What Your Manager Actually Needs From You (A Director Tells All)

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Welcome to The Quietly Ambitious Analyst, a podcast about data,
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communication and business. I'm Seth Forbes, and in each episode
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will unpack what it really takes to influence decisions with data,
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from framing business problems to communicating insights with confidence. Because
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data alone doesn't drive change, people do. Welcome back everyone
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to another episode of The Quietly Ambitious Analyst podcast. Today
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we have Aaron Wilkerson who has over eighteen years of
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experience in data and analytics. He specializes in building and
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driving data strategies that help companies leverage their data assets.
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Aaron lives in Livonia, Michigan, with his wife and their
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five children. So Erin, thank you so much for joining
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the podcast. It's a pleasure to have you here today.
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Absolutely, thank you, Seth for having me.
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I would love to kind of get into your own,
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I guess career journey and kind of reflecting on what
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you've learned over the years. What is the driving force
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for you as a data professional.
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I think it's just a natural curiosity, always looking to
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problem solve and figure things out. And so that's what
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things I do like about our field is we do
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work across many different business functions. There's also interesting problems
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because new technology stacks, you have, different stakeholders come in
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into play. There's always new and latest tools. So at
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one point cloud was a big thing, you know, almost
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twenty years ago, and then you got data minorization with
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lake House and data bricks and all that, and now
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you have AI.
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So I'll say there's.
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Never a dull moment, and everything changes, but it's always
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interesting problems to solve and service to the business, you know,
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just always trying to give them interesting, interesting things that
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they need to make decisions and figure out the best
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course of action for the business.
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So I love that you're saying curiosity is driving factor.
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I am also curious when you were a kid, were
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there any traits that you kind of look back now
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and you're like, oh, that makes sense now that I'm
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in data.
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Yeah, probably analytical thinking, you know, like taking things apart.
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We're curious about curiosity about how things work, and you know,
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trying to figure out things, you know, taking taking apart
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toys and remote controls and things like that, just natural curiosity.
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Because I do think that this is a field.
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Where there are no kind of right answers, is very
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kind of solution driven. Analytical there's no set step of
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go like from step as that B. There are some
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things you need to figure out, especially with leadership, like
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as I've taken on leadership the past ten years. That's
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it's a whole entirely different new challenge. But I think
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kind of kind of that like curiosity, ethical thinking, problem solving.
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You know, I really like math. I was good at math.
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You know, math has kind of natural stepping points around
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how to use all problems on how do you figure
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things out? Yes, I mean like data was kind of
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a career I just kind of fell upon. It wasn't
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like a childhood dream of you know, becoming you know,
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a data leader, like I think at that point I
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didn't even know what data was, whereas most people didn't
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know what that was. So I think it just kind
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of just curiosity and just gotten lucky getting into this
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field of being they able to stay for so long.
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You were saying that in data, there's really no such
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thing as a right answer.
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Can would you mind expanding on that a little bit more?
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Yeah, it's.
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Because it kind of all depends on the context of.
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What you're trying to do. So if someone says to.
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You, you know, I'm like we were inundated with tools
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and that you know in our in the work that
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we do. So it's kind of like these things could work.
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So it's kind of like does it work for your business?
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You know, you always have budget constraints, you know, a leader,
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you have team composition, like if you have an account,
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what do you do with that? From a team, like
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what's the best use of resources for the company. So
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it's it's like trying to figure out approach and selling
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people on that approach of Hey, this is what we built.
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And you know, there's different data models, like there's a
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data you can do, a relational data model, there's dimensional
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data models. There's graphs that we have too, So how
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do you and choose that? So there's there's a lot
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of different choices. There's no set like this is the
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one way to do it. There's variability and different different
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ways to come to a solution and having to stand
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by that and say I think this is the best
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course of action.
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You know, that makes a lot of sense and I
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have experienced that as well.
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Just being a data analyst.
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One analyst could find an insight that is slightly different
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from an insight that another data analyst may find if
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they're looking at the same data set, or what stands
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out to one person may look different for another. And
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then on top of that, like structuring the story and
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the way that you present it to stakeholders, you know,
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could be variable depending on who it is that you're
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working with and what they care about.
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Yes, exactly, especially open into questions like you know, what's
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the reason this happened or why did this happen? Or
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what do we think's going to happen? Or what would
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you recommend? Like those are all those open ended questions
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that make you challenge. Especially when you're young your career,
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you kind of you challenge yourself like okay, well what's
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the best I don't know? Or who can I ask?
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Or who's the best person to check with? Or if
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you're a team on one person, like you really are
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kind of thinking like okay, who I need who do
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I need to validate this with? But definitely, you know,
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it's the way you go to school. There's straight answers, right,
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it's fine, solve for X equels to you're trying to
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solve for that, or you're a science class you're trying
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to do some chemistry problems. So there's very straight forward
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answers when you're in school, but as you evolved, you
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realize that it always is, it depends, it depends, it depends.
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So definitely it's a you know, a difference in like
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formal education versus once you get into the real world.
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Tell us about kind of where you're at in your
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career right now, because you've been in this field for
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some time.
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Yes, yeah, it's kind of a it's a it's a
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mixed bag. I don't want to have started my career
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as more individual contributor, kind of back in business intelligence
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reporting administrators, so more technical platform base, and then I
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got some opportunities to work in ETL so database development,
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and then I got into leadership. So I've been in
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leadership for about going on twelve years now, so it
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is in the twenty fourteen different levels leadership, so kind
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of a manager, senior manager, now in a.
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In a director role.
