#255: Dear APH-y: Career Inflection Points

Summary

Podcast Summary: The Analytics Power Hour – Episode #255: Dear APH-y: Career Inflection Points

Introduction

In Episode #255 of The Analytics Power Hour, titled "Dear APH-y: Career Inflection Points," hosts Michael Helbling, Moe Kiss, Tim Wilson, Val Croll, and Julie Hoyer delve into crucial moments that define and shape careers in analytics. Drawing from listener questions submitted through a survey, the team provides insightful advice on navigating career transitions, leadership roles, skill development, and more. This episode serves as a valuable guide for analytics professionals at various stages of their careers.

---

1. Transitioning to an Analytics Lead Role

Listener Question: "I'm a software developer with strong SQL, but I have very little stats background and I'm about to be named analytics lead for a large team. What should I learn first?"

The hosts discuss the dual challenges of technical proficiency and managerial responsibilities that come with leading a large analytics team.

• Tim Wilson emphasizes the importance of resisting the urge to solely focus on technical problem-solving:

  "How do I get the data? Where do I get it? What are the caveats? ... build relationships and ... figure out what problems the business is expecting you to solve."
  (03:06)

• Mo Kiss highlights the significance of soft skills and understanding team dynamics:

  "Really understanding that niche role that data plays in being a business partner... It's all the soft skills."
  (04:35)

• Val Croll advises focusing on business problem-solving and team advocacy:

  "Advocate for your team ... speak to the capabilities in the way that your team can be most valuably serving the organization."
  (08:10)

• Julie Hoyer suggests engaging directly with team members to understand their roles and needs:

  "Talk to everyone on your team... ask what their day-to-day looks like and what their big pain points are."
  (05:15)

Key Takeaways:

• Balance technical skills with leadership and relationship-building.
• Focus on understanding and addressing business needs.
• Leverage team members' strengths without micromanaging.
• Prioritize soft skills to effectively manage and support your team.

---

2. Maintaining and Growing Skills While Out of the Workforce

Listener Question: "I'm currently out of the workforce. What can I do to maintain or grow my skills?"

The conversation shifts to strategies for professionals taking a hiatus from their careers, emphasizing the importance of networking and practical skill application over merely accumulating certifications.

• Mo Kiss underscores the value of maintaining professional relationships:

  "Don't underestimate your network... keep going to meetup and have coffees with people."
  (10:35)

• Val Croll warns against over-relying on certifications without practical engagement:

  "Keep your network alive... listen to podcasts to stay across changes."
  (10:48)

• Michael Helbling encourages contributing to non-profits or local businesses to apply skills:

  "Great places to contribute... it's both personally rewarding and adds to your resume."
  (12:16)

• Tim Wilson shares personal anecdotes about balancing personal responsibilities with skill maintenance:

  "Do a data project with your personal life, like tracking your day-to-day activities."
  (14:11)

Key Takeaways:

• Prioritize networking and maintaining professional connections.
• Engage in practical projects, possibly for non-profits or personal interests.
• Balance skill development with personal responsibilities to avoid burnout.
• Use downtime to stay informed through podcasts and industry interactions.

---

3. Pivoting Mid-Career into Analytics

Listener Question: "I'm mid-career and pivoting into analytics. What skills do I need to focus on most?"

The hosts explore essential skills and strategic approaches for professionals shifting into analytics roles, highlighting both technical and interpersonal competencies.

• Mo Kiss asserts the primacy of SQL in analytics:

  "SQL's number one."
  (15:36)

• Julie Hoyer emphasizes communication and documentation skills:

  "Own the process of pulling data... communicate assumptions and pivots clearly."
  (16:56)

• Tim Wilson advocates for strong data visualization and documentation practices:

  "Learn data visualization principles... document your process and assumptions."
  (18:09)

• Mo Kiss introduces the concept of "McKinsey headlines" for effective communication:

  "Make your title a declarative statement... it simplifies how others understand your work."
  (21:35)*

Key Takeaways:

• Master SQL as a foundational technical skill.
• Develop strong communication and documentation practices.
• Learn effective data visualization techniques to present insights clearly.
• Adopt strategic communication methods, such as McKinsey-style headlines, to enhance clarity and impact.

---

4. Transitioning from Data Analyst to Data Scientist

Listener Question: "I've been a data analyst, but now I'm trying to move into data science. How can I best do that?"

This segment examines the blurred lines between data analyst and data scientist roles, advising caution and clarity in pursuing such transitions.

• Mo Kiss discusses the ambiguity in defining data science roles within organizations:

  "Data science roles are not consistently defined, leading to role conflicts."
  (24:28)

• Michael Helbling points out economic factors affecting data science job markets:

  "Data science as a profession is more impacted by economic cycles than analysts."
  (25:31)*

• Val Croll recommends broadening the scope within analytics to tackle more complex business questions:

  "Broaden your definition of analytics and the data sets you work with to answer more complex questions."
  (27:09)*

• Tim Wilson shares his personal journey of expanding skills without fully transitioning to data science:

  "Expand your perspective and skills as an analyst to incorporate data science methodologies."
  (28:09)*

Key Takeaways:

• Recognize the fluid and often unclear distinction between data analyst and data scientist roles.
• Understand that economic conditions can significantly impact data science opportunities.
• Enhance analytics roles by tackling more complex and varied business questions.
• Continuously expand technical skills while maintaining a strong foundation in analytics.

---

5. Choosing Between Individual Contributor and Management Roles

Listener Question: "I'm at a point where I need to choose whether to pivot from being an individual contributor to a management role. What should I do?"

The hosts provide guidance on evaluating personal preferences and the distinct skill sets required for management versus individual contributor positions.

• Mo Kiss stresses the importance of passion for either doing the work or developing others:

  "Do you want to be the one doing the work or helping your team achieve their goals?"
  (34:37)*

• Tim Wilson encourages trial periods and setting timelines to assess fit:

  "Consider a 12-month timeline to evaluate if a management role suits you."
  (36:45)*

• Val Croll highlights alternative career paths, such as senior individual contributor roles:

  "Many organizations offer senior IC paths that allow for career advancement without managing teams."
  (39:04)*

• Julie Hoyer advises understanding the value you add as a manager versus an individual contributor:

  "As a manager, your value comes from influencing others and supporting your team's success."
  (43:15)*

Key Takeaways:

• Reflect on whether you derive more satisfaction from hands-on work or from coaching and supporting a team.
• Explore opportunities to lead on projects without fully committing to a managerial role.
• Consider senior individual contributor paths as viable alternatives to management.
• Understand the distinct responsibilities and expectations inherent in management roles.

---

6. Evaluating Analytics Positions During Interviews

Listener Question: "When I'm interviewing for an analytics position, what are some of the questions I should be asking the interviewer that will help me determine if the position is one that I should take if I get offered it?"

The discussion offers strategies for assessing company culture, management support, and the alignment of the role with personal career goals through targeted interview questions.

