Data Science Hangout | Namrata Shetty-Anderson, UPS Store | Professional development for data teams

Rachael Dempsey. Happy Thursday, everybody. Welcome to the Data Science Hangout. I hope everybody's having a great week. If it is your first time joining us today, it's very nice to meet you. I'm Rachael. I lead our pro customer community here at Posit. This is an open space to chat about data science leadership, questions you're facing, and getting to hear about what's going on in the world of data across all different industries and companies. And so every week, we feature a different data science leader as my co host for the day, to help lead our discussion and answer questions from you all.

But together, we're all dedicated to making this a welcoming environment for everybody. So we love hearing from everyone, no matter your level of experience or area of work. It is totally okay to just listen in to Hangouts too. But we do want to hear from you as well. So there's three ways you can ask questions, or also provide your own perspective. So you can jump in by raising your hand on Zoom. You could put questions into the Zoom chat. We also have a Slido link where you can ask questions anonymously.

We do share the recordings of each session up to the Posit YouTube. So you can always go back and rewatch or share with a friend too. With that, thank you so much, Namrata, for joining us as the co-host today. Namrata Shetty-Anderson is Advanced Analytics Manager at the UPS Store. And I'd love to have you maybe start by introducing yourself and sharing a little bit about your work. Maybe something you like to do outside of work as well.

Sure. Hi, everyone. Really happy to be here. I was really happy when Rachel reached out. This is my first data science Hangout as a featured leader. I've attended a few as one of the attendees, and I think that's where Rachel found me. As she pointed out, I'm Namrata Shetty-Anderson. I know my name is difficult to pronounce, so you can call me Ratty. That's what everyone calls me. I'm cool with it. I'm an Advanced Analytics Manager at the UPS Store. I've been with the company for about five years now. Actually, almost six years now. And I lead a team of six analysts, and together we do everything data. We're the data nerds of the organization. And anything that has anything to do with data goes through us, whether it's bringing data on premise, whether it's building reports, dashboards, predictive analytics, machine learning, wrangling, cleaning, everything about data for this organization goes through us.

In my free time, I'm boring. So in my free time, I'm either playing with data. My husband has a PS5. He plays, you know, Call of Duty or Last of Us or what have you. And I'm playing with random data sets online, trying to learn new things. Or I'm with my dogs. I have two big dogs and a cat. And they take up most of my time. So I'm either with my dogs and my husband and my cat or I'm with data. So that's pretty much all about me.

The art of data storytelling

Sure. So, um, this is something that, you know, I've actually struggled with in the past, I'll say, this is something that I learned was an area of improvement for me. So over the last few years, I've really focused on this area. So definitely, I can, I can tell you what I know. In my mind, data science is as much a science as it is an art, you know, to be able to tell your story, you could write hundreds of lines of code, and you could build amazing models. But at the end of the day, if you can't translate it to a non data person as a data story, all that is a waste, right? You're not adding any value. Businesses don't care about your code, right? My boss doesn't care about my hundreds of lines of code, he wants value.

In my mind, data science is as much a science as it is an art, you know, to be able to tell your story, you could write hundreds of lines of code, and you could build amazing models. But at the end of the day, if you can't translate it to a non data person as a data story, all that is a waste, right? You're not adding any value.

I call it the art of data stories, right? It's telling your data story, that's an art. So one thing I started doing, and I actually I took a course by Kevin Hartman from Google, which talks about, you know, how data storytelling is an art. And one thing I learned from that course is, you want to start with a solution for the problem in a tool agnostic environment, you know, don't start with your tool when the problem is posed in front of you. Because what happens is the tool is going to limit your ability to tell your story, right? If I say I'm going to use Power BI, then I'm going to be limited to the visuals that are available in Power BI, right?

So what I started doing, literally, blank page, and a pen, what do I want my outcome to look like? And once I know what that story is going to look like on a blank sheet of paper, then I decide what tool I'm going to use. Now the tool is going to depend on how quickly do I need to turn this around? How complex is this task? And how good am I with this tool? Right, so those are the things you need to look at. So everyone's, you know, oh, Python's better than R, R is better than Python. No, Power BI is the way to go. Tableau is the way to go. No, everybody's right, and everybody's wrong. Your tool of choice will depend on the problem you're trying to solve. So that is what I'm going to say.

I really love that perspective. I've thought about that before, too, even when you're making presentations, like sometimes if you jump straight into the slides, you become attached to them. You don't want to give them up, even if it wasn't the right way to communicate it. Yeah, and that's the thing. Don't fall in love with anything you're doing. Don't fall in love with it, because that's what the data world is. Today you're doing it one way. You're going to find a better way to do it as you keep learning. There's always a better way.

Journey to leadership

Oh, that's a cool one. All right. So I, as much as I may not seem like one, I am not a very people oriented person. I happen to be an introvert and the only reason I'm able to talk to you guys so well is because you're on a screen and not in the same room as me, so it helps. So leadership was not something that was on my radar that I didn't think I was, you know, good for. But I started out as, when I joined the UPS store, I started in a, you know, low leadership level position where I had two analysts reporting to me.

