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Welcome to Women in Analytics After Hours, the podcast where we hang out and learn with
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the WIA community.
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Each episode, we sit down with women in the data and analytics space to talk about what
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they do, how they got there, where they found analytics along the way, and more.
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I'm your host, Lauren Burke, and I'd like to thank you for joining us.
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Today I'm so excited to have Riesling Walker joining us.
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So Riesling, welcome to the podcast.
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Thank you so much for taking the time to join us.
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Thanks for having me, Lauren.
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Absolutely.
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I'm really excited to have you on and learn a little bit more about your background and
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the path you've taken.
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So just to start off, could you tell us a little bit more about your background in data
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and what inspired your path?
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Yeah, absolutely.
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First, I'll say I was very lucky and stumbled into analytics very early on.
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I went to Georgia Tech for my undergrad, and Georgia Tech is really career-focused.
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So one of my first homework assignments, freshman year, was to go to the career fair.
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Most companies are not looking for freshmen interns, and I literally had no idea what
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type of job I wanted to do.
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But I was lucky enough to just pick a long line and end up talking to Capital One, who
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had a one-week case competition for analytics.
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And I applied, and I got it.
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And from that competition, I got a return offer to intern.
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And after that internship, I got a full-time offer to go back.
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I spent almost three years at Capital One in their partnership credit cards office, supporting
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various credit card portfolios like Kohl's and Saks Fifth Avenue, working on government
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sales exams, and working on fraud prevention as well.
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I wanted to move back to Atlanta, and so I took a new job, and I started Home Depot.
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I spent a year there on the online side doing internal search engine analytics.
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And then I moved store-side doing analytics for the merchandise execution team, which
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is sort of like a store operations team.
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They work with associates in the stores packing down merchandise and doing projects to make
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the merchandise shine.
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During the time at Home Depot, I also started the Georgia Tech online master's in analytics
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program.
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Then a recruiter from Atlassian reached out to me about a marketing data science role,
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so I moved over there.
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I realized I wasn't that interested in marketing data science, and I wanted to move internally,
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but Atlassian was actually founded in Australia.
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So all of the roles that I was interested in just didn't have a good time zone that
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overlapped with the Eastern time, and that did not work out for me, which is why I started
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looking for new roles externally and how I ended up at Microsoft.
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So I'm here at Microsoft.
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I'm on the customer experience data analytics and insights team.
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Our team's focus is increasing Microsoft's cloud revenue by looking at cross products
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and across job areas to try and unlock hidden value with data science.
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But I actually just accepted an offer to go back to Home Depot.
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So I will be a data science manager on the merchandise execution team that I mentioned
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earlier.
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So by the time this podcast airs, I will be over there.
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So I've had a long journey, lots of different positions, but lots of analytics along the
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line.
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That's awesome.
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And huge congrats on the new role.
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That's a really exciting transition you're about to make.
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Yeah, I'm so excited for it.
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And so speaking of that, you've actually previously written a lot about changing roles in the
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data space and making moves both internally and externally like you're about to do.
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So how do you do and how do you evaluate when it's time to move on from your current role?
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Yeah, that's a great question.
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And I really think it's different for every person and can depend on your life stage or
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career stage.
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For me, I've changed roles or at least considered changing roles for a ton of different reasons,
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ranging from wanting to move from Chicago to Atlanta, a large step increase in conversation.
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If I'm bored or just feel like I'm not learning in a role, or if I'm really, really excited
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about the next role or if there's a manager I don't like, I think there's so many different
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reasons that someone might think about changing roles.
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I will say I know I've changed roles a lot.
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I feel like it's almost acceptable early in your career.
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But as I step into this manager role, I know how important it is for me to have a consistent
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manager over time.
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So in this career stage, I will definitely not be changing as often because I know it'll
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more directly impact other people.
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For me, when evaluating if it's the right time, that's hard.
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Sometimes when you want to make a change, there's not really anything that's hiring
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or you can't really find the right positions.
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And sometimes when you don't want to make a change, you might get laid off from your
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current position or your former director might call you with an exciting opportunity.
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So when I think about a change, I like to make a grid.
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My columns are the different opportunities I can pursue and my rows are the pros and
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cons.
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And I think just writing it down, trying to gather as much information as possible and
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being as objective as I can really helps me evaluate if it's right to make a change and
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what the next step should be.
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That's a really interesting approach with the grid.
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You're kind of coming out about it from a data perspective, which really fits pretty
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well.
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What are some of the pros and cons you usually look at?
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Yeah, it's hard because I feel like for every good thing about changing roles, there's also
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a bad thing.
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And that's why I try and do the pro-con grid to really like weigh out the benefits and
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the costs.
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I also think it's really important to understand the whole picture when making a decision or
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especially going into negotiation conversations.
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So I think the biggest pro I touched on earlier is compensation.