• Mo Kiss recommends inquiring about decision-making processes:

  "How are decisions made at this company?"
  (45:55)*

• Val Croll suggests asking about managerial support and handling of challenging situations:

  "How will our manager support us during budget cuts or difficult times?"
  (47:00)*

• Julie Hoyer advises understanding team dynamics and collaboration opportunities:

  "Does your team partner with other teams in the organization?"
  (49:36)*

• Michael Helbling humorously adds to the importance of workplace amenities as indicators of company investment:

  "Determine what the coffee situation is... it indicates the level of investment in the workplace."
  (51:53)*

• Val Croll emphasizes evaluating the analytics organization's trust and integration within the broader company structure:

  "Ensure the analytics team is trusted and not siloed or undermined by other departments."
  (53:49)*

Key Takeaways:

• Ask open-ended questions to understand how decisions are made and who influences them.
• Assess the level of managerial support and advocacy for the analytics team.
• Determine the extent of collaboration and integration with other teams.
• Gauge the company's investment in employee well-being and resources as indicators of overall culture.
• Ensure the analytics function is trusted and effectively integrated within the organization.

---

Conclusion

Episode #255 of The Analytics Power Hour offers comprehensive advice on navigating pivotal career moments in the analytics field. From stepping into leadership roles and maintaining skills during career breaks to transitioning between analyst and data science positions, and choosing between individual contributor and managerial paths, the hosts provide thoughtful and practical guidance. Additionally, they equip listeners with strategies to evaluate potential job opportunities effectively.

Notable Quotes:

• Tim Wilson: "How do I get the data? Where do I get it? What are the caveats? ... build relationships and ... figure out what problems the business is expecting you to solve." (03:06)
• Mo Kiss: "Really understanding that niche role that data plays in being a business partner... It's all the soft skills." (04:35)
• Val Croll: "Advocate for your team ... speak to the capabilities in the way that your team can be most valuably serving the organization." (08:10)
• Julie Hoyer: "Talk to everyone on your team... ask what their day-to-day looks like and what their big pain points are." (05:15)

For more insights and discussions, listeners are encouraged to engage with the hosts through their LinkedIn group, YouTube channel, and the MeasuredChat Slack group.

Stay Connected:

• Twitter: @analyticshour
• Website: analyticshour.io

Credits: Special thanks to Josh Cohurst, the producer, and Ken Riverside and the Fourth Floor Production team for supporting the show.

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This summary captures the essence of Episode #255, providing a detailed overview of the discussions and key insights shared by the hosts. Whether you're an aspiring analytics leader, a mid-career pivoter, or evaluating new job opportunities, this episode offers valuable guidance to navigate your career inflection points effectively.

Transcript

[Michael Helbling]

Foreign.

[Tim Wilson]

Analytics topics covered conversationally and sometimes with explicit language.

[Michael Helbling]

Hi, everybody. Welcome to the Analytics Power Hour. This is episode 255. You know, there's a quote that says, if opportunity doesn't knock, build a door. Analytics careers have lots of opportunities and things. Thinking about your path and being intentional about curating your career journey is really important. And as you go through your career, there are inflection points along the way. We all face them. Sometimes they require big risks and other times they just require saying yes or no to the right thing. You know, we did a listener survey and we got a series of questions from our listeners on this topic. And so I think it makes a ton of sense for us to jump in and explore this topic in more detail. And as always, I just want to give a huge thank you to our listeners that sent these questions in as part of the survey responses. So my name is Michael Helbling, and now I want to introduce you to my co hosts, Mo Kiss. Welcome.

[Mo Kiss]

Hello. Nice to be here.

[Michael Helbling]

Pretty exciting. We're recording this show remotely, but on the same continent. So that's pretty awesome.

[Tim Wilson]

It's a first.

[Michael Helbling]

Julie Hoyer. Welcome.

[Julie Hoyer]

Hello.

[Michael Helbling]

It's awesome. Val Croll. Welcome.

[Julie Hoyer]

Hey.

[Val Croll]

Hey, party people.

[Michael Helbling]

And Tim Wilson.

[Tim Wilson]

Welcome. Oh, there was a welcome. There's a. I thought about it and.

[Michael Helbling]

Then I was like, I better just be. Be nice because it'll probably get bad later.

[Tim Wilson]

That's right. It'll be the author. Yeah, we know. We know what it's like off mic.

[Michael Helbling]

Yeah, no, but I think so. This is awesome because I feel like this is such a great group of people to answer some of these questions because we have such a range of different experience and our careers and so let's just dive into it. Let me pull up the first question from our listener survey. So this is somebody who said, I'm a software developer with strong SQL, but I have very little stats background and I'm about to be named analytics lead for a large team. What should I learn first? That's a good question.

[Mo Kiss]

I actually do have experience of having a manager who was an engineer, so, like back end engineer. And there was definitely a steep learning curve that we had to go through. I think it also kind of depends. When you say analytics lead, are they like technical lead or are they manager lead? Because the answer is very different.

[Michael Helbling]

So it sounds like probably they're going to be managing a fairly large team as they step into that new role.

[Tim Wilson]

Yeah, let's go with they have both. Maybe it's even organized that way. No, you're saying it wouldn't happen. They'd report into different areas. Like, first learn a better org structure.

[Mo Kiss]

I'm just like, it's going to make the answer about 40 minutes long.

[Michael Helbling]

Well, better get started.

[Tim Wilson]

MO I would say, like the strong sequel, like, there's got to be some level of figuring out how to be a. You probably have a strong bias towards solving the engineering problem. How do I get the data? Where do I get it? What are the caveats? And there's going to be a big need to sort of resist that impulse. Like, I assume you probably have other people who will have that impulse in spades. And the need to build relationships and ignoring the data and just figuring out what problems the business is expecting you to solve and whether they're solvable or not. I. That would be my. That's like, what do you need to. To learn? It's like, oh, probably it's unlearned. I mean, those skills are needed.

[Mo Kiss]

I'm not saying how should you spend your time? And I think the thing is, when someone from engineering comes into the data world, they have a pretty good, like, model of how data flows from one place to another. They get the kind of architecture pretty easily, I think, because they've normally worked on problems that solve that. But the bit that's actually really complicated is like, really understanding that niche role that data plays in being a business partner. Answering really complicated questions sometimes with high levels of uncertainty and still being able to progress things forward for the team. It's all the soft skills.

[Val Croll]

Yeah. And I would say the question specifically said, strong SQL, but very little stats background. And so one thing to just call out is to keep in mind that you don't need to know how everyone does their job on your team to be a good manager or leader for those people. I think it's a lot of what Mo and Tim are just saying about connecting to the right business problems and understanding what the business expectations are. Soft skills, building relationship with your team. I think don't have this urge to like, oh, I need to learn all the details, because that's gonna first of all lead you down a path of micromanagement. And also keeping your eye not on the ball of the new responsibilities you're taking on in this new lead position and role.

[Julie Hoyer]

Mm. Yeah. And I'm sure this is part of the normal stepping into a role like this. But if you were to actually go and talk to everyone on your team, especially people maybe on the side where they're using the data after you've, you know, Worked with it, set it up, whatever. You may be surprised to find out what they feel like they need in their role. Or you could even just be asking them what is your day to day? If you don't know, I think that would help you start to get into that phase two of figuring out like how do you best help unlock their, you know, day to day stuff? Or what are their big pain points? Or do they feel like they don't know enough about the technical side and maybe you're able to step in and just help even the team communicate better. Because to Val's point, you don't need to necessarily know all the stats, but if the people on your team are able to better collaborate, that might be a good starting point.

[Mo Kiss]

Actually Julie, that's a really good point of like you need to figure out who knows what on your team so that you know when you have a question on experimentation or a task like who is the best person who knows the most about that and who's potentially going to be able to upskill junior people that know less like I totally one Val's comment. You do not need to know and how to do everyone in your team's job, but you need to know who to ask when that problem comes up. And so it could also be like not what do you need to learn from a technical perspective, but what do you need to learn about your team.