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So now in kind of the senior leadership realm of
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how do you create like sense, we take a team,
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create a strategy, leverage that strategy to benefit the business.
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Get to sell ideas. You have to sell strategy.
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You have to connect the business to your team to
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figure out Okay, the business is struggling with this, how
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does our team best help with that? So I'll say
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that's a day to day But also on the side,
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I do lot of networking. So I co founded a
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local data networking group called Data and the D. The
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industry keeps changing. Now we're talking about this thing called
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AI and a lot of us are figuring that out.
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So I think that's the new part of my career
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is kind of the networking part of being interesting people learning,
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asking questions and just trying to vet things out and
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kind of even course correct and challenge myself on things
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because I realized is now, once you work in a
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company for a long time, you have a set way
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of thinking about things, but kind of talking to other
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people and writing things on LinkedIn and having DM conversations
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or things like this or conferences, those really do challenge
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you're thinking, help you think about there may be different
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ways to do things.
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How can we do this at our company?
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So yeah, say kind of leadership and thought leadership and
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networking are kind of the current phase of my career.
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You've really built up an incredible career for yourself. You're
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a director. Now, how long have you been at Carhart.
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Three and a half.
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Years, so number of twenty twenty two, so it's in
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about three and a half years, which is time flies
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for sure.
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I think it's amazing that you also built up this
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community for yourself. And I also read an article about
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this the other day. I wish I could figure out
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where it was, but they were saying that once you're
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in a role for a certain number of years, eventually
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you kind of stop learning new things and then you
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become as knowledgeable as a new person who is entering
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the role. And that just kind of blew my mind
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of just how important it is to be continuing to
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put yourself out there, speak with people, get new ideas,
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and that kind of thing.
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Yeah, because a lot of that is, you know, we're human,
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so I may have a skill set, so I go
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into a company, I have ideas, and because also companies
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will only let you allow you to do certain things,
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so I may have a way I want to do things,
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but you're also kind of limited to either a tech
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stack or change management can be tough with the company,
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depending what business you work with. So you kind of
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get to that comfortable phase where you feel very comfortable. Right, Hey,
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I know that I know my daily job is going
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to be I know the kind of the things I
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want to work on that may change a little bit,
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but you do kind of get a little bit comfortable
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after a couple of years, and then that's where I
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think you need to you need those outside forces to
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challenge you because I found out you get too complacent
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because I think about the company has always changed, the
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company and the business always moving. So if you become stagnant,
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then you become a liability or bottleneck for the company.
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And as you're seeing through like LinkedIn and different things
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out there, like people are losing their jobs because the company.
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You're like, what do we doing my AI, how are
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we moving our company for?
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But if you're saying, well, we're doing the exact same
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thing we did years ago, I think it's not like
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those things don't fly. Companies are looking to improve. You
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have new leadership that comes in all the time and
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they want new ideas. So making sure they have new
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ideas and not saying stagnant I think is a huge career.
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Lesson. I would get it to people.
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Just making sure that you're saying on top of things
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because you're if you're leader comes to you and say
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well this AI thing you said, well, I don't know,
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I've never heard of it. Like, that's not a good answer. You,
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like all of us, need to be prepared to talk
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about what's going on and why. You may think that
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it's not the best suitable plan for the company right now.
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You need to defend that and say, well, based off
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these things, I don't think that we should go this route. However,
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to make improvements, we should go this route instead. But
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just saying well I'm not hired to do that or
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I just do this, it's not going to serve you
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well long term.
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Do you feel like companies are like creating space for innovation?
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Yeah.
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I think part of that is figuring out the DNA
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right for Carhart, I think we stick true to our
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mission statement, but there's ways to be innovative around that,
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And a lot of the innovation is is from the
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team members. It's bringing ideas to your leader. I think
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there's a cultural aspect of it, but I think it's hard.
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People don't really say no, it's really just no one
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has brought up the idea for us. So I would
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say to the listeners it's you know, if you have ideas,
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bring them to your leader, but not just an idea,
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but it's a full Okay, I think we should do
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this for these reasons.
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Here should be involved that.
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So I think it's a hey, I think we should
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move to this platform for these reasons. It'll cost this,
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but I think having ideas because sometimes innovation people think
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is just like I just the ideas at the wall.
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But I think it's fully like, what is the idea,
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Why is it beneficial? Why should we go this route
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has a couple of benefit from it. I think innovation
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is there, but it's you have to be the champion
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of that. If you're just waiting for your leader to
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spark innovation, I think that's not I mean, sure your
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leaders should be doing that, but in the event they're not,
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then you should be the person that just brings ideas
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and kind of identify different opportunities. I mean, it's also
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a way to advance your career. Is kind of helping to.
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Occurs change and kind of be a change agent.
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Makes it a lot easier for someone to make a
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choice when they've got more context.
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Yeah, I mean I think about in your friend group.
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I using an analogy of imagining your friend chat. Someone
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just says Hey, guys, we should we should go out
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to dinner, and everyone says we should, but what does
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that mean? What you really want to see someone say
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is hey, we should go out to you know, Narrobread
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on Friday at six o'clock.
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For like, it's the.
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Actual laying out the idea versuses we should out to dinner,
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or we should go see a concert, or to go
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see a movie.
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Okay, which movie? What night? Like?
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So I think that's the difference between just throwing out
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ideas versus truly let being like, hey, let's actually do
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something new that we haven't done in a while, or
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trying to encourage interactions. It's a full idea versus just
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an abstract concept of this thing. You're like, Okay, that's
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not gonna happen, even for us. I mean for my
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networking group. We had a conference. So we did our
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first ever conference last year November. That start off as
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just an idea in March of we should do a conference,
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and then it was a Okay, people thought it was
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a good idea. So let's get an event planner, let's
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figure out speakers, we need to put together a sponsor package,
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we need to secure a venue, we need to open
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up a bank account.
