and identify why I had friction with so many people who I thought were my evangelists internally. Like, I thought they were like the people I needed to get buy in from when I worked internally to make things happen. But I was always like, when I get an email from them, just get so frustrated. But I was like, that's why. And so, yeah, lots of faces are coming to mind as you were sharing them. So I would love if we could talk through a couple of those and some of the key markers and even some of the ways that you recommend Data professionals work better with those sloth.

[Katie Bauer]

Yeah, sure. It's funny that faces came to mind for you because I definitely had specific people in mind.

[Josh Crowhurst]

Has anyone reached out to you and been like, I'm sorry, am I the distrustful sloth that you were writing about?

[Katie Bauer]

I mean, actually unironically, yes. Someone did ask me, and if they weren't, they weren't the person, but I was like, no, no, you're. You're fine.

[Michael Helbling]

Yeah. And you can always just deny it. Be like, of course not.

[Katie Bauer]

Yeah. I mean, this is a work of fiction. Any resemblance to real events is purely coincidental, but not purely coincidental. But anyway, like, I. The three that I outlined in the post were the uncomfortable sloth, which is kind of what I was describing earlier, where this is a person who. They're expected to use data, but they don't necessarily feel prepared to do so. They're really recognizable by analysis paralysis or maybe uncomfortableness or unfamiliarity with specific tools that they would be expected to use, particularly bi tools. These are people who, they're gonna send you digging. They're gonna expect what we were talking about earlier, where they eventually slice the data in the right way and then suddenly the shafts of light come out of the clouds and angels sing. You have your answer, what to do. These are people that, like, a really ungenerous way to think about it is they're kind of outsourcing their thinking and their decision making to you as a data professional because they just don't know how to engage with data whatsoever. And they won't tell you that. And they'll use that position of authority that they have to just keep having you, you dig, to actually deal with them. I would never recommend calling them out. You don't want to be like, you actually are a sloth and you're really uncomfortable and you don't know anything.

[Julie Hoyer]

You're a sloth.

[Katie Bauer]

Much better way. I mean, calling someone a sloth is probably never a good idea. But at any rate, I don't think you should ever antagonize someone. What I would recommend is just being really explicit about, like, these are my assumptions. This is why we did this this way. This is exactly what I mean. Here is exactly what I recommend, and here are my exact next steps. This may feel frustrating to do because it kind of feels like, well, you asked me for all this stuff. Why don't you know what to do? But this is kind of what I was describing earlier in terms of like, you can just tell people what to do. And that is A form of organizational influence. And maybe they don't say that you told them what to do, but they're doing what you say. It's not optimal, but it is still a form of organizational influence. It's not that bad. And in general, I would say, like, lead by example and working with a uncomfortable sloth of just like, admit when you don't know something, admit when something is complicated in data and like, try to build their comfort with it over time. A second type is a distrustful sloth. And this one is a little more insidious. This is a lot more of like someone is trying to weaponize data against someone else. It might be a manager going after one of their direct reports. It might be like two rival stakeholders who you work with, both of them that are like trying to get the dirt on each other. It could also be someone who has a legitimate concern where they think some team is underperforming, but like, they're not necessarily doing it because they care about the business. Like, they're doing it to get one over on this other person. This one can be kind of hard to recognize because it's very reasonable to ask questions about like, this thing is not working the way that I would expect it to. Can you help me figure out what's going on? There might be boundaries outside of whatever systems this stakeholder works with that you as a data person are aware of and can just help them connect the dots on these systems that aren't automatically connected. A lot more of what you should look out for and looking for a distrustful sloth is kind of the context around the ask where like, is this other person being brought into the conversation that they're seemingly trying to get dirt on? Are they considering a reasonable possibility that the person asking would they accept responsibility for this or are they dead set on it being this other person? Like, it's, it's really more about what are their intentions and asking this. Which kind of requires a little bit of follow up and context and understanding of the stakeholder relationships to get at this. But it's important to dig because you really do not want to get involved in fighting someone else's fights for them. These things never go well.

[Michael Helbling]

Yeah.