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When you change roles, that can be one of the fastest ways to make large step changes
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in compensation.
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But you also have to remember the things you're giving up like unvested stocks and bonuses,
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the benefits packages are probably different.
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And I also want to point out you don't always get a pay raise when changing roles.
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I personally took a step down in compensation when changing locations.
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Another benefit I think about is just like the ability to learn.
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My favorite thing about joining new companies is getting to learn so many new things and
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meet so many new interesting people.
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But the downside to that is you spend so much time ramping up that it'll take you longer
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to execute on a project, which means it will take you longer to get promoted in that position.
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The last thing that I always think is a positive is getting to choose your own work.
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Everything sounds so exciting, you know, the grass is greener.
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But just like you're selling yourself in an interview, the hiring manager and company
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are also selling themselves too.
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So the role might not be quite as amazing as you expected.
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So I find the best way to balance these pros and cons is to change roles internally, because
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you have more information when choosing your own work.
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You can talk to more people, you're more familiar with the company, you get to learn a new area
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of the business without sort of ramping up on those things like HR benefits, tech stacks,
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and more that you might have to learn by changing companies.
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And it's a clear time to renegotiate your compensation.
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Although you can negotiate anytime, but that's a completely different conversation.
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But I just find that changing roles internally is the best way to balance it, which is why
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I always try to do that first when I think I'm ready for a change.
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That's a really good way to approach it.
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I've definitely like I've personally moved internally.
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And like you said, it is really so much easier to get up to speed, especially in the data
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space, you're familiar with the data and how it's connected a little bit better.
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And even if you're moving into a new area or role, you at least have that familiarity
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with the people you might be working with the data you might be working with the company
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as a whole and its goals.
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So that's a really interesting perspective to bring about when you're thinking of making
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that change.
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Yeah.
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And especially on the people, like it can be really hard to keep up with mentors and
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people you admire from previous companies.
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And you never know how people are going to move within a company or which mentor or advocate
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or person you've worked with before is going to show up in your next project, even if it
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feels like an unrelated area.
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So I think people is another one, I mean, relationships take so long to build at new
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companies.
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So changing internally is another great way to keep up with those people.
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Right.
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That's absolutely true.
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And so for anyone thinking about making a transition in the near distant future, either
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internally or externally, what are your suggestions and how do you set yourself up for success
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when you're about to make that change?
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Yeah, I think my first piece of advice is start early and often start before you even
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think you want to make a move.
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I had a mentor tell me once that if you start looking for a new role when you're already
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unhappy, it's too late.
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And he's so right.
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It takes you at least three months to land a new opportunity between the time it takes
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to look for roles, apply for them, meet people, interview and negotiate and more background
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check.
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So if you start looking when you're already unhappy with your role, think about how much
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more unhappy you'll be in another quarter and it might make you take an opportunity
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that's not the right fit for you just because you're so excited to get away from where you're
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in now.
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So the way I do this is I just am constantly networking and meeting new people because
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every time you meet someone, you might figure out that you're interested in working with
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them or you might be interested in working on that subject area and you can learn from
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them.
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This could be like as simple as participating more actively in meetings, being more active
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on Slack and Teams channels at work.
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And like it's not work.
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My favorite Teams channel, Microsoft, is our fiber crafts channel.
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We post pictures of our knitting and crocheting and we have monthly happy hours where we all
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get on a Teams call and we knit together and I get to meet people from all over the company
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and in roles that I didn't even know existed.
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And it's just like such a fun way to meet people.
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That's so fun.
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Yeah, it's honestly the best.
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And lastly, it had one too and so I'm hoping Home Depot has a cyber art Slack channel and
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if not, I might have to start one.
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You definitely should.
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The other thing that's really important to like meet people is scheduling one-on-ones,
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going to conferences and DMing people online who are doing cool stuff.
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It's just more ways to meet people.
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But I think that is the way you build your network and find out what the right next step
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is for you.
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But you also need to build your brand so that you can position yourself to be considered
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to that next role.
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And that means kicking butt in your job, helping others so they know you're a person that you
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can be relied on, and maybe writing and posting publicly to build your brand outside of the
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company as well.
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So yeah, start early, network and build your brand.
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Awesome.
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And so speaking of changing roles, you have an interesting perspective as someone who's
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had the opportunity to be in a lot of different industries and see how analytics works within
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them, including banking, software, retail.
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So what are some of the key differences and maybe a few of the surprising similarities
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that you noticed throughout your work in these roles?
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Yeah, it has been really interesting to move between companies and I didn't realize how
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different my roles would be at each one.
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One really interesting thing that I've noticed is just the speed of the business.
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And by speed, I really mean the time it takes between the idea and the measurable impact.
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So Capital One was my first role and probably one of the slowest roles I've been in, just
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due to regulation in the industry and how people use credit cards.