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So I think I think it's good.
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It's good to have ideas, but then it's say, Okay,
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who's taking the next steps to move this forward and
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kind of make it something. So I think it's part
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of innovation that I think people lose sight on is
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just you have to continue to make steps on the
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day by day basis, is not to starting an idea
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and just hoping that you know something's going to work out.
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I have felt it before where it's like you've got
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really smart people with great ideas and they want to
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implement something, but they just they don't really know how
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to take that across the finish line.
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The tricky part is about to share those stories. So
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in many cases, I think people are doing things, but
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it's kind of hidden behind closed doors. They don't hear
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about it. So I think I'm definitely a current people
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to kind of share what they're doing. But I'll say,
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you know, platforms, architecture, you know, AI is really changing
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a lot of things in terms of you hearing about
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pair programming, So I'm I don't think AI is going
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to replace programmers, but I have heard things about AI
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being able to help out with that. You know eighty
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twenty rule where they can do eighty percent. You can
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do the tweaking of it, but then you use your
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industry knowledge and your specific knowledge actually fill in the
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gaps to kind of say, Okay, for card, this is
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what that looks like, and or it could do the
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twenty percent you do at eighty percent. But I think
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that in this new world of AI, there there's definitely
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going to be a sense of of expectation of getting
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ideas or new concepts or different ways of doing things
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versus just this is how we did this before, because
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our businesses are starting to ask for it.
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With the emergence of AI and all of these different
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opportunities that are available. How do you feel like that's changed.
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I guess the way that we define what it means
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to be data driven.
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Think about even with me, I write, I write a
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LinkedIn quite a bit. So typically when I post something,
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i'll write something I use this tool call it Grammarly.
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I'll put it into grammarly to kind of fix all
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the grammar changes and things like that, then.
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I'll post it. So that's part of my workflow.
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So I think we have this new normal where it's
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going to be in our workflow to kind of make
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us either more efficient we can do things faster or
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at a layer quality on top of it where we
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didn't have that before. I think it's gonna become the default,
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and our businesses are trying to say that too. Okay,
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where we use an AI, put some AI and this,
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So I think that's where we're it's become the default
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of I have a process, but I think about how
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do I use it AI to take it to the
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next level or amplify that because what I'm doing now
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is fine, but I can be better.
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Have you seen cases where you're able to identify, like
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I don't know whether or not a particular use case
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for AI is actually going to be beneficial for the company,
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because one of the challenges I've seen is like this
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differentiation between we want to implement AI, so let's just
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find new solutions to problems that we may not quite
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have yet. We're just excited to implement it, versus something
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that's actually solving a business problem.
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Yes, I think the challenge is that AI is probabilistic,
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where especially JENAI, it's using math and science to kind
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of think about what's the best sequence of words and
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texts and things like that. So in many cases, if
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you have a very determined this system where it's a
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very straightforward rule set of if this then that, if
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that then that, then it's not the best, Like it's
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kind of overkill. And I think that's what I always
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myself of is sometimes you can use tried and true
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methods because they're cheaper, they're world established, more people know
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how to use this stuff. So I think, you know,
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trying to use AI for everything is a challenge. I think,
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you know, you have to look at you have to
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justify why you instead of defaulting. Yeah, I think the
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better questions, Hey, why do I need AI to do that?
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When I can use all these other methods. So maybe
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we've been doing workflow, We've been doing business process automation
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and workflow rules for you know, for decades now. So
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I think people struggle with the best way to use
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it because I think it's a difference between like very
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rules driven. Like when something's rules driven, AI is not
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the best use case for that because you already know,
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like you already can put in very simple checks around
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what it is. But if like when we talk about
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things like marketing copy where it's the language could be different.
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It really depends.
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Or you're doing kind of process documentation where you're trying
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to use language to translate that to that where it
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could be you can make those tweaks. So I think
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there's many use cases out there. You may have an idea,
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but you realize, yeah, AI is not good for that.
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It's it's too it's it's it's too simple. It's like
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what we're trying to do is is too simple for
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air or also, if you think about it, you have
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to support this. So all these use cases, if you
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implement something, you have to have people involved to keep
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it going. What happens when it breaks, Like someone has
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to go and figure out what happened. So if you
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if you're doing all these all these brand new things,
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you need to have a either contractors, consultants or team
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that supports that. You can't just implement this and they
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run away. Because everything breaks. Everything in your house, everything breaks,
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everything breaks, so someone asks to come in here and
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fix that. So if you're doing the latest and greatest,
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the people that can go in there and like solve
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that for you or fix it is very limited. So
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definitely a lot of vetting out of use cases. But
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it's fair. I would say it's challenging to find true
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AI use cases just because you it's not the default.
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It's kind of the let's justify why we would use
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AD in this case because nothing else works.
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When you were mentioning like rules based, I think you
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were saying, like AI is not the best for rules.
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Can you give a couple examples?
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So if I'm doing a workflow, if I'm doing a
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process workflow of let's say AP where I get an invoice,
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I want to double check, I want to tell I
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get an invoice, I have a feel where it has
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the vendor, I verify the spending limits, I set up
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an approver, and I pay the invoice.
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Like, that's a very specific rules.
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I know that when this happens, do this, check this
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value with that, and it goes to the next step
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and the next step. So in that situation, if you
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have very specific steps that you do the exact same
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steps every time, there's no variability.