[Katie Bauer]

And it's just not fun to get caught in the middle of something. And I guess in terms of dealing with them, it's really about getting the surrounding context and making sure you really understand where this is coming from and what they're going to use it for and making sure that it is actually about business outcomes and not just A spat between two people. And then the final type of sloth that I identify in this article is what I call the dreaming sloth, which is basically someone like this one is like very relevant now in the AI hype cycle, where it might be someone who's really convinced that like data has some kind of magical properties that like, they've seen it used really well in this one particular way, or they did this one thing at their previous company or they know this technique is really good and like the data professional is the one that is going to help them do it. And for this one you kind of need to be careful because like, it can be really exciting to have someone come to you and be like, I think we could build a huge business around data selling or whatever their pet thing is, or AI. And like the thing to be careful with on this one is really like recognizing immediately. Like, is this tied to anything else we're doing? Is this a person who has credible organizational authority to drive an initiative like this, or are they just like kind of coming up with a cool idea and trying to get you to go do all the hard work of figuring it out? Like the example I give in the article that I think is illustrative here is, let's say you want to have a data selling business associated with your company. If you're vice president of business development comes to you and suggests that it's more plausible that they would actually be able to make something happen there than just some random PM that you help design a B test. Like that person, maybe they can really think through it, but they don't have any authority to actually give you or grant you or resources to put behind you. So like, once they make the case, what's going to happen? Probably nothing. Companies usually don't just let a random person drive a huge bottoms up new business line. And like, that's the kind of thing that you need to think about with a dreaming sloth is, is this actually something that makes any sense for the business? And in general, I would encourage everyone to be skeptical when you start getting pulled into something that feels sort of too good to be true that's data related. Because usually it's not going to happen. And it might be able to, but probably a lot of other things need to happen first. And you shouldn't spend a ton of your time working on something like this until it's clear that it's actually connected to what your company is trying to do and actually going to be valuable work.

[Josh Crowhurst]

I find myself really drawn to those dreaming slots I actually think I've reported to several over my years because a lot of them weren't analytics professionals and myself like mesmerized and gotten so distracted by some of the ideas that they had and just would eat it up. And I was like, oh yeah, like that was why I was working Saturdays in the office because it wasn't tied to the actual work that we were doing there. But yeah, that was a really good description. I definitely thought of a lot of people within that one and. And well intended. Right. Like that one's not like the distrustful sloth. Like they're just big idea people who can get really excited and want you to be excited about, you know, the role they think data can play. Yeah, so that was a good one.

[Michael Helbling]

It's really cool to kind of try to picture yourself too. So. Because I looked at that and I was like, okay, so I waste most of my time with the uncomfortable sloth because I just believe I can help them somehow. And I'm most likely to be the dreaming sloth because that's my personality is just sort of like, oh, there must be something we can think up that's cool here. And I have to, you know, bring it back in. So it was cool to try to think about, well, you know, which one of these are you and which one are you likely to lose cycles on? So that was mine. It's like a personality type analytics at.

[Josh Crowhurst]

The end, a horoscope sloth.

[Julie Hoyer]

Are you, oh my gosh.

[Josh Crowhurst]

What sloth do you waste most of your time with the uncomfortable sloth? We had an acronym for someone at a previous company that was basically like, could we also see that cut by which I feel like is like prototype for the uncomfortable sloth. Like that face.

[Katie Bauer]

Yeah, no, I, I do think there, there are certain like phrases or analysis techniques that become memes within companies where maybe it'll be like, oh, like can we see this by new and existing users or whatever. Like it'll be something that was really important once and then it becomes important for.

[Michael Helbling]

Right. It's like, why? Why? And then the distrustful sloth will literally rip data teams into multiple parts. And that's why you have like little spin off data teams in different departments and they'll be like, well, I can't get what I want over here so I'm going to hire my own analyst and build a little tableau instance or something so I can get the data I want. And yeah, it's brutal. It could be terrible for the data Org for that stuff to be unchecked.

[Josh Crowhurst]

Well, at least we have it all figured out.

[Michael Helbling]

Yeah, well, I said it's a show. Like it's not necessarily going to be all answers here.

[Katie Bauer]

Going back to the big picture, we will have job security.

[Michael Helbling]

Yeah, that's right.

[Katie Bauer]

These skills are very intimate.

[Michael Helbling]

Let's see you handle that AI right.

