Lauren Maffeo: Thanks for having me, Nicole. I'm really excited to be here.

Nicole Huesman: Congratulations on the publishing of your book. How exciting! Can you talk a little bit about the book?

Lauren Maffeo: Sure. So, I published this book, Designing Data Governance from the Ground Up, with the Pragmatic Programmers last winter. I've spent a lot of time in my career as an analyst at Gartner covering the cloud BI software market. I also work as a senior service designer with clients to help them build and design human-centered technology. And I realized that the more work I did with data-specific clients, it became increasingly clear to me that there was a real lack of [00:01:00] quality and QA happening at that raw and data level that really didn't translate into clean data. And there's an often-cited statistic that data scientists spend about 80% of their day cleaning data. And whether that's true or not, the sentiment is that the most data that exists today is not fit for use, and it's not being managed in an appropriate way. And so, I wanted to write this book to be a simple 100-page, six-step guide to helping readers think of data governance, not as a technical problem to solve or a compliance issue to avoid, but to think of it as a cultural change, which you can co-create with your colleagues outside the data science team. There's too much data that exists and is ingested today for one team or person to manage it. And I have found that the solution for it is to engage colleagues [00:02:00] across teams to help figure out what data quality looks like across each data domain and work together to define what that looks like in each respective domain.

Nicole Huesman: I remember a talk that you gave about unconscious bias in AI data sets and the unintended or unanticipated consequences. How does this bias or these bias issues manifest in the context of data governance?

Lauren Maffeo: Sure. So, when I talk about unconscious bias in AI datasets, it's, you're talking about indirect bias. So indirect bias is what happens when there are sensitive attributes that correlate with byproducts of non-sensitive attributes. So, a sensitive attribute is anything like a, like your age, gender, date of birth, basically any data point in [00:03:00] which it's illegal to discriminate against somebody based on those sensitive attributes, and a non-sensitive attribute is something like your location, your address. And what happens oftentimes in training these data sets is that sensitive attributes will correlate with non-sensitive attributes in unforeseen ways.

So, one example of that is that there can be a higher correlation of skin color with zip codes unintentionally, and the end result of that is that people with who have darker skin or are black are denied home loans or being able to access housing in particular zip codes because they're deemed less likely to qualify for loans. It's a drastic oversimplification of how this occurs, but that is one example of many.

Another issue that we often [00:04:00] see is that older data sets are used in training existing models. So one example of that, going back to housing, is that redlining in the state of Oregon has not been legal for many decades at this point, but it was legal for, I think, over a century. And so any algorithms which are trained on that data are trained using data that is relevant to a practice that is now illegal in the present time. And so, if those sorts of you know, considerations have not been taken into account, that's how you can end up with a lot of issues related to the data that you have today.

Nicole Huesman: Thank you so much for really providing us with that detail and with examples of what really is happening and what we need to be aware of. Can you talk about the publishing process and some of the, maybe [00:05:00] some of the high points and some of the challenges that you experienced along the way?

Lauren Maffeo: Sure. So, the idea for this book came about at All Things Open 2019 when I was talking to a friend of mine and an editor at the Pragmatic Programmers about publishing books. And then during COVID, I had more time on my hands because I wasn't going out as often really ever. And so I reached out to Brian McDonald, who at the time was still an editor with PragProg, to ask if the idea that I had for a book on designing data governance programs would interest him and his publisher. So long story short, I ended up drafting a sample chapter of the book for consideration by the editorial team. They ended up green-lighting it to be written and published.