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You're following the same steps.
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There's no point of using AI versus just using tried
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and true workflows of just a bunch of different boxes
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connected in cereal. And then they just kind of go
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and they implement the process, So are they putting an
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a solution on that? You have to ask the question
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of well, what's the value proposite with you know, why
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would you use an LM in the situation if it's
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just a very simple I don't se sequence stuff.
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So that's a lot of the question wrong. Eight.
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So a lot of people talking about AI agents, but
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you have to think a lot of people think about
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walking use agent for that when you realize it's just
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a straightforward set of steps really an agent, it's not
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going to be very helpful in that scenario.
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No, that makes a lot of sense because it's like
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what you're describing would be the difference between automation.
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Versus craig and like, we've been doing that for a
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long time. That's not really AI use case. So I
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think a lot of us in the past, probably a
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couple of years, are all dealing with that same thing
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where we're kind of trying to vet out all these
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use cases and people bring things to you, but you're saying,
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well that's automation bucket, that's or eliminate that process altogether,
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or sometimes it's completely overhauling a process. Let's not even
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talk about automation AIA, how about we just get rid
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of that, or we need to simplify that in a
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much different way. So the good news is that we're
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all trying different things, and like we brought up innovation,
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it's the The good thing is that the businesses are
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thinking about how do we do things differently, how we
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use our existing workforce more efficiently, how do we scale
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our operations, how do we get the most out of this?
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So I think that there's an inspiration or light bulb blup, Hey,
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there's better ways to do things. And even if AI
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is not the right solution, it's still someone thought to
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try something different or they're trying to figure out ways
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to make improvements. So I think at least AI has
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provided that too. Ethinic society is in terms of a
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you know, there may be better ways to do this
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or more efficient So and now I'll one more point there.
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So I've used AI and for somebody. I have many children.
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They all have different school lists. So in August I
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get I get the school list from the teachers, but
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I have five children, so I get five different school lists,
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so I have to put them on, especially cause I'm
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only going to shop one time. Not shopping fight different times,
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So I think I put in groc where I'd said
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I'd say groc or whatever I use at the time.
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I said, take these, take these lists, upload.
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These, figure out, combine all this list together to make
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more big spreadsheet around. They need to buy twenty crayons,
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they'd buy ten pencils. So that was a use case
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because it would spend me hours trying to look at
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every single thing, trying to collapse them all and say,
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crayon's there, crayons there. So that was a situation where
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it wasn't like a I can't that's not really a
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rules strip. It's more like a hey, take this, like
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take this, take these different pdf there are different structures,
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and try to figure out the language, try to translate
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this together to hammondize that. So you know it definitely
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that saved me a ton of time, like hours try
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to collapse all these school lists. It's a one straightforward
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school supply list that I can then go ordering and
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get a bunch of school supplies.
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It's got such a huge potential to save a lot
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of time. The use cases are very interesting. Would you
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mind sharing a little bit about the kinds of problems
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that you're working dissolve as a director at Carhart.
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A lot of it is a lot of his cross
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functional nature, just different business units asking for things, but
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trying to cut across to figure out how all these
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things impact each other and they work together. So supply
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chain may get this metric, our customer care teammate get
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that metric, and our other teammate it another metric. So
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how do I bring all that together to tell the
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story about how it impacts the business and overall what
480
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we're trying to do with that. So I don't have
481
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data engineering, I don't have data science, but kind of
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everything else falls on me in terms of kind of
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your BI and analytics. We have a center of excellence
484
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as well, so sharing with people like you know, we
485
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can't build everything, but also sharing the knowledge of this
486
00:22:20.359 --> 00:22:25.039
is how you would build this into HARBI yourself, because
487
00:22:25.160 --> 00:22:27.119
you know, we can't do everything ourselves, so we do
488
00:22:27.200 --> 00:22:28.759
try to get that out to different users so they
489
00:22:28.759 --> 00:22:31.119
can you know, this is the models we have, This
490
00:22:31.200 --> 00:22:33.720
is how you use that in your own in.
491
00:22:33.680 --> 00:22:34.440
Your own day to day.
492
00:22:34.920 --> 00:22:37.720
How do you work with your analysts to kind of
493
00:22:37.759 --> 00:22:39.440
find the signal amongst the noise.
494
00:22:40.799 --> 00:22:43.240
I mean, prioritization is pretty key.
495
00:22:43.279 --> 00:22:45.759
So we work with our stakeholders pretty closely to figure
496
00:22:45.759 --> 00:22:49.039
out the what's the high priority work for them, because
497
00:22:49.039 --> 00:22:51.119
everyone's going to tell you different stories, like different groups
498
00:22:51.119 --> 00:22:52.400
are going to say this is more important to me,
499
00:22:52.440 --> 00:22:54.519
this is more important to me. So part of it
500
00:22:54.559 --> 00:22:58.160
is just understanding from our stakeholders just what's their top
501
00:22:58.200 --> 00:23:00.119
three or top five. Then try because you know, we
502
00:23:00.160 --> 00:23:03.240
have limited resources, even we can't do everything, so a
503
00:23:03.279 --> 00:23:05.480
lot of that is just trying to a lot of
504
00:23:05.519 --> 00:23:08.519
the relationship building. So working with our stakeholders ask them
505
00:23:08.599 --> 00:23:10.319
just you know, what's the priority is, what we should
506
00:23:10.319 --> 00:23:13.559
be working on, what maketures are important, what questions are
507
00:23:13.559 --> 00:23:15.920
they trying to answer, what decisions they're trying to make.