[Tim Wilson]

But I'll kind of take a slightly different because Val, as you pointed out, they said the very little stats background and I wouldn't want to over index towards I have people who know stats, I don't need to learn stats. And this, this will be probably the soapbox being mounted. But you know, the SQL piece is very deterministic like did I query the data? Did I get it? I think there's the understanding of statistics not necessarily being able to, you know, do a linear regression or an ANOVA or whatever it is, but I think there's something to get comfortable with what the data can't tell you, the world of uncertainty and maybe a light introduction to causal inference. If you find people on your team who have a statistics or a data science background, they may be a good resource to leverage. But because my concern is that you would go the danger moving in that direction is that you talk to the business and then you start steering yourself towards problems that can be deterministically answered, which there may need to be some level of education. Here's the business problem. What can we do? You know, to run an experiment? If you're doing SQL, you're probably not thinking experiments, even though experiments may be the best way to address some problems.

[Mo Kiss]

I do think you would learn enough about that in the course of your role. I just wouldn't say that that's the top priority or even in the top five. I'm like, you will learn about experimentation through the course of your job. That's inevitable because you will have conversations with the team about what's possible or not possible on a task.

[Val Croll]

Sure. I would just say that. It's just don't underplay how important of a responsibility it is to be able to advocate for your team when you're in a room that they're not. Because now you're a lead. So you're going to be pulled into conversations that you might not have been before, and someone's going to turn to you and say, like, well, what can we do with this? And so you're going to be able to speak to it where you're not writing checks that your team is pissed that they have to cash, or they're going to be like, oh, I could have done something really cool. But now I have to walk into this conversation immediately disappointing those business partners because I'm not able to pull this, you know, rabbit out of a hat. So I would say that I agree with you, Mo, that there's like a lot of things that we can prioritize before, like, you know, taking a Coursera course, that there is a lot that's going to be on the job. But don't, don't underestimate how important it is to be able to, like, speak to the capabilities in the way that your team can be most valuably serving the organization.

[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. They sure have.

[Michael Helbling]

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 pwik 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. And now let's get back to the show. All right, great coverage of that question. Let's jump over to another, a new one. So this person asks, I'm currently out of the workforce. What can I do to maintain or grow my Skills Coursera.

[Tim Wilson]

I mean, I see. If you're out of the workforce, like it seems like it would start with figuring out what you want to do when you re enter the workforce. I've talked to some people now who are out of the workforce and there's a tendency to, if they're looking to get back in the workforce, they're like, I gotta add skills to my resume. And they're trying to find the things that they can get certified on so that they can bolster their resume. Which, easy for me to say, that feels a little dangerous because it's not grounded in what was it that as I reenter that I mostly want to be doing.

[Michael Helbling]

Yeah, Tim, maybe you spent a long time out of the workforce before you started facts and feelings, so didn't learn shit.

[Mo Kiss]

Now I think, I think the thing as well is don't underestimate your network.

[Val Croll]

Totally.

[Mo Kiss]

I was chatting to someone recently who through no fault of their own, has been out of work for a little while and they decided to take a couple months off, relax, that sort of thing. Then was like, then I'll find another job. And the reality was the market is not what it used to be. It is actually really hard and they're finding it quite a struggle. And the biggest reflection he had was in that period of time off. I didn't put time into my network and that's actually so valuable. So, like, I don't know, I get a lot of resumes with people who have just done 50,000 Coursera courses and things that that makes them super qualified to do the job. And like, sure, it can add skills. Not disputing that, but the reality is if I was out of the industry for a bit, I would probably still keep going to meetup. I would still keep having coffees with people, which is actually what I did when I was out of work, like, keep my network alive. And I would also keep listening to podcasts because I feel like meetups and podcasts are a very good way of staying across changes to then know what things you need to learn to fill that skills gap. So I don't know, like, maybe I would have a very different perspective on what to do.

[Tim Wilson]

But this wasn't. I mean, I wasn't reading this question as saying what should I do to find a job to re enter. It was they're specifically saying they want.

[Michael Helbling]

To keep their skills up.

[Tim Wilson]

I mean, I think. I don't disagree. I also think it's one of those things that's like, don't wait until you're out of a job to decide to start networking.

[Michael Helbling]

Yeah. But in that vein, I do think there's many non for profits and even like local mom and pop businesses that have zero analytics capabilities, zero access to those skills and would be great places to contribute. And especially if it's things that you already care about or you're interested in, it's a great connection to make. I know that is a place where I've spent some time doing some small things for different groups and it, it's both personally rewarding as well as you get to answer some questions and work through some problems with teams and sort of burnish those skills. And honestly, I think, you know, if you are planning to enter the reenter the workforce or get back in the workforce, it looks good on your resume that you're. Things like that and contributing like that.

[Tim Wilson]

Well, it's great for a behavioral interviewing question when you're like, oh yeah, you know, by the way, I help feed 10,000 kids by doing a little database work, you know, or something.

[Mo Kiss]

It does depend though on the reason they're out of work.

[Michael Helbling]

Sure.

[Mo Kiss]

And the reason that I say that is I can imagine a person asking this question if they're on parental leave. And I think sometimes when you're on parental leave you have this like expectation of yourself that you're going to be able to do all this learning and volunteer. Like I volunteered to do a project on some equitability data on my first parental leave. Do you think I did any of that? No, not a single thing.

[Tim Wilson]

As I recall, you went from. You were missing it. So you're like, I'm trying to let them do it. And then they're like, sure, have at it. And you're like, what the fuck was I thinking? I don't know.

[Mo Kiss]

Yes. I was like, no, I do not have the. I don't have the time.

[Tim Wilson]

It wasn't so much the time, it was more the energy and the mental capacity.

[Mo Kiss]

Right.

[Tim Wilson]

I mean it's like. Right.

[Mo Kiss]

Yeah, I do think you need to be careful.

[Val Croll]

I actually did do a lot of data work when I was on parental leave about timestamps since the last time my kid pooped, ate, slept.

[Julie Hoyer]

Yeah, that data collection, you know.

[Val Croll]

Data collection, exactly.

[Michael Helbling]

That's actually not a bad idea. Personal data, like, you know, doing things with your own data is kind of interesting.

[Julie Hoyer]

I mean, I didn't do anything with it, I just collected it. Because again, I overestimated that I would have time to do that and then.

[Tim Wilson]

Collected it just in case. Just in case you needed it.

[Val Croll]

Ooh, Matt Gershoff burn.

[Tim Wilson]

Yeah.

[Michael Helbling]

Yeah, that's right.

[Julie Hoyer]

You know what? I have a really nice one year summary though, that I was like, wow, I feel really accomplished. All the, you know, all the things I did collect just for that little pat on the back, I was like, worth it.

[Tim Wilson]

The decision making at the margins, that did not happen.

[Julie Hoyer]

Yeah. You know, or when you're just sleep deprived, you're like, when's the last time they did X, Y or Z? And you're like, oh, let me go to my app.

[Tim Wilson]

By the time you have your fourth kid, you're gonna be like, you know what, I might want to do a bears a comparison across all of the children. It could happen, you know.

[Julie Hoyer]

Yeah. Probably not four kids. Who has time for that?