[Josh Crowhurst]

Get a little Skunk works project over here. We'd love your help.

[Katie Bauer]

Like we're joking a little bit by saying AI can't do that. But like, I think some of the value of the data professional is steering people away from things that aren't a good idea as well. Like having some big fight about who actually drove this incremental new user population. Like, usually it doesn't really matter that much because the company is doing better. And if a data team refuses to incentivize that fight, it is valuable. Like you're keeping people focused on the right things and that should be like, said and appreciated is that keeping people focused on the right things is a really important way for data teams to drive the conversation and insist upon being a part of value. If you are getting dragged into things that are a waste of time, it's probably a waste of other people's time too.

[Josh Crowhurst]

So how do we land on the right level of depth that we want to be able to expect from those business partners? So I think we started to touch upon it a little bit with the data literacy. So if it's not training on those specific hard skills necessarily, like if that's not what's most important to us, what would be the data literacy? Renamed and rebranded 2024.2.0 to help drive more fruitful discussions, then.

[Katie Bauer]

Yeah, I don't know if I have a name for it, but I think the most important thing is to clarify expectations about where data is useful and have a clear purpose for introducing data into situations. And by this I mean like a lowercase data, not like data as in a data team, although those are related. Like if you want to use data to help you discover new opportunities, for example, like how does that actually happen? Like, what physically do we want to happen in that case? Is it that we want someone to look at things that are related to products that we're already building or audiences that we're already marketing to? Is it that we want someone to go do some crazy experimental spike and figure it out? Like that kind of thing is helpful to specify what you actually want. And it also comes up a lot in things like impact measurement, where for data people, I feel like we always want to be really accurate and precise and get into the cool methods that help us quantify impact of something. But before you do that, it's probably worth actually taking time to think about the requirements of the situation, where it might be. Do we need a really quick go, no go answer? We don't need a really precise answer on something like that. Sometimes. Sometimes we can just launch something doesn't tank in the first couple of days and put whatever guardrails you want, like whatever level of rigor you want on that, but don't automatically default to a ton of rigor. I guess what I'm describing is you have a situation like a problem that you're trying to solve and then you go and you apply data to that, which may be how you actually use techniques. It may be the specific data sources you want to integrate. It may just be talking about the process and the expectations overall. But you should have a purpose and it should be something that's actually going to help you operate better or drive an outcome for the company. And that's where you should start. I don't think it should start with like, can you read a bar chart? It's like, that's important. But I don't think that's the thing that actually makes a data team valuable is that we know bar charts. It's helping people to focus on the right things.

[Julie Hoyer]

It sounds. And that the new training that would be best for our stakeholders.

[Katie Bauer]

Right.

[Julie Hoyer]

If we're the data team and we're giving a training to our stakeholders, it's almost feeling like we're saying we want to train you to know in general what we can do for you and how to identify, like, where to bring us in. That's kind of how it feels like it's going is we're saying, do we need them to be able to do the data skills on their own? Do we want to train them in those or do we want to train them in slyly better partnering with us? Like, that's kind of how it sounded.

[Katie Bauer]

Yeah. I mean, my general inclination, I'm like, not neutral in this, of course. My general inclination is better partnering with us rather than people having to go and learn all these very specialized skills, which tools make easier to like approximate doing them, but not to do them well. But, like, even more so than like training people about when to ask for these things. I think we should spend more time, like learning what other people are doing and inserting ourselves in the conversations of like, hey, it seems like you're trying to do this. Here is a way that we can use the data that we have for this or here's the way that we can use the tools that we have for this. It's being problem forward rather than like giving them a menu to pick from.

[Julie Hoyer]

Oh, the menu. The menu is the bane of my existence. I hate that.

[Josh Crowhurst]

I'm curious how, because this is, I mean that makes so much sense in the problem forward framing. Wrote that down, Loved that. How do you coach your team then to work like this inside of your organization? Because it feels like a pretty unfortunately novel because we're kind of all nodding our heads like, yeah, it makes so much sense, like ingrain yourself and be more pumped forward. But what are some of the guidance that you give or ways that you instruct your team to be more effective in that way?