508
00:23:16.880 --> 00:23:18.440
And then based off that, then I think is when
509
00:23:18.440 --> 00:23:19.960
we can kind of start to build out and say
510
00:23:19.960 --> 00:23:22.519
what we have that we can build this out for you,
511
00:23:22.599 --> 00:23:23.759
and then we can give that to you or we
512
00:23:23.799 --> 00:23:25.319
can show you that, hey, this is how you would
513
00:23:25.359 --> 00:23:27.279
use that, or this is how you can leverage that.
514
00:23:28.799 --> 00:23:31.200
I think the tricky part that I've seen, and we're
515
00:23:31.240 --> 00:23:35.200
on a loan where you know, companies don't have large
516
00:23:35.319 --> 00:23:39.519
analyst teams like a couple, a couple functions may have
517
00:23:39.599 --> 00:23:42.319
like a data analyst or a business and that does
518
00:23:42.359 --> 00:23:45.240
some data work. So you know there's some data work
519
00:23:45.240 --> 00:23:47.039
that's done out in the business units, but a lot
520
00:23:47.039 --> 00:23:48.960
of that does fall onto the data team to kind
521
00:23:48.960 --> 00:23:50.960
of help fill in the gaps in terms of Okay,
522
00:23:51.039 --> 00:23:53.200
we've here's some data we can pull for you, or
523
00:23:53.319 --> 00:23:54.920
based off what you're saying, here's what we think you
524
00:23:54.920 --> 00:23:58.039
can use. So I think that's where I'm also kind
525
00:23:58.039 --> 00:24:00.839
of focused on too, is like how do you scale that?
526
00:24:01.119 --> 00:24:04.839
Just because every group doesn't have a dedicated analyst, so
527
00:24:04.880 --> 00:24:07.279
how do we automate, like either through AI or something
528
00:24:07.279 --> 00:24:09.480
like that, Like how do you how do you scale
529
00:24:09.680 --> 00:24:13.079
that analytical nature of like okay, the data change, what happened,
530
00:24:13.079 --> 00:24:14.880
why did it happen, What's going to happen next?
531
00:24:15.200 --> 00:24:17.079
So I think that's kind of the next frontier line.
532
00:24:17.119 --> 00:24:19.200
The data work is going past so you have bi
533
00:24:19.400 --> 00:24:22.039
of course, but it's also going past how to say, okay,
534
00:24:22.279 --> 00:24:25.079
BAEd off the metrics we capture, What does this mean?
535
00:24:25.240 --> 00:24:27.240
Why is it interesting? Like what is this going to
536
00:24:27.319 --> 00:24:30.359
tell the company? So I think that's where because we
537
00:24:30.359 --> 00:24:31.880
don't have enough people, we do not have enough bodies
538
00:24:31.880 --> 00:24:34.519
to do that, So that's goes to the efficiency and
539
00:24:34.559 --> 00:24:37.039
how do you use different methods to tell that story.
540
00:24:37.759 --> 00:24:40.519
It's an interesting problem but makes me think about just
541
00:24:40.799 --> 00:24:43.720
self service dashboards obviously. I mean a lot of teams
542
00:24:43.759 --> 00:24:46.079
are working to get more people in the company access
543
00:24:46.160 --> 00:24:48.839
to the right data, but then ensuring that they know
544
00:24:48.960 --> 00:24:51.200
like how to use it, how to filter it, and
545
00:24:51.279 --> 00:24:53.000
how to also make sense of what it is that
546
00:24:53.000 --> 00:24:55.839
they're saying so they don't make a decision based off
547
00:24:55.880 --> 00:24:59.240
of something that's like out of context. Aaron, I would
548
00:24:59.240 --> 00:25:01.599
love to talk with you a little bit about like
549
00:25:01.680 --> 00:25:05.680
your leadership, if that's okay. When you're leading these teams,
550
00:25:05.720 --> 00:25:08.359
you're working with people, what are the values that kind
551
00:25:08.400 --> 00:25:09.799
of drive the way that you lead.
552
00:25:09.920 --> 00:25:12.839
Yeah, I'm still learning, but I would say clarity. I mean,
553
00:25:13.480 --> 00:25:16.559
I'll say being in the weeds as much as you can.
554
00:25:16.680 --> 00:25:18.519
So I try to have one on ones with each
555
00:25:18.519 --> 00:25:21.200
of my direct reports every single week just to keep
556
00:25:21.240 --> 00:25:23.039
on track of like what's going on with the projects
557
00:25:23.039 --> 00:25:24.599
they're working on, just to make sure I have an
558
00:25:24.640 --> 00:25:25.759
idea of just what's going on.
559
00:25:26.240 --> 00:25:27.200
So I think that's that one.
560
00:25:28.079 --> 00:25:30.200
I'm clarity, Like I mentioned in the beginning of it,
561
00:25:30.240 --> 00:25:34.720
just being very clear about roles and responsibilities of like
562
00:25:34.839 --> 00:25:37.839
this happened? Is this team ever evolved? Like are you
563
00:25:37.880 --> 00:25:39.680
involved in this? Are you not involved in this? What's
564
00:25:39.720 --> 00:25:41.240
your role in this? What are you supposed to be doing?
565
00:25:41.279 --> 00:25:43.599
With expectations of you? I think a lot of people
566
00:25:43.799 --> 00:25:46.519
know this frustration that can happen because you think someone
567
00:25:46.519 --> 00:25:49.200
should be involved, but they never, like you never told
568
00:25:49.240 --> 00:25:50.880
them that they should be involved.
569
00:25:50.480 --> 00:25:52.440
And they never confirmed or deny it.