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For each decision we wanted to make, we had to do a lot of analytics around recession
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testing to make sure this would still be good.
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And that's a government regulation.
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One had to go through credit and legal approvals.
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And then after the decision was implemented, it probably took four to six months at least
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to see how a cohort might behave after this change.
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And then if you want to account for seasonality, you're looking at like at least a year and
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a half until you can really evaluate the results of the decision, which is just a really long
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time.
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What's funny is my second role was on the Home Depot search team, which was the fastest
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team I've ever been on.
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My first day, I was playing around at the search engine trying to understand the business
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and I found a really small bug on their website and I showed it to my manager.
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He said, yeah, that's a bug.
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Screenshot it, throw it in this Slack channel and I'll get an engineer to fix it by end
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of day.
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And I was like, what?
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End of day?
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Don't we have to go through an approval process?
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Don't we have to measure the impact to show the value?
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And he's like, it's just a bug.
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Obviously the impact's good.
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No one needs to approve this.
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We just do it.
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Obviously larger changes to algorithms or experience have to go through approvals, but
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with so much volume on the search engine and because most people search and then purchase
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in that same visit, you can start getting indication of results within the same day
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and really test changes within weeks, which is awesome.
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So yeah, I mean, the speed of work is completely different and pretty much every industry and
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even every team you're on.
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It depends on mostly customer life cycle and government regulation.
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So things with longer customer life cycles probably need more analysis upfront because
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you're investing more time before you can make a change if the idea doesn't work.
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But if you're working with shorter customer life cycles, it's a lot more like iterative
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and testing because you can just throw something at the wall and if it doesn't work, remove
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really quickly and it really doesn't impact many customers or it doesn't impact customers
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for as long as the time period.
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So I think the speed of work is just really interesting and can completely change the
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way that you work from team to team.
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Definitely.
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I've seen a lot between industries, but also what you mentioned between teams because a
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lot of times, especially looking at analytics or data teams, the data you're working with
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in that division, say you're in the customer side versus maybe the marketing side, that's
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completely different data in some ways that you're looking at it.
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So for you to want to execute on a project in one area, it might be very quick.
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You might have people ready to work on it.
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You might have a team dedicated to fix things like that that are popping up versus somewhere
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else.
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You might have to be the one to drive that and push that along and make sure that you're
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able to accomplish that in a timely manner.
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Yeah.
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And I mean, even within a department at Iglasi and I was on marketing analytics and I was
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supporting a marketer who worked on top of funnel metrics and another marketer who worked
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on bottom of funnel metrics.
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So top of funnel is really awareness, like new customers, how do we land someone and
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just have them think positively about the brand and not selling at that point?
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Well bottom of the customer is like, how do we get them on the phone with someone?
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How do we get them doing a demo of our product or a trial, a free trial to get them used
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to it and playing with it and using it?
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And so the life cycle between top of funnel and bottom of funnel of when you can measure
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impact is also huge.
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So yeah, it really depends team to team and sometimes project to project, but that's been
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something that's jarring, changing teams and industries.
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Yeah.
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So what are the different types of data you've found yourself working with and how can it
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differ across these different industries?
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Oh gosh.
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Yeah.
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I think that's one thing that's similar at every company is there's always something
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wrong with the data, but every company has their own unique thing wrong with the data.
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I've worked with so much data, I've worked with credit card data, fraud claims, online
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clickstream data from Adobe, store operations like timing in and out, marketing data, Salesforce
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data, cloud usage data.
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And every piece of data has its own quirks.
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Sometimes it's quality issues when it's human entered or there's issues with the implementation
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on the website.
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Sometimes it's a lack of documentation, sometimes it's a lack of latency, sometimes it's hard
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to join data sets because their keys don't work together.
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Sometimes it's business definitions.
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How do we measure this?
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Is everyone measuring it the same way?
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So I think every time you change, there's a whole new set of things to learn about the
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data, which is exciting, but it's also challenging to ramp up on all of the new complexities.
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Right.
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And the complexities between different industries and the data within them vary, right?
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Because some places you might have really, really enormous data sets, but you're just
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looking at that one data set, that one table.
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But other areas you might have a slightly smaller set, but it's across 10 disparate
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tables and you have to figure out the correct way to combine those before you can even begin
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working with them.
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I think it's really interesting just the different perspective it can give you on how you go
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about working with these different sets of data to kind of come back to the same methods
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in every industry, but the way you get there and the way you approach it might have to
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be a little bit different.
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Yeah, absolutely.
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For that example, when I was working online, it was one table of all the clickstream data
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and that was pretty much what we used.
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But while working store-side, it's like, okay, well, we have to understand the structure
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of all the stores and the structure of all the products and what people are doing in
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the stores.
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So you have three completely different data sets that you have to figure out how they
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work together and if they can work together.