I did not get an advance for that book and think that's an important thing to describe because [00:06:00] that's very common when you work with larger publishers to get an advance for your book. You hear about these six-figure numbers that sound really big, but I've learned a lot about the economics of publishing and what that means, and the reality is that if you get a book advance, you do not see any royalties until you make that advance back. So you can get an upfront sum to write the book, but then anything in addition to that is after you have earned in sales what you were paid in your advance. Whereas, the way that the Pragmatic Programmers does it is they give you up to 50% royalties on each copy sold, and it actually works in your favor to have e-copies and digital copies of your book sold rather than working with the print versions because there are higher costs associated with those print versions. So that's something that I think people should [00:07:00] be aware of because the economics of publishing are really difficult to pan out and that's why it's really almost impossible, I would say, unless you have a windfall of some sort, to be a full-time author. Most of us have to write our books alongside working full time. That's what I did. It took me about 18 months to write the full draft of this book. And then once I did, I had to get it read by technical reviewers. I had eight people who read the first draft of the book and then provided me feedback on it, which I incorporated into the final version. So the full process from getting a contract to getting it on shelves was about a 20-month process, I think, and that's for a 100-page book. So, for a larger book, it can take even longer as you can imagine, or on the flip side, you can have shorter deadlines, but you are expected to deliver [00:08:00] more, faster.

Nicole Huesman: So, it sounds like it's a marathon, not a sprint.

So Lauren, I love that you're looking forward into 2024 and thinking about what's next for the book. Can you talk about a little bit about the LinkedIn Learning course that you've published?

Lauren Maffeo: Yeah, the course is available on LinkedIn Learning. It's called Designing Data Governance, and it released to the LinkedIn Learning platform on December 12th, so anybody can go take it. It is based on content from the book, specifically the first three chapters.

Nicole Huesman: One of the things that I think is really beautiful about your story is that you moved from being a journalist and really focused on the liberal arts into a highly technical field like data science. Can you talk about your path? I think that's really exciting in sky's the limit, right?[00:09:00]

Lauren Maffeo: Yes, and I'm always happy to talk about my shift and career path because I want people to know, especially women, that there are many opportunities for them in tech and that have not having a, you know, STEM degree academically, it doesn't prevent you from entering the sector, either in non-technical or technical roles.

So to back up a little bit, I was a media studies major in college. I did my first TV news internships when I was in high school, so I knew pretty young that I wanted to pursue a career in journalism, ideally broadcasting. So I majored in media studies. I went to college in a large city where I would have easy access to different types of internships. I did on-air radio reporting in college, and then I went on to graduate school in London. So I got my B. A. and M. S. C. within 5 years. And [00:10:00] I, even when I was in graduate school, wanted to pursue a career in media. And specifically as a journalist, I was able to do that on a freelance basis for a year after graduating. So I specialized in tech reporting for digital news sites like The Next Web and the Guardian and the beat that I fell into was reporting on European startups and the growing tech sector there, specifically in London. I was still in London at the time after graduating with my Masters from the LSE, and I didn't care at the time which sector I covered. I just wanted experience, so that I could get clips and continue getting work as a journalist, and instead, I realized through both freelance reporting and starting to consult that I actually had a lot of interest in technology and I really enjoyed learning about it. It was very clear to me that this was not just a sector that was really [00:11:00] growing, but one that would allow me to keep learning. And I, that was one of the things that I loved about news was the opportunity to do something new every day and use the same process to have a different experience.

So it became increasingly clear that the journalism sector was destabilizing, which it has only continued to do, and that really drove my decision to try working in tech. I started in content marketing for a Silicon-Valley-based SAS company, since that was the easiest transition from journalism into tech was through marketing and specifically writing content. Eventually, I became a business analyst and a research analyst at Gartner, where I specialized in covering trends in the business intelligence market for small and mid-sized businesses. That's really where I started to specialize in AI and I decided I wanted that to be my beat.

And then I have for the last three and a half years been working as a senior [00:12:00] service designer at Steampunk, which is a human-centered design firm that designs and builds human-centered solutions for the federal government. So now I have gone from being a journalist to being a marketer, analyst, and designer in my tech career. So I've been leapfrogging across different roles, and I don't know exactly where I will be in five years, but I think the leapfrogging is probably going to continue.