[Michael Helbling]

And we don't mean to assume that the asker of this question was someone who's on parental leave or not.

[Val Croll]

It's a good example for modus stretches to think it not just about some of them.

[Michael Helbling]

Yeah, yeah.

[Val Croll]

What's good?

[Michael Helbling]

I like it. All right, next up, I'm mid career and pivoting into analytics. What skills do I need to focus on most? R. Ooh, yeah.

[Mo Kiss]

No, I think, I think SQL's number one.

[Michael Helbling]

I mean, a lot of this depends on where you're coming from.

[Julie Hoyer]

Yeah.

[Michael Helbling]

Because there's some real complementary career paths that flow into analytics really nicely. So like people from finance or things like that. And so depending on where you're coming from might do that. I think SQL is a great answer. Less so. R. But I get where you come from, Tim. But I think the thing that I've observed about people who make those transitions is how can you leverage where you're coming from into where you're going? And so in other words, you've learned a lot of very, very valuable skills. And actually creating a lateral bridge of leveraging those skills into your analytics career is actually a huge resource for you that you might not realize. And so that's what I'd first encourage you to do is just think a lot about and meet with people who are in the analytics field you're looking to get into and collaborate on thinking, like, what skills have I learned that serve me well? And then use that to build out a skill map of like, okay, now here's where I've got things I need to learn versus where things I've learned. Now I can translate those over and I think that'll really help you realize that maybe you're not as far off from this career switch as you thought.

[Julie Hoyer]

I would say to Communication skills as an analyst, like, I run into a lot where people aren't even thinking of, like, how are you going to make sure you document the process of pulling the data to answer the question? Or did you even document the question being asked? It's like some of that more hidden part of the process. But I do feel like as an analyst, like, you have to own that you have things like you have a clear process, you have documentation, you can go back to it, you can communicate what you did, you can communicate the assumptions you made or like, based on the question being asked. Did you take a pivot on the question halfway through and you didn't realize it? I think there's a lot of those quote unquote, softer skills or like, more hidden skills. But because when you get to the point of having to communicate the work you did and like present back to people, I feel like I spend a lot of time coaching people in that area and I don't know, depending on, like you said, Michael, depending on where you're coming from. If that wasn't a normal part of your role, like, it does take a lot to be able to make a deck, a short one to like give a readout from how much are you going to share? What kind of data visualizations are you going to use? Like, there are some basic tools you need, I feel like, to be an analyst that can function well with your stakeholders.

[Tim Wilson]

There are the skills that you're not really going to understand why they're so critical until they bite you in the ass a couple times. Like the documenting your process, you know, documenting your assumptions, writing out a plan, that sort of thing. And then I think there are skills that are relatively straightforward that I think are critical. Because Julie, as you were saying, communicating, I mean, I know I have a little axe to grind about data visualization, putting that even separate from data storytelling. Like, you can read one book, you can read Cole Na Affleck or Alberto Cairo or Steven Few, read one book and it will like, change the way you're thinking. Now, Michael, to your point, if they're already somewhere where they're like, no, I'm already doing this stuff all the time and I already know these principles. If you don't know some principles of data visualization, then I would think that because it's kind of quick, it gets you to a base level very, very quickly is one of those things. The other part is going to be like, very situational. Like, well, you're. If you're pivoting into analytics and you have a role Then you need to also include in your sort of skill map of like what's most needed here. I need to talk to my peers and talk to others and figure out which are like the biggest gaps that I can fill with some education, which are things that are going to take a little longer, but I should be riding along to do them. If it's, I'm just going to pivot generally into analytics and it's very, very broadly defined. That's really, really hard. Like, I, I look at the, the analytics subreddit where they're sometimes people are like, what the hell? Just, you know, I got this job, I thought I was going to be doing X, instead I'm doing Y. Is this normal? And it's like, no, buddy, it's not normal. Nothing's normal. Everybody hits that in some way. Like if you're expecting some tight little list of this is the definition of what this role is. It's not. You can't learn it all. You can't learn it all now. So you're going to have to find a way to focus.

[Michael Helbling]

Yeah, definitely read a data visualization book and then realize every reporting tool you interact with isn't going to let you do the thing you want.

[Julie Hoyer]

And then learn R to Tim's point, right?

[Michael Helbling]

Yeah. Because then you can, yeah, maybe to.

[Julie Hoyer]

Focus on the business you're going into because maybe you have a lot of context moving into analytics, but if you don't, like you're making a jump and it's a new business, I would say focus on learning the business process, what they care about because that context will help you decide what you actually have to learn about the analytics like execution part. But you can stay grounded more in what's going to matter instead of getting sidetracked.

[Val Croll]

Yeah, and I think that's the only other thing I had in my head besides some of the awesome tips you guys had to share is to think about some of the nuances of where you practice analytics and understand what you might gravitate more naturally towards, like based on some of your strengths or the things that you've enjoyed in past roles. Because it can look very different depending on where you're at, if it's consulting or in house, just as, you know, lots of different flavors.

[Mo Kiss]

And last bonus tip, learn what a McKinsey headline is or an insights headline. Like whether you're using it in an email as like paragraph titles, whether you're using it in a presentation, whether you're using it as like the headline for like a write up. You're doing learn what they are like, your life will be phenomenally easier and people will understand what you're saying or what the team are saying. It is hands down one of the most important skills, I think. And it's like a little bonus one that most people don't think about.

[Tim Wilson]

It's bonkers if you search for not doing it right now. The last time was a couple years ago where I'm like, surely if I Search for McKinsey title, there'll be like a million posts. And like, there aren't. But it's like, this is so simple. So if you search and you're not finding it, it's literally. Yeah, it's crazy. It's like buried in little niche places.

[Mo Kiss]

Where maybe I should write a blog on it. Another thing on my to do list, I won't do.

[Tim Wilson]

Yeah, I mean, if somebody doesn't know what it is and they're like, well, tell me what it is. Just, it's making it a declarative statement. Like, make your title. Don't put, you know, results from last quarter. Make it a declarative statement at the beginning of a paragraph at the top of a slide. Once you start doing it, you can't unsee how ineffective it is when people.

[Michael Helbling]

Don'T do it and you feel it right away in terms of impact. I'm sitting here thinking, can Generative AI make these for me? I don't know. Yeah, probably not very good ones, but.

[Tim Wilson]

And if you struggle to make one, then maybe you don't have a point.

[Julie Hoyer]

That's actually interesting too, Michael, on the AI, because if you just had a slide with the data visualization, it kind of proves the point of, like, what would anyone else take away from the data? Like, you're supposed to use the McKinsey title to tell them what to focus on. And, like, the slide is there for them to validate your title. So it would be funny to say, this is what I would say, and then show it to AI and be like, what the heck would they say from this slide? Because it might be really different.

[Michael Helbling]

Oh, quality.

[Tim Wilson]

Although there could be a feedback loop where you keep refining your visualization in the way that you present it until. Oh, until the AI actually says it. Then you've got torture it. Reinforcement. Yeah. It's been a while since we've said McKinsey titles on here. It's a good one.

[Michael Helbling]

I think we gave more than they bargained for on that one.

[Val Croll]

Yeah.

[Tim Wilson]

What skills? Just learn one. Just learn this one thing. That's right.

[Michael Helbling]

Yeah. Oh, yeah. Probably pivot tables. Learn pivot tables. All right, next up. I've been a data analyst, but now I'm trying to move into data science. How can I best do that? My advice, don't.