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Totally.
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And so with these transitions you've made between industries, between teams, seeing
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all of these different types of data sets and how they work and what you need to do
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to get them in the correct format, do you feel like that has helped you develop a perspective
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that across these different industries can be helpful even if it's not the same type
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of data, the same type of approach?
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Yeah.
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It's just so interesting seeing similar problems at new companies and saying, oh, I've seen
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a way to solve this.
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But also sometimes the solutions don't work across each place.
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Sometimes the issues are different and it's just a new problem to try and solve.
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That's a good lesson to learn that we hear a lot about all of these really cool methods,
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especially in data science.
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There's this new model coming out.
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There's this new way you must approach data cleaning.
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You must approach visualization.
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You must approach your empty and missing variables.
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But like you said, sometimes that doesn't work in the particular domain.
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And I think that's important to recognize as you gain that experience.
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Yeah.
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I mean, one of the things I didn't touch on yet about the differences between companies
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is just the differences of tech stacks.
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Lost count of how many ways I've learned to query data between Teradata, Databricks,
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GCP, Azure.
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There's so many different ways and not all of the solutions work the same.
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Some of them execute things differently.
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So there's different efficiencies.
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You know, Tableau and Power BI are very different in how you can bring in data and their best
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practices.
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So yeah, sometimes solutions at one company don't work at the other.
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Sometimes you're really excited about the new tech stack.
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Sometimes you really miss things about the old tech stack.
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I also think one change I didn't really expect was how different the way of working was.
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I expected differences in tech stacks, but I didn't expect differences in what I'm calling
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the communication stack or how people work together.
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These are things like using PowerPoint decks instead of docs and pages.
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Or if your team defaults to sending emails instead of using Slack by default, some teams
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are in meetings more than others and that's normal expectation.
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Or on-camera versus off-camera.
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What different job titles mean?
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So at one company I was at, everyone above manager was given the title head of something.
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So it was really hard to figure out what is the hierarchy in areas of the business I wasn't
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familiar with.
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So I think that's another thing people don't realize they have to ramp up on when they
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change companies is like, oh, I was really good at communicating in this way and people
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don't communicate like that here.
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So you have to spend time building these relationships and trust and communicating in new ways even
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though you thought, oh, I would just be ramping up on the data in the subject area as well.
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That's so interesting.
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And I'd like to add on to the communication side.
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I think between companies you also see differences in your ability to communicate with people
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on different teams or in different areas.
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You were saying with your group where you just meet other people in the company and
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you knit together, that's a way that you're able to find people in different areas and
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just already build that relationship versus if you have that maybe more email-based culture,
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you don't have random Slack groups you can join to kind of find more of the community.
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You don't have like communities of practice for little areas.
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So I think that's interesting to kind of think about as well.
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And I really think those type of things can enhance how well your company and your teams
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work together just because having that ability to not only connect when it's necessary really
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helps you have that basis for some of the things you might need to be doing in the future
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and that just sets you up for success.
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Yeah, yeah, absolutely.
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00:22:20,880 --> 00:22:24,400
So thinking of some of the other things that might help you when you're thinking about
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transitioning to a new role, I know you studied math in undergrad and then after you worked
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as an analyst for a couple of years, you ended up going back for your master's in analytics
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from Georgia Tech.
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And so what inspired you to jump back in and what did you gain from that experience?
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Yeah, so I actually had one more role that I left out of my intro because it's a lot
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of roles.
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When I was looking to move back to Atlanta, I accepted a contract position and I thought
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it was going to be analytics and it really wasn't and I really didn't like it.
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And I knew at the end of my contract, I wanted to do analytics.
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And I was like, gosh, how do I tell hiring managers that all I want to do is work with
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data?
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And I picked the Georgia Tech online master's in analytics for a couple different reasons.
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The first is it's completely online and flexible.
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It's not quite go at your own pace.
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There's deadlines for homeworks and exams, but you pretty much have a week anytime it's
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assigned to when it's due.
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So you can do it on your own.
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The videos are all recorded so you can watch them anytime.
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And so I knew I could do that part time while still keeping my job.
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Well, I mean, the first reason was really I wanted to get back into analytics.
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The second was it was part time.
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And the third was it's really cheap.
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I did the whole program about over three years and the whole thing was around $12,000.
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And Home Depot and Atlassian have very generous tuition reimbursement programs.
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So I paid for less than half of it myself.
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And I will say having that on my resume really helped with interviews.
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A lot of people saw it as a signal that I really was passionate about analytics.
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And taking the classes helped me in my interviews, but also executing on the job.
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I started taking classes and I was able to implement the things I was learning in class
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right away to my work, which I think was really great and really helped advance my career
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as well.
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The other thing I gained from it is the network.