[Katie Bauer]

Yeah, I mean, one place where it starts for us is in how we even talk about our goals and the way that we operate in the company. We always talk about an outcome. It's not like improve data quality, it's reduce overhead of queuing the eventing in the product or whatever so that engineers have more time to go and build XYZ thing. Like, it's talking to people about like, here is the value of the thing that we are doing for you. Because if I go to people and I'm like, you need to register your schemas, they're going to be like, why? That feels like it's slowing us down for no reason. But if we explain like a technical project like that in the context of all the benefits that they get and all the things that they are not going to have to think about, they understand it a lot better. So one big piece of this is coaching the team to speak about what they are doing in a way that is really in the language of the people that they're doing it for. Otherwise the people may not understand it. And another piece of it is just as we're working through things, talking about like, okay, is this actually going to move them towards their goals? It's a lot of refusing to do work that maybe feels low value. People will always try to get you to pull data for them or to do an analysis that maybe like proves that what they're working on is actually valuable, even though it wasn't in the A B test that they ran. And, and like, what you actually need to do in that situation is be like, hey, like we are doing these other things and they are contributing to whatever metric, whatever company objective. And like, we will stop doing that if you want us to do this other thing, which we don't think is a good idea. So you can show us that that is higher priority. But until we have a reason to believe that it is higher priority, we're not going to deprioritize what we're already doing. Also spending a lot of time building context for the business, that's another really important thing too. And even people who are maybe further back in like the kind of traditional data pipeline model, like data engineers and analytics engineers, I expect them to know things about the business and I talk to them about the performance of the company and why things are in the place that they are and encourage them to think about what they are doing and contributing to that so that they have the context to be able to make calls like that about. Like this task that I've been asked to do is probably not super valuable. So if there's something that is more obvious and it's ROI more possible, and it's roi, I'm going to do that.

[Josh Crowhurst]

That's great. Help them build that intuition.

[Michael Helbling]

How much does it matter the broader organization and its posture towards that in terms of the data team and the data professional? So in other words, you know, you're describing that for the company that you're working in and I think probably you're spearheading a lot of that. But how, when you joined that organization. I'm sorry, I don't want to pick on your company. I'm just saying, like, generally speaking, you know, from your experience, where has that worked well and what markers of organization are good identifiers for it? This. Okay. This is going to be a good partnership.

[Katie Bauer]

Yeah. I mean, that's a great question. And I think it comes down a lot to how willing are they to have a conversation with you.

[Michael Helbling]

Yeah.

[Katie Bauer]

People being receptive to feedback, understanding that you're not just a vending machine, and really like demonstrating to them like you care about the outcomes and when you disagree with them, that it's about you working towards the same goals as them that makes them feel like you are more on their team. And like mistakes I've made in the past have usually been about like not convincing people that I was on their team and like asking them to do things. And I like, go away. Like you're, you're annoying. Why are you asking me for this? But if I'm committing to hitting the same goals as a team or people on my team are committing to hitting the same goals, that changes the conversation a lot. It makes it a lot more of a partnership where we're both trying to accomplish the same thing and we may disagree and we should have a discussion about it. And sometimes we will disagree and commit, but we also expect other people to disagree and commit.

[Julie Hoyer]

Sometimes that's good when you say that too. It makes me think of being in consulting. It really is a big deal, that first impression you have with a client on a project. Because to your point, if you aren't able to early on establish, I think that you're there to be a partner with them to like help them achieve these outcomes, you very quickly fall into what we call a lot like an order taker. And it's really hard to dig yourself out of that. And then if you try to push back, even in a respectful way.

[Josh Crowhurst]

Right.

[Julie Hoyer]

Of like, well, if we prioritize this, then we can't prioritize this other thing. They usually don't take too kindly to it. And you usually don't have the ammo to your point, to say we suggest prioritizing this other thing because it ladders up to what actually matters to your leadership and you know, the bigger goals of the business. Like you don't get that. You kind of get siphoned off into. We're gonna send you guys requests, it's gonna come in as tickets, you know, or we're gonna have our weekly meetings, we're gonna toss some things over the fence, you're gonna toss some things back and you might get to like peek through every once in a while. So it's such a good point that you bring up as like that beginning initial relationships. Important.

[Katie Bauer]

Yeah. Another thing I value a lot is people on my team having standing relationships with different cross functional stakeholders because it does make it more of an ongoing partnership where it's not just like the only point of contact that they have is that a JIRA ticket shows up at one point.