[Tim Wilson]

No, I'm just kidding.

[Julie Hoyer]

I wish I could follow up and ask them what is drawing them to data science. How are they describing these two roles?

[Tim Wilson]

The pay differential, I mean that was absolutely me seven or eight years ago because I thought the industry was going that way. Now I feel like data science is almost. It's gotten so liberally spread out to anyone in any kind of role that is mildly technical. I don't know. Mo, do you feel like the data science role at CANVA is reasonably tightly defined? No. Okay.

[Mo Kiss]

No. And it actually is a huge problem because we actually were all called data analysts and we also had business intelligence analysts and we made the decision to call everyone data scientists. And one of the things that happens through like the performance review cycle is there is an upweighting of people say that build tooling or work on experimentation or create models, that sort of thing. And one of the things that has actually come up that's kind of a conflict is we have some very incredible people who answer the most complicated business questions out there. But it is what you would describe as like a typical data analyst role. And sometimes like it, it doesn't fit because our definition of data science is not actually representative of the skills we have. And so it's a point of tension that we're trying to work through at the moment of like, how do we accurately reflect. Reflect what very good performance is when your skills are more on like the business acumen or commercial side than they are on the like hardcore data science side.

[Michael Helbling]

I mean, certainly from an analyst perspective, shifting in toward data science, obviously you need to pick up and learn a programming language of a couple of kinds, probably Python, maybe R, maybe both, because those are going to be critical. But I think, I mean, to your point, Mo, like there's not seem to be like the only thing I see a lot of times in organizations where there's a sort of distinction between here's our analytics team and here's our data science team is the data science team is typically working on broader longevity types of business questions, whereas the analytics team is working on more short term, more like observational and or day to day business questions. And I don't know if I agree. Like I don't personally think that's a great way to organize around data and analytics necessarily. So in a certain sense, like I struggle with the fundamental underpinning of where that lives. Because as analysts, we should be looking to tackle all along that and building our skill sets up to go into things like complex experimentation and segmentation and cohorts and those kinds of things. And you build your skills up and at some point you tip over into becoming a data scientist instead of an analyst. I don't know, somebody probably has an answer to that question, but I feel like the frontier is pretty vague.

[Val Croll]

I will agree with Tim that like five, seven years ago that it did feel like the way to like future proof ourselves, to make sure that we stayed relevant, stay on top of our skills. But I will say that what I personally have noticed a lot more of as a way to future proof yourself is to broaden your definition of the analytics that you work with or the data sets that you'll touch. And so you're able to answer more complex business questions if you're more rooted in the data that runs your business. And so I also agree with Julie that I wish we understood a little bit of that inspiration because, you know, hey, fair point, like if you really want that pay bump, pay increase, like different things motivate different people. But it does seem like, especially if you're in house, that there would be a way to like weasel your way into certain projects that have some of these skills required or some of the projects that data science team is working on. And I think there was like a couple episodes that we've done in the past that we can link in the show notes that have some great examples of ways that you can start to build the skills or to gain those experiences that really excite you. Because again, if you're drawn to or pulling yourself in the direction of the things that you're most interested in about the data science world, that's what's going to make it the easiest, most fun, the most rewarding, the most engaging.

[Tim Wilson]

That is reminding me. And Robert Petkovic listens to this. He will reset his timer on reminding me of this phase in my life for another four years. But I went for like, oh, I'm going to become a data scientist pretty quickly. Said, well, maybe I'm just going to be more data sciencey was kind of my shorthand because I was figuring out in a semi public way, like, what does this mean? And I think ultimately where I landed was I'm expanding my perspective and skills as an analyst. Like absolutely thrilled that I learned R. If I went back to previous me, maybe I would have tackled Python instead. Can I, without even looking at documentation Run even a simple linear regression in R? No. So I use it for all sorts of stuff that's useful. Same thing with Python. I've gotten to where I can kind of scan it and read it. I would consider myself still very much an analyst. The things I've picked up along the way, or a better understanding of some of the deeper principles and capabilities and ways of working. But I think I gave up pretty early on of like trying to reach. But that makes it sound like it is a progression. So I don't know that you just sort of tip over into being a data scientist. I think Cassie Kozarkov would say she doesn't say data scientist. So she's like the analyst and the statistician or the analyst and the decision scientist. And it's along the lines, I think Michael, where you were was the analyst is a little quicker hit. They're finding things or doing explorations. They may be doing a linear regression, they may be doing regression, they may be doing other things to say, is there something here? The statistician says, okay, now we're going to put the real rigor on it. It could be we're building a model that's going to be productionized. That's often a data science role. It could also be, no, we're going to build a model, we're going to appropriately split the data set into training and a testing thing. You get one shot at it. So there is a higher level of the question being answered. You have fewer shots, there's more rigor, it takes a little more effort. But you are trying to get to something that you can really move forward with. Whereas I think she looks at it as the analysts, a lot of times are kind of inspiration for those smaller things that are the select number of things that then bubble up.

[Michael Helbling]

Yeah.

[Mo Kiss]

It's funny, when we came to this question, the first person I thought of was Cassie, but not because of her definition split. It actually comes down to her, like, just be useful thing.

[Tim Wilson]

Yeah.

[Mo Kiss]

Which definitely comes top of mind. And I guess one of the things I think you really need to think about is your motivation. I feel like there are too many people that are like, I want to do data science stuff because I want to do the cool, sexy, fun stuff. Like, I've had people literally come to me and be like, I don't want to do the like 10% boring bit. I just want my job to be all of the fun, sexy stuff. And I'm like, that's not realistic. Like, that is not a thing. And so I think you Just need to make sure that it's not like you're chasing the shiny thing. It's like if you were genuinely more interested in that work, that's cool, like that's not a problem. But like you really need to interrogate your why of why you want to make that move.

[Julie Hoyer]

Can I just say a little tactical too, like depending on what data science and data analysts mean in your organization, like try to partner and work with a data scientist. I know I get to do that at my company. And so the way we interact, I'm able to bring some context maybe, but I'm also able to learn from them, like the rigor of the different methodologies and tell them like, here's the business problem, you know, here's what I'm really trying to focus in on. Here's maybe what I hypothesize is happening to best answer this though, like I was thinking, maybe I use, you know, this type of regression or whatever it may be and then I can have a conversation with them where they are bringing in that deeper knowledge of what are the tools we could use here. So maybe even just thinking, can you do that to help you in your transition? Because those are the people if you're wanting to stay in that organization, like they're going to highlight to you where you have the skill gaps too to focus in on. And then to Mo's point, like you can decide, is this really what I want my role to look like and grow into? So the shadowing or like partnering would be my biggest tip.

[Michael Helbling]

Yeah, I'd also throw out there that I feel like data science as a profession is more impacted by economic cycles than an analyst typically is. And so you'll find potentially in the job market more or less opportunities depending on sort of where the economy is going in the cycle of the economy. Certainly a lot of people I've told talked to in the last year, year and a half who've been looking for data science roles have definitely noticed some shift in the uncertainty in the marketplace has caused companies to pull back on data science hiring a little bit. And it kind of goes to some of what we've been talking about. You know, when a company is more uncertain, they take shorter swings at bigger deals and so they're pulling their risk perspective back. And so the big analysis or segmentation or pricing strategy, the long term project they might have taken on before, they'll now put on hold. Whereas they still need to run the business day to day, find opportunities and do the analysis around those things. So you might Find that as well. And so that's the other thing. If you're not finding opportunities in the job market to transition into data science roles, it may require some patience. It's not you. It might just be the cycle of the economy that we're in. So hopefully somewhat encouraging. Don't feel bad if it's sort of like, man, I've been trying to be a data scientist and no one will hire me. It might not be you. All right, great. Let's jump into a new question. So this is actually one where we've actually done some topics around this, but I think it's ever present. And so let's jump into it. I'm at a point where I need to choose whether to pivot from being an individual contributor to a management role. What should I do?