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Speaking of those communities that we were talking about, there's a Georgia Tech online
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master's in analytics Slack community that's only for GTOMSA students.
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And through that Slack community, I've made friends.
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I found the jobs that I'm in right now and I sound back fills for when I leave positions
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to make sure that my teams have someone to fill it when I leave as well.
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So the Slack channel is absolutely amazing.
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But yeah, I mean, I really did it just to get back into analytics and ended up going
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through the whole program and finishing it out.
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That's awesome.
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So as you made that transition, you also made the transition from analytics from an analyst
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role to a data scientist role.
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Do you think the masters helped you make that transition and what else encouraged you to
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make that move?
416
00:25:08,840 --> 00:25:10,840
Yeah, that's a good question.
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00:25:10,840 --> 00:25:16,120
I will say, I don't think you need a master's to advance your career.
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I was told pretty early on in my career that you can sort of think of a master's as like
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one to three years of work experience.
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00:25:22,920 --> 00:25:25,240
These are things you can learn on the job.
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It just sort of helps accelerate your career.
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00:25:28,320 --> 00:25:33,320
I don't know if my shift from analytics to data science was necessarily intentional,
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but what I found was the work that I had been doing in analytics kept shifting over to data
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science.
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When I moved off my analytics team on the search team at Home Depot, within three months,
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they actually changed all those titles to data scientists.
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00:25:47,820 --> 00:25:52,200
And when I moved off the merchandise execution team at Home Depot to Atlassian for my first
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data scientist position, they then also broke that analytics team in half and made half
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them data scientists and half them analysts.
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I think that transition is sort of naturally happening with a lot of people.
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As you advance your career, you're going to be using some of these data science techniques
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in your analytics to be solving problems.
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00:26:11,840 --> 00:26:15,920
I think it's a combination of, yeah, my skill set grew through this master's in analytics
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from Georgia Tech, but also the industry's kind of changing the lines of blurring between
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analysts and data scientists.
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And so it sort of was a natural progression for me as well.
437
00:26:26,960 --> 00:26:28,240
That makes a lot of sense.
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00:26:28,240 --> 00:26:33,000
A lot of roles that are data analyst or data scientist kind of actually fit more in the
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middle and you are doing analytics work, you are doing data science work, but it really
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can almost blur if you're on the right kind of team, especially if it's a more limited
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member team, just because you need people to fill those gaps.
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00:26:45,520 --> 00:26:49,260
I've kind of seen a new title more recently that's called analytics scientist.
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And I feel like that actually might be in the middle of that.
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I'm seeing a lot more data science, data analytics related job titles that might be defining
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that divide a little bit more, but we'll have to see as that progresses.
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00:27:03,480 --> 00:27:07,540
Yeah, I think people are trying to figure out the right titles.
447
00:27:07,540 --> 00:27:08,540
I think there's so many.
448
00:27:08,540 --> 00:27:10,520
I mean, there's analytics engineer.
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00:27:10,520 --> 00:27:14,320
I saw on LinkedIn someone saying, you know, maybe we should make a business scientist
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role.
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00:27:15,320 --> 00:27:21,280
I think what I do instead of looking at job titles is I look at job descriptions to see
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what skills they're looking for.
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It takes a lot more time.
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It really does, but it helps figure out the right role.
455
00:27:29,400 --> 00:27:33,160
I think another thing that's important to keep in mind is one thing they talked about
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at the last Cinalop is building balanced teams.
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And so I feel like it's really good to have someone who spikes more in analytics, you
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know, quick and dirty problem solving, being able to pull SQL, likes talking with stakeholders
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and also having someone on the team who leans more data science, who enjoys that heads down
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00:27:52,600 --> 00:27:58,660
work, likes to build more complex statistically rigorous models because having a whole team
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00:27:58,660 --> 00:28:04,140
of one of those people, like they'll get work done, they'll add good value, but having both
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of those kinds of people on the team, they'll help push and grow each other.
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They'll also learn from each other and if one person doesn't like doing something, the
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other person might enjoy it and you sort of get to do more of the work that you enjoy
465
00:28:16,880 --> 00:28:20,880
and less of the work you don't enjoy because that is something someone else enjoys too.
466
00:28:20,880 --> 00:28:25,880
So I like teams where the titles sort of like ambiguous because then you get people more
467
00:28:25,880 --> 00:28:30,920
interested in model building, more interested in business stakeholder engagement.
468
00:28:30,920 --> 00:28:36,320
And yeah, so I'm okay that the titles are sort of wish washy right now because I think
469
00:28:36,320 --> 00:28:40,600
having a balance of interest is really valuable.
470
00:28:40,600 --> 00:28:45,040
I think that actually makes it easier to kind of position not even internally, but within
471
00:28:45,040 --> 00:28:46,680
your own team, right?