And I was part of the same team I am today. I think the key factor towards me moving to the leadership role was the fact that I can communicate my data story, right? In the data world, if you want to make it to a leadership role, even if you don't want to, and if you somehow make it there, the driving factor is going to be your ability to tell stories with your data. And two is to build a level of confidence in the organization, you know, because let's face it, you're going to come up with amazing data stories. And if they don't, you know, tell a story that people want to hear, it's going to be hard to convince them to, you know, still do what the data is telling them to do. If it's counterintuitive, right?

Because a lot of businesses have, you know, years and years of experience, your subject matter experts are going to use gut feeling or what I like to call just, you know, their intuition, which is right sometimes, but sometimes it's not. So I think a big factor, at least in my success in my current role was the fact that I was able to build better confidence in our data. And the way I did that was, the first thing we did was we have, we had data sitting in so many different databases, Excel files, what have you, we started working towards one source of the truth, right? We're still not there, you know, five years in, I'm telling you, we're still not there. And it's a long, long journey there. But at least we're working towards improving your data quality.

Before, five years ago, the way it was done was, if we had a forecast, we would always tone it down. Because we were afraid what if the model's wrong, right? And our model wasn't great. Over the course of time, we built a model that was so strong and robust that eventually we just let the model. Today, I don't even tell people. It's just there. It's sitting there. People can go look at it and be like, oh, okay, we're going to make plan or we're not, right? So it was a journey to get to a point where people have confidence in the data.

Of course, the other aspect of it is managing people, which I have learned through experience as, you know, a type A who's like, you know, striving to get everything perfect. I do struggle with, you know, balancing empathy with accountability. But I think experience teaches you that as long as you're open to learning and realizing that not, you know, you have to change your ways, you can't manage everyone the same way. So the path to managing people is you just learn with experience is how I'll say it.

Professional development for data teams

That's a great question. You know, I've been in this leader position for two years now, and I am so proud of how far this team has come. I have to say, you know, I can't take all the credit and I won't take any credit. But I am very glad that I have a very good team. So part of it is because they're awesome, right? The second half of it is I've been trying to like, focus on so look, being in data science, managing a data science team is different than managing, you know, a team that's operations driven or a team that's product driven, or even a team that's a software engineer team, right? It's different because problems that come to our data science team are not always well defined. And the solution is not a cookie cutter. There's no right answer. There's no wrong answer, right? You have the research oriented approach to what we do.

So what you need is that drive to keep learning. And not everybody has that drive. So that is something that we've been kind of working on as a team. So here's what we do. We have been doing. We have, my whole team has, you know, the option of picking their learning platform. Some will pick Coursera, some are going to pick, you know, I want to attend a virtual summit by Power BI, or I want to buy this book, whatever. You tell me what you what your best learning approach is. And you can have it, right? And the team is also encouraged to spend like block off time on your calendar, you know, x hours a week, or x hours a month, whatever you like, to focus on learning, and put it on your calendar, no one's gonna bother you.

The other thing I started doing is every year, around the middle of the year, we have kind of a week long boot camp, as I call it, where we bring in a certified, so we've been doing R, I've been focusing, my team has been focusing on R for the last two years, at least. So what we had as a certified instructor come in, and just work on, you know, little projects with the team. So we had like a week long boot camp, or the team was not bothered by anyone, just to focus on learning.

And then the third thing, and I think this is where it kind of closes the gap, is you can learn everything online, you can learn from instructors, but then when you have to apply it in the real world, it's not, you know, a one to one translation, real world data is not normally distributed, real world data has noise in it, you know, it doesn't work. So what we started doing is we started having monthly sessions. One, we started out with best practice sessions, where one of the team members would say, hey, look, this is a project I worked on, and here's how I approach the problem. The other thing we started doing more recently, is we said, you know what, we're kind of maxing out on our best practice sessions, we have nothing to share at some point.

So we started doing a micro assignment. So I have a, I have a very, I don't know if she's on the call today, but she was going to join. My team member Liz, she's the supervisor of the group, or part of the group, she puts together micro assignments for the team with real work data, like, hey, look at these online reviews, look at this customer data, or look at our sales data. And perform these operations in R. Because this year, our last year, our goal was R. And the team would do those assignments. A month later, when she gives them a new assignment, we meet to discuss how everyone approached their assignment. And you learn a lot. You know, you learn more when you make mistakes, than when you successfully complete an assignment, in my mind.

Two years ago, the same was Power BI, right? So two years ago, our goal was Power BI. Now, we switched to R. We'll pick something for this year, and that's how we've been doing it for the team. I love that idea of micro assignments, and really, really like the emphasis on learning, and kind of letting people drive a path that they're interested in.

The other thing that, you know, because my team does so many things, we have predictive, we have descriptive, we have data engineering, like a little bit of data engineering, not as much. We also put together like a central kind of repository, where we have reference guides, best practices, a lot of documentation. To the point where today, someone new joins my team, all I'm going to tell them is, hey, look, you're going to spend your first week just reading everything that's on that repository, and then ask questions.