Nicole Huesman: And that's exciting, I think, that you don't feel stuck in any particular thing, that you can move to places that speak to you and that you're passionate about.

So being a woman in a technical field is, in and of itself, a challenge. Can you talk about some of the challenges that that has presented along your journey and [00:13:00] how you addressed those challenges?

Lauren Maffeo: So in my case, I mean, I've talked to some women who faced really overt discrimination, and I think in this modern era, that has largely gone by the wayside, and in its place, has been replaced with, for lack of a better phrase, microaggressions. There are several examples I can think of.

One of them is that when I was on a data project at work, one of the data engineers, I would be in a meeting with one of the developers, and then the senior data architect and. one of my colleagues. The senior data architect was wonderful and very affirming and a great partner, but the other guy would literally just not address me in meetings, even though the three of us, or the broader team was collectively talking about something. We're all virtual, but he never made eye contact with me, never addressed me [00:14:00] directly. Even if I had just spoken, even if we were having a conversation about something data-specific, he just wouldn't acknowledge my presence. I don't even think he was necessarily doing that to be rude. I don't think he realized he was even doing it. So that's one example.

Another example is this dichotomy I encounter where I, people are, when I tell them what the book is about, or I tell them what I do, people, just out of turn, get very dismissive about it. If someone asks me, what do you do, which is a question I get a lot, especially living in Washington, DC, I will say I'm a service designer and the first response is often, what is that, and they say it like with that tone. Similarly with the book, if people ask what it's about and I say it's about designing your data governance programs from scratch, I get a very similar out-of-hand response and I get people who say, I don't understand what [00:15:00] that is and that they don't say it with the question of, oh, what is, what does that mean? Even when I say I'm a service designer, sometimes people ask what that means, but typically they'll shut down the conversation before it even starts by saying, I don't understand that. The flip version of that is I have had managers who have said to me, I don't think of you as technical. And so it's this dichotomy of, you are both, at the same time, too technical and not technical. And technical, of course, is never even defined, because people who are making those black-and-white assertions often can't explain what they mean because they don't even understand what they're trying to say.

Those are some pretty recent examples of things that I have encountered. I often feel like I don't belong anywhere because I'm more technical than a lot of [00:16:00] designers and not as technical as, you know, a data engineer, for instance. So I'm in this weird in-between space, which I am happy to occupy and I don't mind, but it seems to make other people uncomfortable sometimes.

Nicole Huesman: That's amazing that you've described it that way because it so resonates with me and where I sit in the world where I've had those same experiences. In one room, I'm technical and can talk about technical things. In the next room, I'm not technical enough, and I can't dive as deeply into technical subjects. For me, it's been around being technical enough to understand, at least at a high level, and some people would, you know, in that first room would actually say you actually understand it far more than they [00:17:00] do, but having sufficient knowledge to describe why it is what they are doing, the more technical folks, right? Lines of code, for example, writing lines of code. Why is that important? Why should the rest of the world care about that, right?

Lauren Maffeo: The distinction you make is that I know my limits. I am not a data scientist. I don't have the very advanced backgrounds in math that you need in order to be a data scientist. I don't have that capability. I'm also not a builder. I don't build, you know, data pipelines, things like that. But I know I have enough technical acumen to be considered a data architect. And very often I am on data projects where we are designing data infrastructure, whether it's a data lake house, designing a data mesh environment, things like that. And I'm the person designing the blueprints for what the data [00:18:00] engineers ultimately build. I feel confident being in all the meetings with the data engineers and scientists and I know the subject very well, but I am not the person who is building it or the person who is deploying models themselves. So I definitely have a role in the process and I do acknowledge it's an important one, but I also acknowledge what I can't do and what isn't within my sphere.

Nicole Huesman: What has been your guidepost or your secret to navigating those situations?