[Michael Helbling]

Yeah.

[Katie Bauer]

They attend their meetings, they weigh in, they participate in their retros and their goal setting processes. I think that's really important for data teams if you can manage it from a bandwidth perspective to be involved in a lot of other team rituals because you are literally a part of that team in that case.

[Josh Crowhurst]

That's exactly the question I was going to ask is I would love to get specific about the ways that you demonstrate that you care about the outcomes. That phrase really stuck with me when you just said that, Katie. So participating in those team ceremonies and like really being ingrained, even if you're a centralized team. Right. Even if you're not distributed or you don't have a dotted line, it's like really putting yourself out there. So I Think one of the things that I've always felt, but I'm feeling even stronger position for, is that the responsibility in this relationship is really on the data professional. And maybe some people will think that that's unfair. Like, why does it have to be my job to push into these conversations? But there's so much benefit like that. I just don't. I'm not seeing any downside to this. To show and to demonstrate how focused you are on their outcomes and to really being green. And that's only going to help you, you be more invested and excited about your work because every second you're spending isn't being an order taker or a vending machine, which I love that one too. It's doing really meaningful work. It probably is more inspiring to spend your time doing things like that versus adding another filter to a dashboard and throwing it over the wall. So, yeah, so this is a call to analysts out there. It's on your shoulders whether you like it or not.

[Katie Bauer]

I mean, the one thing I'll say on that is that I don't think it's entirely on their shoulders to drive everything. Like, it does take two people to have a partnership, but you can't at least start it. Like, the best way to make a friend is to act like a friend. So showing people what you want to be treated like is a good way to actually start making that happen.

[Michael Helbling]

This is great. All right, we do have to start to wrap up though, so good. Katie, thank you so much for coming back on the show. Really appreciate it. All right. One thing we love to do is go around the horn, share something we think is interesting. We call it a last call. Katie, you're our guest. Do you have a last call you'd like to share?

[Katie Bauer]

Yeah. One thing that I mentioned earlier, a good thing for people to think about is what are quantitative things related to data and how can you get more involved. A book I really recommend reading related to this is called the Strategy and Tactics of Pricing. Pricing is something that can be very quantitative, but it's very cross functional in a way that probably is familiar to a lot of data professionals. That book is kind of a theoretical overview of pricing as well as a lot of specific things that you can do and analyses that you can do to figure out how much a customer would want to pay for something. It's really interesting even if you're not actually working on pricing, just as a way of learning more about how businesses work.

[Josh Crowhurst]

That sounds interesting.

[Michael Helbling]

Awesome. I know, I wrote that down. I was like right up My alley. All right, Val, what about you? What's your last call?

[Josh Crowhurst]

So my last call today is actually a episode of Lenny's podcast, which is a very product focused podcast. You can find it anywhere you find podcasts. I love watching the videos though, because he has such amazing guests and they get into such fun animated discussions. But one that I listened to or watched recently was when Clairvaux was on, which I'm a huge Clairvaux fan girl, ever since I saw her speak on stage when she was CEO of Experiment Engine. But this one was all about like bending the universe in your favor. And she talked a lot about her career and the choices that she's made. But there was one quote that like, really stuck with me because she's been, she currently is and has been the CPO of many organizations. And she said, people often think that I get hired into companies because I'm supposed to teach them how to operate like a big company. And in fact I'm hired to remind them that they can operate like a startup. And I just was like, so genius because she's worked at a lot of these, these larger organizations like Optimizely and launchdarkly. And so I thought that was really interesting and like she went into the details of the values of thinking small and the ways to organize this team. So anyways, I found it inspiring and interesting and fun as always. Like the Lenny's podcast. So definite. Good listen.

[Michael Helbling]

Nice. All right, Julie, what about you?

[Julie Hoyer]

All right. Mine is an article that was about like, fun new use of AI. Maybe not fun new, but at least to me I didn't know they were using AI in this way. And so I found it interesting. It is an article called the Sperm Whale Phonetic Alphabet Revealed by AI. It's an article on BBC and it's kind of crazy. So they've been studying these whales for years and years and years and they have all these recordings of them. And I guess humans were able to identify 21 like patterns. They call them codas. And then when they started using AI, AI was actually able to identify 156 distinct like patterns of these clicks. I mean, they're literally just clicking. I was listening to a recording of it. And so they're using AI though, to try to discover their actual, like, language. And they've been shocked to see that they may actually have much more sophisticated language than we ever thought, which was just really cool.