[Tim Wilson]

That's the listener question. You're not asking.

[Michael Helbling]

Yeah, that's the listener question.

[Tim Wilson]

Yeah.

[Val Croll]

Michael Schmelwing.

[Michael Helbling]

That's right. Pay no attention to who asked the question. Just answer it, Tim, for gosh sake.

[Val Croll]

Sorry.

[Mo Kiss]

Is the question, what should I do? Should I pivot to being a manager?

[Tim Wilson]

No, I think it's whether or not.

[Mo Kiss]

Yeah.

[Tim Wilson]

Should I?

[Michael Helbling]

Yeah.

[Val Croll]

At a point where I have to choose.

[Michael Helbling]

I mean, I don't think we're gonna get to a good answer, dear listener, about what you should do.

[Mo Kiss]

No, I actually think we can.

[Tim Wilson]

You know what that means, Michael? Let's do it. You're an IC material. MO is management material.

[Michael Helbling]

Oh, well, that goes without saying. Absolutely agree.

[Mo Kiss]

I think it comes down to, like, do you want to be the one doing the work or the thing? Actually, that is probably the biggest determiner for me. I was interviewing someone a couple weeks back and I said to them, like, because it's a person who actually is an IC at the moment, and we were considering them for, like, what we call a coach's role or a manager role. And I was like, what's made you want to go back? And he's like, I actually just love developing people. I love coaching a team. I love helping them achieve, like, their goals, work through problems. Like, yes, I'm an amazing ic, but that's not the thing that excites me the most. So I think it actually comes down to, like, what excites you the most? Is it the doing the analysis and, like, giving the business the answer and having less meetings and being in the weeds and knowing the data really well? Or is the bit that really excites you the, like, helping a junior person learn how to do their first presentation or explain a technical Methodology really well to a stakeholder or like, I mean, obviously management also comes with all of the stakeholder stuff, which is a whole nother separate thing. But I think you need to figure out which bit you're most passionate about. And I'm obviously very passionate about the team stuff, much more than I am about the doing the work myself. Although sometimes I still get kind of sucked back in because like it is a bit of a high. Right. Like we would work data, we didn't love that high of like, oh, look at this amazing analysis. But like the reality also is if you're a person who someone once said to me, they're like, oh, they were in their first management role. They're like, this feels like a lot of politics. And I'm like, it's not politics. It's about figuring out the best way to communicate something that you help the company make the best decision possible with the information that you have that might feel political to you. But that's not how I describe it. So if you don't have the stomach for that. Also probably not a manager type person.

[Tim Wilson]

People who note politics, I, I'm like, that's either. They're either very cynical and they're gonna, they need to adjust their perspective, which I think you just did a great way to kind of reframe that. It's possible they're at a company that actually has. If it's that dysfunctional. I mean, not that there isn't some level of some aspect of gamesmanship, but it's. If that's your framing, like you're. Whether it, it is politics or whether you're perceiving it as politics. Yeah, you're not gonna, you're not gonna work out well in either one because I don't think you want to work long term unless you, you know, are a direct descendant of Machiavelli. I think it's really tough. Like I know now deeply and have known for years that I am an IC for life. Doesn't mean I don't like collaborating people, doesn't mean I don't like coaching and providing some support and guidance. But I don't know that I would know that as deeply as I do if I had not been in a management role. So I could see, I don't know that I've seen this happen. I don't think you really know until you try it. So if there's an opportunity to say, put me in a role that is somewhat supervisory or managerially, but I am going to put a 12 month timeline on it and somebody is going to hold us accountable to have a discussion about whether I want to continue with this or whether I want to go kind of back ic because there are people who, a year in, they're like, this is amazing. I know I will be satisfied for the next decade of my career because I'm getting so much energy from this. And there will be people who say, I'm waking up every day and spending the first seven hours of my day getting to the last hour when I can go and actually just do some work that I'm really excited about. But I think it's hard to know. So the extent that you can, like, try it out in a way that you don't feel like, I mean, you're never committing. You're never like, oh, if I go down this track, there's no going back. Because it's not a going back. It's, you know, taking a step over to the, to the lateral path of continuing as a, as an ic.

[Mo Kiss]

And I have two people on my team that just did that, that we're leading teams, and then we've had a conversation and they've gone back to both being senior ICs, and they're absolutely killing it.

[Val Croll]

I also think that just because you tried at one place, like, also keep in mind the context, like, you might be in a position to give it a try later because maybe there's something about that context that makes it unappetizing. But don't close it forever because I totally agree with Tim. You're not pouring concrete. Whether you switch roles at your current company or switch to a different organization, it's all fluid. But the one thing I will say, you never know until you try it. But Mo, I thought you made a really good point when you were saying, do you really like explaining things to stakeholders that's really complex or training or coaching analyst? So you think like, oh, I've never been a manager before, but think about some of the elements that would become more central to your role because there's. It's likely that you've actually dabbled in it. Like, maybe you had an intern working for you last summer. Did you enjoy that experience? And did you enjoy when they saw success? And some of those things to give you, like, that little inkling. But the one thing that I will say that I hear a lot when this, like, divergent path conversation comes up is the only way to move up is if I manage a team. And I will say that I, at least in the past couple years, I feel like so many organizations have given a more senior path to success on the IC side. So if you've hit a ceiling at your current organization and you really feel pulled to that IC side, maybe it is time to look outside your organization. Because there is absolutely a path, you know, staying IC where you can move up in seniority and your scope and the type of decisions that you can be helping influence within the business. So just want to make sure that that gets out there too.

[Tim Wilson]

That's a great point.

[Mo Kiss]

Plus one move companies if there's no path to seniority in ic because like we have an IC track and a coaching track and like you can get the same pay or the same like benefits of being a senior person and your complexity definitely grows with that as well or the problems that you're solving. But yeah, like then if that's the route that you want to take, move to a company that will have a path for you as an IC.

[Tim Wilson]

That's right. Canva.comjobs/mo Kiss ReferralCode yeah, it's also, I think really important.

[Michael Helbling]

I think we've sort of touched on it to just recognize the complete difference in skill sets required. Right. So managing your own efforts versus starting to take on and managing the efforts of others. So if you transition into a management role, you should expect a lot of learning and building of skills, which is by its nature confusing, disorienting and frustrating. And so I think that's the other thing is give yourself time to grow as a leader, as a manager. If you take that on and don't necessarily say, oh, in my first year I was, it was rough, I felt disoriented, throw it away. It could be as you kind of evaluate both kind of your personal skill sets, desires, those kinds of things in your personality also factor in. It may take you a couple years to really start to build the skill sets necessary to do that. A lot of times when I've led teams, I'll say managing people is not for everyone and you should work really hard. You deserve it and your people deserve great leaders. And so don't just jump into a management role if you know that it's not right for you or you know, you don't actually want to do it, you'll hate it, your people won't be benefiting from it and so nobody benefits. And so it's tricky. But also if you do decide to go into management, make sure to give yourself time to grow in and learn the skills because it's nothing like what you've done historically.