Lauren Maffeo: My secret has been that you can't put all your eggs in one basket. You have to have a diverse network, a diverse array of opportunities, a large amount of people to rely on, because anytime that I have over-relied on too much on one person or organization, it failed me. And [00:19:00] so I don't believe in holding one person's opinion in absolute regard above everyone else's. That is not to say that I'm not open to feedback because I am and I'm very well aware that I'm far from perfect, but I think experiences like the ones I've already described are all the more reason to expand, not just your education and opportunities, but also your network because you need people that you can trust who will tell you when you're falling short and truly have your best interests at heart versus people who just don't respect you enough to give you the consideration that you deserve.

Nicole Huesman: For those who are following after us, right, this next generation, what are some of the key pieces of advice that you would provide to them?

Lauren Maffeo: Well, I think we're at an exciting time in [00:20:00] education where there are more interdisciplinary courses for people who are both computer science majors and liberal arts majors to learn more about both subjects. And this is a small field, but it's rapidly growing with student demand for the for these educational skills. And I, I really that is this is the future is blending tech with liberal arts in a way that education historically has not.

Part of the reason I was a liberal arts major is, yes, I loved the subjects that you study more, but I also just didn't think of tech as something that was for me or applicable to me, and that really has more to do with the way those subjects were taught than my aptitude for them. And so I see that changing now in a way that's very exciting.

Even if students are not pursuing traditional education, there is so much free [00:21:00] content available online. I know many developers personally who are fully self-taught. They also got degrees in communications or liberal arts, which they do not regret, but they taught themselves to code on the side while they were also working full-time jobs. And so I feel like, especially in the tech world, many of us get a lot of fulfillment out of teaching. And that's something I love about the sector is that people in open source and in tech are very generous with their expertise because they want to help other people learn. And personally, on a selfish level, it helps me get better at my job. I learn things by reading about them and then having to write about them or speak on them. And so when I do that, it helps me learn my subject in more detail than I did before. So the end result of that is that we have more content out there online and in classrooms than ever before.

And so [00:22:00] I think the opportunities to harness that for the next wave of technology jobs is going to be really essential, especially as we see AI start to automate a lot more tasks. It is going to be even more important, not only that you know how to use various AI techniques, but that you really polish your non-automatable skills because those cannot be replaced.

Nicole Huesman: Absolutely. So as you look back over your career and your body of work, of all of the different experiences you've had and navigated, what's been the most rewarding to you or what are you proudest of?

Lauren Maffeo: I think I am the most proud of writing the book and launching the LinkedIn Learning course. I have always been interested in writing. I always thought I was going to do it as a journalist and then things changed, but [00:23:00] inherently, whatever I've done in tech, I do feel like I'm a writer at heart and I don't want to lose that about myself.

I don't think I'm done with the book yet either. I do foresee a second edition coming down the pike. This field of data governance is expanding so much and changing and there is a lot of ground that was not addressed in this book that feels unresolved to me. And so I do foresee continuing to update it and keep it fresh for additional readers. And I really hope that it will be an evergreen book that people can pick up and use in five years, as opposed to being a book about a very trendy programming language that is no longer relevant in five years.

And then I really did love creating the LinkedIn Learning course as somebody who lived and worked and interned in TV news studios. Being with LinkedIn was such a great opportunity because they have [00:24:00] top-notch production teams and they, it really did feel like being in an old-school newsroom when I got to record the course. And so doing that was also a huge thrill for me because it reminded me of my days when I worked in broadcasting.

Nicole Huesman: Oh, sweet. That's cool. Yeah. And so you obviously had the opportunity to leverage that earlier experience you had, and apply it to this new and very quickly evolving world of data science. That's fantastic.

Lauren, how do you define success and has that definition changed over time?

Lauren Maffeo: So I do tend to be somebody, it's funny as a liberal arts major, I tend to be somebody who tries to quantify things. And I would both say I'm successful and I sometimes think about ways in which I have not [00:25:00] succeeded to the degree that I would like. I don't know if my definition has changed as much as my expectations for what success will look like for me because I have both achieved things that I didn't think I could, and I have not achieved things that I thought I would. So I think it's more about readjusting my own expectations of what success will look like for me.