570
00:25:52.480 --> 00:25:55.759
So I think really being clear about expectations of this
571
00:25:55.799 --> 00:25:57.279
is where I need you this week. This is what
572
00:25:57.319 --> 00:26:02.559
I need from you. There's also vision which I'm working
573
00:26:02.599 --> 00:26:05.880
on too, like trying to share a vision of like
574
00:26:05.960 --> 00:26:07.720
this is where I think we need to be going,
575
00:26:07.759 --> 00:26:09.319
and that's why I think we need to be This
576
00:26:09.359 --> 00:26:11.960
is what I think we need to be doing. This
577
00:26:12.039 --> 00:26:13.960
is what I think the team should be aligned to.
578
00:26:14.240 --> 00:26:17.319
I think being repetitive around just like consistently saying the
579
00:26:17.319 --> 00:26:19.799
same things over and over again, just make sure that
580
00:26:19.799 --> 00:26:22.319
people get the message. So a lot of it is
581
00:26:22.359 --> 00:26:24.440
not even like the technical side. I think as you
582
00:26:24.440 --> 00:26:27.680
pull out into the leadership round, like the technical side
583
00:26:27.720 --> 00:26:32.519
isn't as strong, like it's more the change management, sharing strategy,
584
00:26:32.720 --> 00:26:36.599
executing a strategy. So I think that's the tricky part
585
00:26:36.599 --> 00:26:38.559
about a lot of the work that we do is
586
00:26:39.480 --> 00:26:41.240
you're not the reason that you might do like as
587
00:26:41.240 --> 00:26:43.200
you used to be, but you still have to have
588
00:26:43.319 --> 00:26:44.119
to lead the team.
589
00:26:43.960 --> 00:26:44.640
And lead the function.
590
00:26:45.200 --> 00:26:47.440
This is why I like having these kinds of conversations
591
00:26:47.519 --> 00:26:50.079
because you know, for me as a data analyst, I'm
592
00:26:50.079 --> 00:26:52.359
sure for other people who are analysts, it's like you
593
00:26:52.359 --> 00:26:55.519
don't always get the opportunity to speak with a manager
594
00:26:55.640 --> 00:26:58.359
or a director to find out what their perspective is, like,
595
00:26:58.440 --> 00:27:01.519
what they're looking for, what challenge is they experience. I
596
00:27:01.519 --> 00:27:05.440
feel like having that human awareness, you know, this is
597
00:27:05.440 --> 00:27:08.400
somebody on the other side who's got an entirely different
598
00:27:08.440 --> 00:27:11.200
world of their own that they're dealing with that as
599
00:27:11.200 --> 00:27:13.680
an analyst you just don't always get to peek into
600
00:27:13.880 --> 00:27:15.079
is very interesting to me.
601
00:27:16.359 --> 00:27:18.720
Yeah, I think you know, leaders are also figuring these
602
00:27:18.720 --> 00:27:21.079
things out. Like as a leader, when it comes to
603
00:27:21.200 --> 00:27:23.200
a question, we also like none of us know and
604
00:27:23.240 --> 00:27:25.000
we talk to our peers and they don't know either.
605
00:27:25.039 --> 00:27:27.079
So I think that's the that's the secret no one
606
00:27:27.119 --> 00:27:30.559
talks about is when it comes to AI strategy monetization,
607
00:27:30.759 --> 00:27:33.400
ROI like we're all trying to figure this out, like
608
00:27:33.440 --> 00:27:36.359
everyone's trying to We're trying things, we're testing things out.
609
00:27:36.960 --> 00:27:39.440
The question, just as leader, are you, are you showing
610
00:27:39.440 --> 00:27:41.079
your hands to your team and saying, hey, guys, like
611
00:27:41.079 --> 00:27:43.279
I need help with this, like I need input, I
612
00:27:43.279 --> 00:27:45.960
need feedback, I need to I need some responses. So
613
00:27:46.319 --> 00:27:49.359
I think there is some vulnerability too about being open
614
00:27:49.400 --> 00:27:52.480
and sharing you know, these things your team and say hey,
615
00:27:52.519 --> 00:27:55.319
I'm being pressed to figure these things out. I don't
616
00:27:55.359 --> 00:27:57.599
know what's going on. I need your help with this.
617
00:27:58.960 --> 00:28:01.680
But to your point, I mean, I think it's it's
618
00:28:01.799 --> 00:28:05.119
very tough to like we have one on ones, but
619
00:28:05.200 --> 00:28:07.480
it's you need like a separate one on just talking
620
00:28:07.519 --> 00:28:09.240
about these things separate because you want one You usually
621
00:28:09.279 --> 00:28:11.880
talking about like projects, you have half hour project updates,
622
00:28:12.119 --> 00:28:14.079
how's everything going, how's the family? But to your point
623
00:28:14.160 --> 00:28:16.720
is these are deeper discussions that you probably don't have
624
00:28:16.759 --> 00:28:18.920
time to talk with your leader about unless you sped
625
00:28:18.960 --> 00:28:21.200
the umber time and say like, can I just ask you,
626
00:28:21.279 --> 00:28:22.839
like how ask you about this?
627
00:28:23.079 --> 00:28:24.359
Ask you about this topic? So was it?
628
00:28:24.680 --> 00:28:27.920
Yes, it's definitely a different topic, which I think a
629
00:28:27.960 --> 00:28:30.559
lot of these podcasts are very very helpful, just talk
630
00:28:30.599 --> 00:28:33.599
to different leaders and executive leaders, to the c suite,
631
00:28:33.640 --> 00:28:37.200
senior leaders, you know, managers. I think that's why these
632
00:28:37.200 --> 00:28:39.440
are helpful, just to hear from the people in the weed,
633
00:28:39.519 --> 00:28:41.319
to hear from the little leaders about how they think.