[Josh Crowhurst]

Oh my God.

[Michael Helbling]

So Douglas Adams was right.

[Katie Bauer]

Makes the book Moby Dick really different.

[Michael Helbling]

Yeah, right.

[Josh Crowhurst]

I want the follow up piece to Be like, so then we took that and are able to talk back to them using AI to write the sentences. That's what I want.

[Julie Hoyer]

Right. So, so crazy. We'll see. I'll keep an eye out for it.

[Michael Helbling]

But that's amazing.

[Julie Hoyer]

What about you, Michael?

[Michael Helbling]

Well, I'm gonna recommend a book too. It's something actually I think I've had as a last call before, but it's been probably five or six years, so I feel like it's what you could do a refresher. And it's because I recommended this to someone recently and then they texted me this week and said how much they were getting out of it. It's a book called the Effective Executive by Peter Drucker, which is a really old book. I think it was written in 1967, so if you read it, just throw out the parts that don't make any sense in our world anymore. But there are really great things and actually sort of germaned even what we've been talking about in terms of things. Thinking about how do you as an analytics professional kind of elevate and think about the business as a whole? It's a very short book, but really helpful in aligning thinking. So that'll be my last call today. All right. As you've been listening, you're probably like, hey, how do you find out about this stuff? Well, first off, we would love for you to go subscribe to Katie's Substack because it's where we find all this information. And then we. So we're going to include that in the show notes, so we'll include the links to that. So please subscribe to that because then you can be in the loop and one of the cool kids like us and we'd love to hear from you. And we're always on the measure Chat Slack group or our LinkedIn page, or via email contactnalyticshourio so feel free to reach out. And of course, we want to give a huge shout out to Josh Crowhurst, our producer, who is behind the scenes making all this possible. And also maybe just a little shout out to Tim Wilson for being a little bit behind the scenes helping make produce this show this time as well. So thank you to both of you. And I think that's it. Katie, thank you. Yeah, you're in the two timers club, which I think as of this moment and can't look into the future, but that's the most anyone's ever been on the show. So the next time you're on, you're gonna hit that Third Timers Club. Club, yeah, that's right. Three punches, free ice cream. Your loyalty card is in the mail. Anyway, so excited. We are very thankful. Thank you so much for taking on the time. And I mean, we just always get a lot out of it. And, you know, I always am telling people, Katie Bauer is the one who thinks about this stuff better than anyone else right now. You've got to read her stuff. So it's cool for me.

[Katie Bauer]

Thank you. That's a very, very nice thing to say.

[Michael Helbling]

Well, it's coming from me, so just, you know, it's not what you think it is. No, no. I mean, I. It's heartfelt in that. In that, at least, in that regard, I just. I really enjoy the way that you approach the problems in analytics and our industry. So thank you. And I know as you're sitting there trying to figure out how to partner with your org and be problem forward, remember, I know I speak for both of my codes hosts, Val and Julie, when I say keep analyzing.

[Ken Riverside]

Thanks for listening. Let's keep the conversation going with your comments, suggestions and questions. On Twitter at analyticshour, on the web at analyticshour IO, our LinkedIn group, and the measuredchat Slack group. Music for the podcast by Josh Crowhurst.

[Josh Crowhurst]

So smart guys wanted to fit in, so they made up a term called analytics. Analytics don't work.

[Ken Riverside]

Do the analytics say, go for it, no matter who's going for it. So if you and I were on the field, the analytics say, go for it. It's the stupidest, laziest, lamest thing I've ever heard. For reasoning in competition.

[Michael Helbling]

And I'm Ken Riverside. No, I'm just kidding. I'm Michael. Hell, it's. Sorry, that's an alter ego we're working on. We're just shopping it 4th Floor Productions. Really big in the Chicago podcasting business. But I'm actually Michael Helbling.

[Josh Crowhurst]

Rock flag and data work is definitely a job.