472
00:28:46,680 --> 00:28:51,440
Because if your role has a title like that, like you're saying, it makes it easier for
473
00:28:51,440 --> 00:28:56,160
you to just take on more of those responsibilities or just shift a little bit into something
474
00:28:56,160 --> 00:28:58,760
that's also considered that responsibility.
475
00:28:58,760 --> 00:29:03,520
Yeah, and figure out if you even like it, you know, sometimes you want the shiny new
476
00:29:03,520 --> 00:29:08,360
title and you don't and you realize maybe you don't actually enjoy that type of work.
477
00:29:08,360 --> 00:29:12,800
So I think being able to dabble and try and meet people with different interests and skill
478
00:29:12,800 --> 00:29:16,920
sets is a great way to figure out like what you actually want to do.
479
00:29:16,920 --> 00:29:21,160
Yeah, I think putting yourself out there like the ways you've mentioned just to kind of
480
00:29:21,160 --> 00:29:26,960
learn what's out there, how they look in different industries, how the same role might differ
481
00:29:26,960 --> 00:29:31,520
by responsibility just across teams, across industries, all sorts of things like that.
482
00:29:31,520 --> 00:29:37,220
There is a lot of variation out there and until you know it's there and you see it,
483
00:29:37,220 --> 00:29:41,320
you kind of don't really know what area you want to be, you want to focus on.
484
00:29:41,320 --> 00:29:43,080
Yeah, yeah, for sure.
485
00:29:43,080 --> 00:29:46,840
I mean, there's been so much variation between all of my roles.
486
00:29:46,840 --> 00:29:50,560
It is crazy how many different things you can do out there.
487
00:29:50,560 --> 00:29:55,060
Yeah, and speaking of putting yourself out there, one of the ways that you have done
488
00:29:55,060 --> 00:29:58,400
it in the last couple of years is getting started with public speaking.
489
00:29:58,400 --> 00:30:01,080
So how and why did you get started with that?
490
00:30:01,080 --> 00:30:04,760
Yeah, public speaking is a great way to get yourself out there.
491
00:30:04,760 --> 00:30:08,560
You get to go to conferences and meet a ton of people.
492
00:30:08,560 --> 00:30:13,640
So I actually started public speaking because I attended a conference as a participant.
493
00:30:13,640 --> 00:30:17,760
The conference is called Measure Camp and the whole idea of Measure Camp is there's
494
00:30:17,760 --> 00:30:22,880
no agenda until the conference starts and anyone can sign up to speak as long as there
495
00:30:22,880 --> 00:30:27,680
is an open time slot and an open room or video room since it was remote available.
496
00:30:27,680 --> 00:30:32,400
I was not planning on speaking, just planning on meeting people and I noticed a topic about
497
00:30:32,400 --> 00:30:38,680
halfway through the day that Marissa Goldsmith put on the agenda and it was to lead a discussion
498
00:30:38,680 --> 00:30:45,120
about why 50% of the participants at the conference were women, but only 20% of the people who
499
00:30:45,120 --> 00:30:49,560
signed up to speak were, despite anyone being able to sign up at any point.
500
00:30:49,560 --> 00:30:53,360
Right before that, I grabbed a couple of books that I knew statistics from, I pulled them
501
00:30:53,360 --> 00:30:54,360
out.
502
00:30:54,360 --> 00:30:59,000
During that conversation, we talked about data, we talked about why each one of us chose
503
00:30:59,000 --> 00:31:04,720
not to speak, and we talked about what things the conference or we could do for each other
504
00:31:04,720 --> 00:31:08,740
to encourage more women to speak at future events.
505
00:31:08,740 --> 00:31:13,320
And there I opened my mouth and I gave an open commitment that I would speak at the
506
00:31:13,320 --> 00:31:19,040
next conference that I was able to, which I thought no one would remember.
507
00:31:19,040 --> 00:31:20,560
It turns out that's not true.
508
00:31:20,560 --> 00:31:25,440
Dylan Lewis remembered and he messaged me a couple months later and encouraged me to
509
00:31:25,440 --> 00:31:28,120
apply to speak at a conference.
510
00:31:28,120 --> 00:31:33,880
I came up with so many excuses, but after his relentless encouragement and a few sleepless
511
00:31:33,880 --> 00:31:38,400
nights from anxiety, I applied to speak and I got it.
512
00:31:38,400 --> 00:31:42,360
And after that talk, I was approached by someone else to speak at their conference and I've
513
00:31:42,360 --> 00:31:48,120
just been speaking more and more and my confidence has grown tremendously.
514
00:31:48,120 --> 00:31:53,200
And as a shameless plug, I will be speaking at the Marketing Analytics Summit in Las Vegas
515
00:31:53,200 --> 00:31:56,160
in June, June 19th through the 22nd.
516
00:31:56,160 --> 00:31:58,920
So please check it out.