[Tim Wilson]

Do you think though after, I mean I pulled a year out of my ass. But do you feel like after a year you'd be like, oh, this is. I'm getting fulfillment. I just have skills to develop and I should continue versus.

[Michael Helbling]

Yeah, for myself. And again, I would rate myself as somewhat slow learner. I started to feel like I was doing a decent job as a manager probably about three years in. And those first two years I was definitely struggling and learning. But I feel like you're.

[Tim Wilson]

Then I started reporting to you and I tried to, I tried to undo it all.

[Michael Helbling]

That, yeah, that was kind of the nail in the coffin where I was like, that's it, I'm out. Like, I don't want to work with nobody anymore.

[Julie Hoyer]

I. I think a good question to ask yourself too, or something to research in your decision is understanding what does adding value look like to the company? Because it's a very different mindset. Like, I think as an individual contributor you're very used to having a lot of control and you have a very like, physical output to say, this is my value to hang my hat on. And I've experienced a little bit of this and had some conversations around it. Like, it's a big mind shift and a new mindset to have when your value add is like influencing others or letting them have that like very physical success of a deliverable or something or hands on keyboards. And so sometimes you can feel like, what am I doing? I was busy all day in meetings, talking to people and things. And then you're like, I don't know what I have to quote, unquote, show for it. And so I think you have to be really honest with yourself of like, my expectations and value in this new role would be, you know, this whole list of things and not what I'm used to as an individual contributor. And that can be part of that pivot, I think, Michael, you're talking about. So maybe thinking through that early on would help you make the decision too of would I be fulfilled by trying to add that value instead of the individual contributor.

[Mo Kiss]

And the funny thing is, Julie, I have this a lot with people who are managers that are going for promotion because like, as part of the promotion pack that we put together, you have to explain your achievements. And people are like, I don't want to take credit for my team's work. And you're like, I'm not telling you to take credit for your team's work. What I am saying is describe the project and your role, which might have been like identifying the resources. Who in the team is going to work on it. It might have been getting the stakeholders or buy in. It might have been helping the team refine, like next steps and that sort of thing. And like your role, you did play a role in that project. You might not have been the technical lead. It's not taking credit for your team's work, but you just. The way that you get comfortable with describing your value is understanding that those things you're adding now may look different to what they look like before, but it was still very important in helping the team deliver that project successfully and.

[Val Croll]

Sharing the story of their wins and being able to put the spotlight on them and pull out those best parts, you know?

[Julie Hoyer]

Absolutely.

[Michael Helbling]

Yeah. I always say as a leader, your people's wins are your wins. All right, let's do one more before we wrap up. So let's. This one came in. When I'm interviewing for an analytics position, what are some of the questions I should be asking the interview that will help me determine if the position is one that I should take if I get offered it? Oh, that's a really great question.

[Mo Kiss]

One question that I got asked during an interview and it was for more of a leadership position in data, but it, I felt like it was such a solid question because the reality is when you get asked this as well, you're going to answer, well, I felt, my intuition said to answer it honestly, which was, how are decisions made at this company? And when you think about it, it's a very open, broad question, but you can go in so many directions in answering it. Right. But one is, is the company kind of data informed in their decision making? The other one is like, is there a particular specialty that leads decision making? So like it's our product managers or our engineers or our designers that like really drive decisions. Is it a founder led company and that's how decisions are made. Like you actually somehow kind of get the dirt on the company and like get a really good understanding of what you're going to have to navigate by asking that question. So that was one that I'm probably always going to ask now if I'm interviewing myself.

[Val Croll]

That's a really good one. I think that like throughout the interview process, like, hopefully you've gotten a sense for the culture based on the different people that you're talking with or what the responsibilities of the job will be. So if that's all taking place care of and you don't, you're solid there. I think making sure that two things that I've learned are very important to me is making sure that your manager is going to be there to be your support person. Slash like shit umbrella taker for the stuff that comes down from above. Right. So maybe you're going to ask some questions about when a difficult situation arises or when we're dealing with a budget cut, like how will we make sure that our most important projects are continue to say resourced or asking some little bit scenario based questions because making sure that you are in lockstep with your manager, like there's you can put up with your plus two you not really seeing eye to eye with or not getting along with. You can put up with some peers that aren't great, maybe even some direct reports that are thorn in your side. But if you're a manager doesn't if you don't feel like they're going to be someone who's like in your corner advocating for what you need to be successful, it's going to all fall apart. You could be working at the most inspiring, amazing company that's aligned with your missions and if that's broken, there's nothing that can repair that. And then I think the second thing too is if you sit down and write down a list of things that are really important to you in your role. One of the things that I learned doing that exercise most recently is it wasn't necessarily about the size of the.

[Tim Wilson]

Team or wait, hold on, wait. How recent? How recent? Is there something I need to know?

[Val Croll]

Cannot disclose. The last time I did this exercise, which was a couple years ago, that previously it was all about what is the remit or what team would I be supporting or what type of company or what is the size of the company. And the last time I made the exercise it was way more about do I feel inspired by the work that I'm doing, Do I feel like my manager supports me like the one I just touched upon? But again making sure that you understand like Mo said, how decisions are made, that's such a good one I think how the work gets done, how are we going to build our budget together to make sure that the resources that I have, whether it's teams, technology, the support cross functionally. It's just really digging into what's that going to feel like. So that you're going in as eyes wide open as you can. Because it's it really the nuances of must have 6 years of experience in whatever like throw that out the window. It's once you get there it's really more about like again the value delivery and the relationships you can build to do cool Shit. So I think that those are two things that I always probe on.

[Tim Wilson]

Is that second one a better one to try to get from the hiring manager or if you've got someone who will be a peer to ask them or is it.

[Val Croll]

Good point.

[Tim Wilson]

I mean, whoever seems like they're the straightest shooter.

[Val Croll]

Yeah, there you go.

[Michael Helbling]

Yeah.

[Val Croll]

Someone who's not feeding you the company line.

[Julie Hoyer]

I would ask too, trying to understand, like your support structure. So depending on the role you're going for, like if you're more middle of your career, let's say, and you still want to learn a lot while you're there, and you want mentorship or you really like collaborating with peers. I think asking questions about understanding the size of your team, who else is on the team? Maybe people that have similar knowledge sets to you so you could learn from them or trying to understand, like, does your team partner with other teams in the organization? So then you kind of go in knowing, like, are we really siloed and nobody talks to us and I'm really gonna have to create inroads or are there things in place and relationships I can lean on to make me better and ready to go at this role? Like, I think for me that's something that would be always top of mind to try to ask about.