634
00:28:41.160 --> 00:28:42.119
About these things.
635
00:28:42.759 --> 00:28:44.920
I think so too, because it helps build empathy.
636
00:28:44.960 --> 00:28:47.279
Because an analyst, when you're when you've got like this
637
00:28:47.359 --> 00:28:49.759
big black box, you just can't see what's happening on
638
00:28:49.759 --> 00:28:53.200
the other side, it's so easy to fill in those gaps.
639
00:28:52.880 --> 00:28:53.799
With your own narrative.
640
00:28:54.119 --> 00:28:56.359
Yeah, and it feels it feels so right, but it's
641
00:28:56.400 --> 00:28:59.240
not because you're just you're actually missing quite a bit
642
00:28:59.319 --> 00:29:01.920
that's happening. And even if you know, let's say you
643
00:29:01.960 --> 00:29:04.119
have an idea and you present it to your manager
644
00:29:04.240 --> 00:29:06.599
or with the director, and you know they're saying, this
645
00:29:06.640 --> 00:29:08.720
is a good idea, but not right now. You could
646
00:29:08.759 --> 00:29:11.079
fill that in with your own story or they may
647
00:29:11.160 --> 00:29:14.720
genuinely like it. There's just not enough resources, the timing's
648
00:29:14.799 --> 00:29:17.119
not right, it doesn't align with the goals, whatever that
649
00:29:17.200 --> 00:29:17.480
may be.
650
00:29:18.680 --> 00:29:23.480
Yeah, and I think that's the clarity question of like
651
00:29:23.559 --> 00:29:25.839
just confirm it, because I think to your point, in
652
00:29:25.920 --> 00:29:28.000
the void, we make up around narratives, around what we
653
00:29:28.039 --> 00:29:30.599
think happened and why it happened. But I think that's
654
00:29:30.599 --> 00:29:31.839
where you have to ask the question. And I think
655
00:29:31.839 --> 00:29:33.880
those are those hard discussions, but I think they're helpful
656
00:29:33.920 --> 00:29:36.079
just to kind of clear it up the narrative because
657
00:29:36.079 --> 00:29:37.759
someone say, hey, I was having a bad day or
658
00:29:38.119 --> 00:29:40.119
I thought we were going this way, they completely changed
659
00:29:40.119 --> 00:29:42.759
the story right before the meetings. I think getting things
660
00:29:42.799 --> 00:29:45.319
documented because the problem I have is sometimes people say, well,
661
00:29:45.359 --> 00:29:47.880
they said this and they said that, and I always say.
662
00:29:47.359 --> 00:29:48.759
Is this documented? Is there an email?
663
00:29:48.839 --> 00:29:50.559
But they're like no, Well, first of all, let's get
664
00:29:50.559 --> 00:29:53.400
this on paper or digital paper, like an email something,
665
00:29:53.759 --> 00:29:55.119
just the way we can share and say I have
666
00:29:55.200 --> 00:29:57.000
it here where you said this, or this happen, or
667
00:29:57.000 --> 00:29:58.079
this project thing you happen.
668
00:29:58.440 --> 00:30:00.519
Let's talk through that. Yeah.
669
00:30:00.519 --> 00:30:03.039
But I think it's just clarity and having just a
670
00:30:03.039 --> 00:30:07.240
lot of discussions. And that's just as much as the
671
00:30:07.279 --> 00:30:10.160
technical work like the coding and building the things are helpful,
672
00:30:10.599 --> 00:30:12.240
you still have to deal with the people themselves to
673
00:30:12.279 --> 00:30:13.799
make sure that you're going on the right path.
674
00:30:14.200 --> 00:30:14.400
Right.
675
00:30:14.559 --> 00:30:17.839
There was another podcast that you had spoken on and
676
00:30:17.880 --> 00:30:20.759
you mentioned the player coach model. Would you mind sharing
677
00:30:20.759 --> 00:30:23.119
a little bit more about what that What that means.
678
00:30:23.480 --> 00:30:25.440
I've seen that a lot where you have people that
679
00:30:25.480 --> 00:30:28.200
were promoted to like a manager position, but when they
680
00:30:28.240 --> 00:30:31.839
were promoted, they didn't they didn't give their work to
681
00:30:31.839 --> 00:30:34.000
anyone else. They're still doing the work they did before,
682
00:30:34.000 --> 00:30:37.000
but now they're a manager. But that's very difficult because
683
00:30:37.160 --> 00:30:39.039
I normally have to be in the ways doing this stuff.
684
00:30:39.079 --> 00:30:41.440
I have to also lead the team, but it's very
685
00:30:41.440 --> 00:30:45.319
difficult to like lead the team. You don't really have
686
00:30:45.400 --> 00:30:48.079
much time to think about strategy or resourcing because you're
687
00:30:48.119 --> 00:30:50.400
so busy. So I know a lot of people do
688
00:30:50.480 --> 00:30:52.599
that because it seems like a cheaper out just because
689
00:30:52.599 --> 00:30:53.960
oh I can, like I can pay them to be
690
00:30:54.000 --> 00:30:55.839
a manager, but I can also do that stuff too.