517
00:31:58,920 --> 00:32:02,720
We will absolutely link that so people can check it out and hopefully attend.
518
00:32:02,720 --> 00:32:06,800
I do want to say that Measure Camp is absolutely so fun.
519
00:32:06,800 --> 00:32:08,880
And if you ever, it's awesome.
520
00:32:08,880 --> 00:32:11,960
If you ever have a chance to attend, it's free.
521
00:32:11,960 --> 00:32:16,400
It's like she said, you sign up the day of, it's almost like a bunch of lightning talks
522
00:32:16,400 --> 00:32:17,560
that are full length talks.
523
00:32:17,560 --> 00:32:20,880
You sign up the day of, you can speak on any topic.
524
00:32:20,880 --> 00:32:24,580
You don't know who's going to be there speaking on what until you get there and you see the
525
00:32:24,580 --> 00:32:26,140
board all filled out.
526
00:32:26,140 --> 00:32:31,900
It's a really fun experience and it's a very, I feel like low stress way to start public
527
00:32:31,900 --> 00:32:33,560
speaking just because.
528
00:32:33,560 --> 00:32:34,560
Yeah.
529
00:32:34,560 --> 00:32:35,560
Yeah.
530
00:32:35,560 --> 00:32:38,480
And that's when I thought I was going to get my first talk because I was like, oh, that's
531
00:32:38,480 --> 00:32:39,520
so low stress.
532
00:32:39,520 --> 00:32:44,840
And the talks, like some of them are really big speakers who talk at conferences, who
533
00:32:44,840 --> 00:32:47,640
use it as a way to like pitch it or try a new talk.
534
00:32:47,640 --> 00:32:50,680
And other talks are just like, you know, small round discussions.
535
00:32:50,680 --> 00:32:54,960
They usually do like a data therapy session where like everyone just like complains about
536
00:32:54,960 --> 00:32:55,960
work.
537
00:32:55,960 --> 00:33:00,480
I mean, there's some like really chill ones, some really technical ones, but it's just
538
00:33:00,480 --> 00:33:03,600
a great way to meet other data people.
539
00:33:03,600 --> 00:33:08,420
It really attracts the data enthusiasts because it's really low pressure.
540
00:33:08,420 --> 00:33:09,420
It's free.
541
00:33:09,420 --> 00:33:10,420
You don't know what's going on.
542
00:33:10,420 --> 00:33:12,340
You don't know who's speaking in advance.
543
00:33:12,340 --> 00:33:14,580
You're not paying for a ticket to a conference.
544
00:33:14,580 --> 00:33:16,120
It's usually on a Saturday.
545
00:33:16,120 --> 00:33:21,040
So I feel like the people you meet there truly want to be there, truly want to learn and
546
00:33:21,040 --> 00:33:26,200
want to just kind of be able to chat and meet other people who are in data and analytics
547
00:33:26,200 --> 00:33:28,600
and just kind of want to have a good time.
548
00:33:28,600 --> 00:33:29,600
Yeah.
549
00:33:29,600 --> 00:33:30,600
Yeah.
550
00:33:30,600 --> 00:33:33,560
I will say also, I've never attended a bad conference.
551
00:33:33,560 --> 00:33:37,240
MeasureCamp is particularly fun, but if there isn't one in your area where you can't make
552
00:33:37,240 --> 00:33:42,000
a Saturday work, like go out there and go to a conference because you're going to meet
553
00:33:42,000 --> 00:33:44,200
amazing people.
554
00:33:44,200 --> 00:33:45,200
That is absolutely true.
555
00:33:45,200 --> 00:33:51,080
The best part of conferences are often networking, especially if it's in an area you don't usually
556
00:33:51,080 --> 00:33:54,600
find yourself in, if it's not near where you live or if you're meeting people that are
557
00:33:54,600 --> 00:33:57,400
coming from different areas that you wouldn't meet typically.
558
00:33:57,400 --> 00:34:01,440
It's just a really good way to enhance your perspective and meet other people who make
559
00:34:01,440 --> 00:34:05,160
you better just by being able to teach you new things.
560
00:34:05,160 --> 00:34:06,160
Yeah.
561
00:34:06,160 --> 00:34:11,560
I think also with the pandemic, a lot of conferences have gone online or hybrid.
562
00:34:11,560 --> 00:34:15,680
It's a great opportunity to learn more things and see more presentations.
563
00:34:15,680 --> 00:34:20,960
They're a little harder to network, but honestly, it is the best part about conferences is being
564
00:34:20,960 --> 00:34:24,020
able to meet so many new people.
565
00:34:24,020 --> 00:34:30,120
When I attend virtual conferences, I put a conscious effort into participating in the
566
00:34:30,120 --> 00:34:36,360
chat or the Slack channel or emailing speakers afterward that I really connected with to
567
00:34:36,360 --> 00:34:39,680
let them know because the content's great.