[Mo Kiss]

I think, Julie, you're. I mean, this is like both Julian Val's points smushed together. But one of the things, like, I do actually also have criteria that I rank like, that I've worked out are important to me in a role and I rank them. And the last time I was job hunting, I had a few different opportunities and I was trying to figure out which one to take over a different one. And I basically scored the things that were important to me in like a matrix. And one of the things that was really important to me was people I could learn from. Now, I don't think that is necessarily something that you're going to be able to answer with a question, but sitting down before and knowing that was important to me. I walked out of one interview where they had asked me. They basically expected me to be able to rote learn our functions and describe them to them. And I was like, that's not me. When I use an R function, I'm going to look it up and understand how it works. That's where my level of R programming is. Another interview I was in, which was my CANVA interview, I had three people, we were working through some R code and one of the guys was like, have you ever used this function And I was like, no, I haven't. He's like, let me show you how it works. And I walked out of the interview being like, oh, I learned three new things in R today. If that's what it's going to be like being in this team every day, that's the team I want to be in. So it's not always necessarily having the perfect question, but it is spending the time and giving thought to what's important to you and how you're going to assess that through an interview process.

[Val Croll]

Very smart.

[Michael Helbling]

I think first and foremost you want to determine what the coffee situation is.

[Mo Kiss]

Oh, that is true.

[Michael Helbling]

If you don't have decent quality coffee provided by the company, do not work better.

[Tim Wilson]

But Michael, this is a remote position.

[Michael Helbling]

Oh well, that's a little trickier. Remote. You have some benefits of being remote.

[Tim Wilson]

Blah, blah. Anyways, no, I thought be a great question to ask.

[Michael Helbling]

I'm not sure you want to just observe that or just ask that. If you have a over lunch interview, that's a good time to bring up the coffee question. But you definitely should know, and I say it jokingly, but actually there's some seriousness behind behind that, which is little things like that will indicate level of investment. On a more serious topic, I would try to determine the disconnect between senior management and the analytics team that you're joining and where is it? And so asking questions of the management team, what is the biggest challenge that you're facing? And then comparing that to what the analytics team tells you will tell you how much disconnect there is between management and analytics. And that's a really, really key thing for you to know going in. The other thing I would try to understand is to what extent is the analytics organization trusted and brought in? Because if there are other parts of the organization spinning up their own analytics and data, you're walking into a sea of bullshit that you want to avoid, to be honest. So it's sort of like, oh yeah, well, the sales order kind of does their own thing over here because. Because they don't trust any of our data. Red flag, red flag, red flag. And maybe as a leader you're coming in to change that culture. But know what you're getting into and if you're an individual contributor, know that that's going to pull against your ability to do your job in a meaningful way in an organization that's going to be splitting itself up and having internecine battles between different groups of the organ, different parts of the organization. So those are two parts that I Would.

[Val Croll]

That's so good. And that last one, you should also be wary if it's like, oh, we have this centralized analytics team, but they never really give me what I want. So we have this role to just sit on our team. Like, that's the same red flag. It's just the other side.

[Michael Helbling]

You're getting hired to basically work against the organization, which has some benefits depending on how powerful the person is within the organization. But know you're walking into a war. And that means if you need a data warehouse set up, review set up, you may be going into enemy territory to work with the data engineering team to do all those things. So it's all these things. But anyways, hopefully that's helpful.

[Tim Wilson]

Apply for a job right now, Mr. Wilson. I'm ready to go. I've never been more equipped.

[Michael Helbling]

I'm feeling fired up right now. Yeah, I use these tips.

[Val Croll]

I knew that was going to be a really good one. I was excited about those answers.

[Michael Helbling]

This has been great. And again, huge shout out and thank you to our listeners for bringing those questions to us. Who? The listener survey. It's so great to hear from our listeners and get these questions. So really appreciate it and thank all of you, Julie, Tim, Val and Mo for your expertise and taking the time to answer some of these.

[Val Croll]

Thank you, Michael.

[Michael Helbling]

Oh, well, I was waiting for that. That was what I was waiting for. Thank you, Val.

[Tim Wilson]

Val. Read it.

[Julie Hoyer]

Right?

[Michael Helbling]

I feel like I threw the fishing rod out there and then just anyone?

[Val Croll]

Yeah. And also just a huge shout out to Ken.

[Michael Helbling]

Oh, Ken Riverside and the whole fourth floor production.

[Val Croll]

We'd be remiss if we didn't thank them.

[Michael Helbling]

Huge in the Chicago podcasting scene. All right, listen, obviously we also want to give a big thank you to Josh Cohurst, our producer. He does so much behind the scenes to make the show possible. So thank you, Josh. You know, as I look back on sort of this topic and some of the questions we answered, you know, the career journey that we all face as analytics professionals is. Is definitely complex, but I think there's more clarity now and more help now than there's ever been in this. And so hopefully you're feeling encouraged and hopeful about that. And we'd love to hear from you if you have more questions or more discussion. And there's great ways to do that. We've got a lot of presence on the measureslack chat group, as well as a lot LinkedIn page and our YouTube channel, which you go to YouTube and search for Analytics Power hour. You can find that. And please subscribe. We just got that up and running with all of our old episodes recently. So you can find episodes there on YouTube as well as on your favorite podcasting platform. So I know I can speak for all of my co hosts when I say, no matter what your role, no matter if your management or an individual contributor, contributor, wherever you're at in your career journey, keep analyzing.

[Tim Wilson]

Thanks for listening. Let's keep the conversation going with your.

[Michael Helbling]

Comments, suggestions and questions on Twitter @analyticshour.

[Tim Wilson]

On the web at analyticshour.IO, our LinkedIn group and the MeasuredChat Slack group. Music for the podcast by Josh Crowhurst. So smart guys wanted to fit in, so they made up a term called analytics.

[Val Croll]

Analytics don't work. Do the analytics say, go for it, no matter who's going for it.

[Mo Kiss]

So if you and I were on the field, the analytics say, go for it.

[Val Croll]

It's the stupidest, laziest, lamest thing I've ever heard. For reasoning in competition.

[Michael Helbling]

This podcast studio is costing me money. So let's get this show recorded now.

[Mo Kiss]

Yeah. Also, why are you in a podcast studio? Okay.

[Michael Helbling]

Because his house is still on fire and I have no Internet. I have no garage door openers. And yeah, my computer is half broken. My brand new microphone is dead.

[Tim Wilson]

Georgia. Georgia's infrastructure. Yeah.

[Michael Helbling]

I have a feeling like my house is some sort of like, electric Faraday cage thing where it's like, just hit me with lightning all the time. Wires.

[Mo Kiss]

Can you put some, like, giant trees next to the house?

[Michael Helbling]

Giant trees? There's trees all around my house. They. And they hit my house.

[Val Croll]

And your ear.

[Michael Helbling]

And my ear. My mother ear. How's that for an outbreak?

[Mo Kiss]

Josh?

[Val Croll]

I never got confirmation from Tim on my eyes, so just crossed my fingers.

[Mo Kiss]

Val and Julie, both of your eyes are better now. Like after Tim gave you that feedback.

[Val Croll]

Yeah, I do it during internal meetings.

[Mo Kiss]

Good.

[Julie Hoyer]

Yeah. Me and my wonky eyes.

[Val Croll]

Facts and feelings. And your eyes and 2/3.

[Tim Wilson]

Type A, Type A personality. Oh, I didn't even put any conditional formatting. After you change it.

[Michael Helbling]

To what.

[Julie Hoyer]

Conditional format? Did you manually color those?

[Val Croll]

Did you get hit by lightning?

[Tim Wilson]

One of us did. No. You know, people get Michael and me mixed up a lot. Who really isn't?

[Michael Helbling]

I'm gonna bring a bunch of eggshells and just scatter them around your feet so people have to watch on eggshells around you.

[Tim Wilson]

Rock Flag and McKenzie titles.