What do you think are maybe three things that you've learned along the way? So one of them is it's totally normal to not have a long career at the same place. I put a lot of pressure on myself in my twenties to stay somewhere a really long time. I always envisioned myself working at a company for five to 10 years. I think there is a more honest conversation happening now about the tenure that people have and I've come to realize that staying somewhere for five [00:26:00] to 10 years like my parents did is just not only is it not relevant, it's not possible in many cases in today's economy, especially with the way the tech sector has been in the last year. So, having to readjust expectations there has been freeing because it takes away that pressure to be somewhere for a long time, purely for its own sake.

I've also learned that you can pivot if you're strategic about it, that the skills you gain by studying, let's say, media studies are applicable outside of directly studying media. You definitely have to be strategic about it, and you need to have a sense of where you're going, but you can also apply what you learn outside of your very narrow sphere.

And it's also a great time to define what it is that you like to do in terms of devoting more time to it. And what I mean by that is you [00:27:00] can take a job and really do your best at it and use it as a space to not only gain new skills, but also to save, and have the freedom to maybe save up money and do something more creative later on. And so rather than just looking for towards retirement, you can actually save for a future that allows you to pursue work which you maybe couldn't have done as readily earlier in your career.

Nicole Huesman: Can you talk about mentors or advocates or inspirations, things that have inspired you as you've traveled your path, made some different pivots and gotten to where you are today?

Lauren Maffeo: Broadly speaking, the open source community has been a huge source of inspiration. I first went to the Open Source Summit in Vancouver five years ago in [00:28:00] August of 2018, and I went because A) I love Vancouver, but B) to gain speaking experience, not thinking that I would become part of the broader open source community. And I did. And the fact that this book that I just wrote was really born at an open source conference, it was published by an open source publisher, I have continued to contribute to various open source projects and attend those events -- and it is not related to my day job, my day job does not have anything to do with the open source space other than utilizing open source technology, which every organization does today -- but I really developed this passion and interest in open source, which is spearheaded by people in the community like you. I would love to make it a more of a focal point of my career in the future, whether that's leading an open source program office, exploring open source [00:29:00] AI, helping to bring open source into classrooms and getting students to contribute to open source projects. That has been a big part of both my career and life over the last five years. And in terms of mentorship, it gives me a crucial outlet for that, which I don't always have in my day job. So I'm very grateful for that. It is.

Nicole Huesman: Wow. And, you know, I think that event in 2018, I think that's where we met for the first time. Yeah, yeah. Oh, great memories. So you've talked a little bit about what's next, what you're looking forward to in 2024. Are there any other things that you'd like to mention or that you're, you know, most excited about?

Lauren Maffeo: The main thing I would just love to mention is for folks to check out the book because I am still [00:30:00] really in the thick of promoting it and I'm promoting it without an agent and as a first time author. Any reviews on sites like Amazon or Goodreads make an enormous difference in helping my book get discovered by the people who need it. So the main thing is thing I would love to ask people to do is to purchase a copy of the book in any format from any publisher and then write a review of it on Amazon or Goodreads, because again, those reviews are a huge tool towards helping books get found, and the more reviews your book has, the more it is likely to surface in relevant search results.

Nicole Huesman: Absolutely. And we will definitely put a link to it in the show notes, so listeners, look for that as well. As we close, I'd love to get your take on what She Lights the Way means to you.

Lauren Maffeo: It means continuing down a path [00:31:00] that others have started and passing a flashlight back so that others can follow you like you are following people that came before you.

Nicole Huesman: Lauren, I cannot tell you how excited I am to have you here today and to talk about what you do, your book. And I'm so excited for your, to just look at what you do in 2024 and to have you back on the program at some point. It's been a pleasure to, to talk to you today.

Lauren Maffeo: Likewise. Thanks so much, Nicole. And I would love to be back. So just say when and I will come back anytime.