568
00:34:39,680 --> 00:34:40,680
Don't get me wrong.
569
00:34:40,680 --> 00:34:44,560
I've learned so much from conferences, but it's really those connections and discussions
570
00:34:44,560 --> 00:34:48,400
about the conferences that last a lot longer.
571
00:34:48,400 --> 00:34:49,800
Virtual conferences are good too.
572
00:34:49,800 --> 00:34:55,760
Just know that it takes effort to build those relationships from attending those as well.
573
00:34:55,760 --> 00:34:56,760
That's absolutely true.
574
00:34:56,760 --> 00:34:59,120
I feel like you're very good at networking.
575
00:34:59,120 --> 00:35:03,820
I know you have an article on networking one-on-one, so I'm definitely going to link that as well
576
00:35:03,820 --> 00:35:09,080
because the tips you said just during this podcast are already amazing, but I know you
577
00:35:09,080 --> 00:35:14,600
have more amazing tips in that article that people can definitely benefit from.
578
00:35:14,600 --> 00:35:15,920
Networking is hard.
579
00:35:15,920 --> 00:35:19,520
It's a skill that I have practiced over time.
580
00:35:19,520 --> 00:35:23,200
I'm always working on it, but people make it look easy.
581
00:35:23,200 --> 00:35:24,200
It's not.
582
00:35:24,200 --> 00:35:25,200
It's hard.
583
00:35:25,200 --> 00:35:26,200
Put effort into it.
584
00:35:26,200 --> 00:35:29,040
I swear it'll pay off.
585
00:35:29,040 --> 00:35:31,360
It's a tremendous resource.
586
00:35:31,360 --> 00:35:37,160
Speaking of resources, I like to ask everyone, what is one resource that you feel has helped
587
00:35:37,160 --> 00:35:40,800
you in your career that you think might help others who are listening?
588
00:35:40,800 --> 00:35:46,680
I'm going to sound like a broken record, but I think building your network of people is
589
00:35:46,680 --> 00:35:50,300
the most important thing because people help you with so much.
590
00:35:50,300 --> 00:35:55,400
They help you with finding a new job, figuring out the best resources to solve a problem,
591
00:35:55,400 --> 00:35:58,240
telling you about conferences, and just fun to talk to.
592
00:35:58,240 --> 00:36:03,560
I've personally found my data people through Slack communities, specifically the Measure
593
00:36:03,560 --> 00:36:09,040
Chat community, the Data Angels Slack community, and the GTO MSA Slack community.
594
00:36:09,040 --> 00:36:14,840
But there's other Slack places like Locally Optimistic, DBT Slack, or there's communities
595
00:36:14,840 --> 00:36:17,880
on Discord, LinkedIn, Medium, Twitter.
596
00:36:17,880 --> 00:36:19,920
I'm sure other places I haven't even heard of.
597
00:36:19,920 --> 00:36:24,440
I think there's Data TikTok now, which I am not a part of, but it exists.
598
00:36:24,440 --> 00:36:28,840
So, don't feel like you have to move places or put a lot of effort into it.
599
00:36:28,840 --> 00:36:32,600
Find your community where you spend time on the internet.
600
00:36:32,600 --> 00:36:37,920
There are absolutely data communities everywhere, and for almost every niche you can probably
601
00:36:37,920 --> 00:36:40,360
imagine within data and analytics.
602
00:36:40,360 --> 00:36:43,200
Like you said, you don't have to find those in person.
603
00:36:43,200 --> 00:36:46,680
If you aren't somewhere, there are a lot of in-person meetups.
604
00:36:46,680 --> 00:36:51,760
There's online communities that you could have a similar experience with and meet people
605
00:36:51,760 --> 00:36:52,760
through.
606
00:36:52,760 --> 00:36:56,680
So, before we wrap up, how can our listeners keep up with you?
607
00:36:56,680 --> 00:36:59,880
Yeah, you can follow me on LinkedIn or Medium.
608
00:36:59,880 --> 00:37:04,960
I'm also very active on the Measure Chat Slack and the Data Angels Slack community, which
609
00:37:04,960 --> 00:37:08,300
I think we can link in the show notes for how to join.
610
00:37:08,300 --> 00:37:09,300
We absolutely can.
611
00:37:09,300 --> 00:37:11,260
So, thank you so much for joining us, Riesling.
612
00:37:11,260 --> 00:37:12,760
This was a really awesome talk.
613
00:37:12,760 --> 00:37:16,920
I learned a lot, and I know our listeners will also really benefit from listening to
614
00:37:16,920 --> 00:37:18,880
what you've had to say.
615
00:37:18,880 --> 00:37:19,880
Thanks so much, Lauren.
616
00:37:19,880 --> 00:37:41,240
I had a ton of fun chatting.