So I think documentation, process documentation plays a very important role, and allows us to kind of cover for each other, right? Because we want to rely on processes that make our people's lives easier, as opposed to saying, oh, hey, Matt's out of office today, so this work's not going to get done. And then setting expectations is, you just have to realize that you may learn something, and you may not get a chance to apply it in the real world, because organizations are not always ready for, you know, machine learning. They're not ready. They're not there yet to use these fun, cool things and data. You still have to clean the data. You have to do the groundwork. You have to do the non-fun aspects to get to the fun aspects.

But don't wait for someone to come ask you to apply it. You know where the data is. You know what you've learned. If you really want to apply it, go apply it. And if you can build something that's useful and adds value, hey, I'll make sure the organization uses it. So that's how I approach that.

Subtracting to add value

And one other thing I would also add there, Eric, I definitely recommend this book. It's called Subtract, especially if any of you are on the path to that leadership path in the data science world. I would highly recommend the book Subtract because it's not always about what you need to do. It's also about what you need to stop, right? There was a time my team was building reports week after week, sending them out. We're spending all this time, and we didn't know what those reports were being used for.

This was a recommendation. This is something I learned from our CEO. She recommended this book to me. She's like, look, don't send those reports. Just stop and see how many people come knocking at your door for that report. And that's exactly what I did to the point where now I have a policy in my team where if we think something is not being used the way it should be, we put it on what we call a 90-day pause list. We don't tell anybody. We put it on a 90-day pause and see how many people come banging on our door. Sometimes they do. We put it back up. We're like, okay, sorry, we were just testing. And sometimes they don't. And after 90 days, I just sunset it. We're not doing this anymore. And I can't tell you how much time that has freed up for us to actually focus on things that matter. So subtract is just as important as add.

And I can't tell you how much time that has freed up for us to actually focus on things that matter. So subtract is just as important as add.

Handling failure and knowing your audience

Yeah. So I'll answer the first one later. I'll answer the second one because it's a shorter answer. I read this somewhere. So this is how I look at failure. Personally, if I fail, I'm very hard on myself. And my husband will attest to that. He's had to talk me down a lot. So as put together, as I come across, I can be a total mess sometimes. I'll make one tiny mistake, and I will harp on it all day. Like, oh, my God, how could I do that?

If I do that to myself, you can imagine how I do that to my team sometimes. They might miss something, miss a deadline here and there. And I can get a little naggy about it. But because I know that I do that, I've been consciously working on, okay, mistake has happened, failure has occurred. Focus on what is the impact, right? At the end of the day, we're not saving lives, right? If I make a mistake, nobody died, right? But that's it, right? You have to remind yourself every day, at the end of the day, you're not saving lives. What is the impact of this failure? And if the impact of the failure isn't significant, then the failure is actually a good thing, because you learn from it.

I think for me, because, you know, the people aspect of things is always stressful for me. The data aspect, you know, it's fun. I can do it. It's, you know, when I'm talking to people, I think one thing that I learned from my failures was reading the room. A big thing I've learned is the data story that I'm telling is going to have many different versions. It's going to tell the same story, but it's going to have many different versions depending on who I'm talking to. If I'm talking to a, you know, district manager or a senior vice president person, you know, on UPS or the UPS store side, I am not going to give them like a page-long story, right? I'm not going to give them too much detail. I'm going to cut right to the chase and give them information that matters because they don't need the details, right? If I'm talking to a peer, I may give more information, and if I'm talking to my team, I'm going to go right into the weeds, so I learned this the hard way, but it's knowing your audience, reading the room, and just cater, you know, tailor your story for your audience, and that goes a long way.

Mentorship and looking ahead

Yeah, mentoring, look, I don't consider myself an expert in anything, right? The mindset is I'm always going to keep learning, but as a mentor, I can always help someone who's a few steps behind me on their learning journey, so that's what mentorship is to me. I don't consider myself an expert on anything. I'm just trying to help people who are probably a few steps behind me and tell them what I've learned, and hopefully, it helps them in their journey. So, if that's how you approach mentorship, you can start today. Just, you know, reach out to your network, see if there's anyone that could benefit from your learnings, and voila, you're a mentor.

All right. Books. Let's see. I'm not sure. So, because you're all data people, I'm sure most of you have read it, but I really love the Freakonomics series. I would also recommend, again, this is not specifically for leadership or data, but I love The Undoing Project by Michael Lewis. If you've read Moneyball, it's the same author. So, again, a lot of sports references. My husband had to explain a lot to me, and then Unapologetically Ambitious is another book I loved. It was more for, like, female leadership, but a lot about leadership. Unapologetically Ambitious, Freakonomics, Moneyball, Undoing Project, and, of course, Subtract, because I also mentioned that earlier.

No, I didn't really prepare for this, because I didn't know what to prepare for this. So, this is a lot of fun, really good questions. I really love the energy. I would love to connect with you guys on LinkedIn. You know, if you just want to say hi, drop me a message, I'll accept your connection request. And if you have any more questions for me, just feel free to shoot me messages on LinkedIn. I'm happy to answer them. Thank you. This was really fun for me. Thank you so much. I love the energy that you brought to today, as well. Have a great rest of the day, everybody. You, too. Thank you. Bye, everyone.