691
00:30:55.920 --> 00:30:58.960
But it's very difficult to kind of do both very
692
00:30:59.000 --> 00:31:03.079
well because then you're your haphazard coder or haphazard analytic,
693
00:31:03.079 --> 00:31:04.960
but you're also a haphazard manager. So you're kind of
694
00:31:05.000 --> 00:31:07.359
like part time, Like you're doing like part time with
695
00:31:07.440 --> 00:31:10.200
each of them. But then it feels like if you
696
00:31:10.200 --> 00:31:11.799
pay people, if you do things part time, you get
697
00:31:11.799 --> 00:31:15.839
part time results. So it's a difficult model to be
698
00:31:15.960 --> 00:31:16.920
very successful at.
699
00:31:17.720 --> 00:31:19.519
It sounds kind of like You've got one foot in,
700
00:31:19.640 --> 00:31:22.480
one foot out, Aaron. You've shared so much about like
701
00:31:22.519 --> 00:31:25.680
your work experience. Before we wrap up this conversation, I
702
00:31:25.720 --> 00:31:27.799
was hoping to learn a little bit more about the
703
00:31:27.920 --> 00:31:29.759
data and the d community that you created.
704
00:31:30.119 --> 00:31:32.039
Every single month, we kind of have these happy hour
705
00:31:32.079 --> 00:31:34.519
and we just have one last night, which is why
706
00:31:34.559 --> 00:31:36.920
I came home late. My daughter drew a picture. So
707
00:31:37.079 --> 00:31:39.039
every single month we go to a different bar and
708
00:31:39.079 --> 00:31:41.240
restaurant in the area. So very simple. So we don't
709
00:31:41.279 --> 00:31:43.359
charge anything, so it was a free event. We don't
710
00:31:43.400 --> 00:31:46.640
have any speakers, no sponsors, and no sales, so you
711
00:31:46.720 --> 00:31:49.240
just show up, you talk shop with some people that
712
00:31:49.319 --> 00:31:52.920
they're in the same industry, then you go home. So
713
00:31:52.920 --> 00:31:54.720
you do them every single month. We started this in
714
00:31:54.799 --> 00:31:57.039
June twenty twenty four. We've been doing it for the
715
00:31:57.079 --> 00:32:00.440
past year and a half. Been very successful. We realize
716
00:32:00.480 --> 00:32:03.039
a lot of our people, of our community members wanted more,
717
00:32:03.119 --> 00:32:06.480
so we did a conference. We actually ended up selling out.
718
00:32:06.559 --> 00:32:08.599
So it's one of the things where we're like, we're
719
00:32:08.599 --> 00:32:10.799
all volunteers. We don't do it full time. It's a
720
00:32:10.799 --> 00:32:13.519
part time gig. After work. I talked with my peers
721
00:32:13.559 --> 00:32:15.519
to figure out what they're working on. So it's been
722
00:32:16.000 --> 00:32:18.200
growing very organic that like people tell each other about
723
00:32:18.200 --> 00:32:20.400
it to hear about it. So it's just been a
724
00:32:20.400 --> 00:32:23.799
great experience, just because Detroit has a very vast and
725
00:32:23.920 --> 00:32:28.240
diverse businesses, so it's good for people with like people
726
00:32:28.240 --> 00:32:30.039
in the food inutry. You can talk to automotive, they
727
00:32:30.079 --> 00:32:32.279
can talk to healthcare, they can talk to financial services.
728
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So it's just been a great way for the local
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data community to get together and just have good discussions,
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meet people connect because I think after COVID, everyone's working
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remote now, so people want to meet people in the
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real world.
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Well, I'm sure it's incredibly valuable for the community, so
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I'm really thrilled that it exists out in the world
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00:32:50.559 --> 00:32:53.559
and that people are attending. Erin, thank you again so
736
00:32:53.680 --> 00:32:56.319
much for joining this podcast. It really was a pleasure
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00:32:56.759 --> 00:32:59.559
getting to know you and your leadership and your story.
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For anyone who's listening, I will also be posting Aaron's
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00:33:03.759 --> 00:33:06.359
link to his LinkedIn in the description so that you
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00:33:06.400 --> 00:33:10.079
can follow him and learn more about what he's up to.
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So thank you again, Erin. It really was such a
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pleasure having you on the show.
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00:33:14.200 --> 00:33:16.519
Yes, thanks for having me time flew by quickly, so
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00:33:16.519 --> 00:33:18.640
these are all fun discussion. Like you mentioned, get I
745
00:33:18.680 --> 00:33:20.680
don't get to talk about a lot of these things
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00:33:20.720 --> 00:33:22.279
in just the day to day because you so focus
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00:33:22.440 --> 00:33:26.400
on like the projects and the company. So these are good, fun,
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00:33:26.680 --> 00:33:29.359
free willing kind of discussions to have. It is how
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00:33:29.400 --> 00:33:31.039
you learn, is how you continue to grow as having
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00:33:31.119 --> 00:33:31.839
dialog like this.
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00:33:32.200 --> 00:33:35.960
So thank you, Thank you for listening to the quietly
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00:33:36.039 --> 00:33:40.160
ambitious Analyst. If today's episode helped you see data in
753
00:33:40.200 --> 00:33:43.319
a new way, share it with someone who'd appreciate it too,
754
00:33:43.799 --> 00:33:47.279
and follow the show for more lessons on business communication
755
00:33:47.599 --> 00:33:51.119
and driving impact with data. And if you found it helpful,
756
00:33:51.319 --> 00:33:54.599
leaving a quick review really helps this podcast reach more
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00:33:54.680 --> 00:33:57.079
people who want to grow in the same way. Until
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00:33:57.160 --> 00:33:59.759
next time, focus only on what you can control and
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00:33:59.759 --> 00:34:01.920
star drive to be one percent better each day.