There are two types of data influencers on LinkedIn:
1. Those who talk directly about the products and companies they work for
2. Those that provide more general guidance, tips and opinions
Can influencers actually be passionate about the products they’re developing and straightforwardly talk about them without sounding salesly?
Megan is one of those influencers that combine the two approaches, and with almost 100K followers, her content seems to be resonating with many data folks. She talked to the bros about her approach to data advocacy as well as the power of notebooks, especially when they become broader and enable collaboration.
Listen on Spotify or Apple Podcasts
Data bros (00:00.946) All right, perfect. Hi everyone, and welcome back to another episode of the Data Engineering Show. Great to have you with us. Megan, great to have you as our guest today. Megan is a data advocate at Deep Note. We'll learn a lot about Deep Note, I'm sure. She was a data scientist before, kind of then moved into data advocacy, which is awesome. I actually never met a data advocate before, so it's going to be exciting to hear from you today what that's all about. Do you quickly wanna intro yourself, Megan?
Megan (00:30.047) Yeah, great. I am so excited to be here with you guys. My name is Megan, as you guys said. I got started in the data industry, I guess, as a data analyst. After that, moved to being a data scientist and now am a data advocate or another name would be like a developer advocate or DevRel. And so, yeah, I've held a couple different roles in my short time in data and I'm very passionate about talking about my learnings those different roles and also just soaking up knowledge from experts in the field because I acknowledge that I am still very new to this wild and wonderful world of data. So having conversations like these and listening in on podcasts is one of my favorite ways to learn from other professionals in the field. So very excited that I have the opportunity to like sort of pay back the learnings that gotten from podcasts.
Data bros (01:32.234) Sounds awesome. So take us through that journey, right? Like data analyst, to data scientist, to data advocate now. Like what got you into data in the first place?
Megan (01:42.711) Yeah, after I graduated from school, I was, I had studied finance. I had held a couple of internships in finance, and so I jumped into the finance world. I was working at a Big Four consulting firm as a financial analyst, doing mergers and acquisition, valuation and advisory. Sounds fancy, but realized two years in that was not the path for me. And so had to go back to the drawing board What's the... the many years of my future as a working adult would hold for me. And so I thought back to a couple of courses I took in college related to data analytics and data science. Um, at the time, I think those courses were still, uh, very much in the infancy of like a lot of the data science, um, graduate programs that you see offered at big universities now. So it wasn't anything super rigorous or super hardcore, but it was one of the very few, um, skills or courses that I had taken in my time at the University of Virginia. So I was like, you know, I was like, I don't have any other technical skills that I'm interested in developing really. So thought back to those courses and I was like, I guess I know stuff about data. In reality, I was like, I took one SQL course and I thought I knew what it took to become a data professional. But... Regardless, dove headfirst into that and haven't looked back since. I think, well, actually I think in the process looking backward and like questioning whether moving into data was the right move or not would have been even scarier than just like...
Megan (03:26.883) blindly going in and just being like, yeah, like this seems cool. Data science, AI is like what everybody's talking about these days. So like for all intents and purposes, why would I look backward? Right. But I'm glad I didn't. And I'm glad I've kind of forged the path that I have had in data, which is like I've held a lot of different roles in the space and so have been exposed and worked with a lot of different types of personas.
that you normally would when you are working with data and learned a lot in the process and am still very much learning.
Data bros (04:02.594) Nice. That's awesome. And you're also huge in terms of just like thought leadership, right? Like you have almost 100,000, I checked earlier, like followers on LinkedIn. I think you'll crack the like six figures there soon, kind of where we're rooting for you.
Megan (04:15.443) It's literally all I want right now. It's like, ah, end of the year, please. But it's not, it's not going to happen by the end of the year. That's okay. It's okay.
Data bros (04:24.371) What got you into that? So at some point you got deeper and deeper into data science, became an expert, you said okay, I want to share knowledge now. What happened there?
Megan (04:34.508) Yeah. I, yeah, first and foremost wanted to share some of the ups and downs of the journey. And it wasn't even like, oh, like I am now an expert and I want to impart my knowledge on people. It was more me finding some situations were funny. Some situations were very relatable. And I've always enjoyed writing. So like what better way to kind of react to those funny situations than just like put writing it down into words and releasing it. And some people were like, oh my gosh, like I experienced that too. And so over time I realized that like, I had this knack of picking up situations that happened to me that are like, I don't know, like maybe I get a feedback that I'm not like, that I'm not. doing as well in a certain area of development. And I was like, I can either take that or I can be like, well, I have some more thoughts around it. And I feel like these thoughts could be very relatable to others. And so that process of me just picking out these takeaways from my career and adding more thought around it has allowed me to relate to a lot of other people who go through those moments too. And so that's how I really started. Just like, I treated it like my daily diary. I do have a physical diary that I write into every day and then I think of my LinkedIn posts as just like a more...
Megan (06:07.959) marketing version of what I write in my daily diary. And yeah, it's just led me to this point where I don't take anything that I write in there super seriously. It's just my musings. But then with my current role as a data advocate at Deep Note, I do kind of have to take that more seriously. So it's been interesting to treat that side hobby of me writing thought leadership just on the side and turning that into a full-time job.
Data bros (06:39.146) Nice. So turning it into a full-time job, right, is like... Last time on the episode we had like Chao Kshu and she also kind of is a kind of thought leader in the space. And she was like, hey, I started writing and kind of suddenly some of my posts went viral. Right. And like, it's actually like, it was very unintentional in a sense. Like she didn't plan out like the three year journey to like become a kind of influential, like kind of thought leader. And like, how was it with you? Like, was this kind of something where in the beginning you were just writing and then some things kind of gained traction or were you like from the beginning very focused on figuring out your time? our good audience and so on.
Megan (07:16.931) Ooh, no, it was definitely not. Solid no, it was not the latter. Like, I don't think anybody, I don't know, I feel like if you go into it thinking, hey, I wanna become an influencer, hey, I wanna get all these followers, it's just not.
Data bros (07:18.358) Hell no!
Megan (07:32.575) gonna work out because you're going to start writing in a way that's not authentic to yourself. You're gonna write in a way that you see other influencers or thought leaders have already been doing and they've already perfected their craft. And so if you were to try to inject yourself into that voice, people can really easily pick up that is not how you normally talk. And also when you constantly write in a way that is not... true or authentic to you, it's just gonna make it so much harder to like consistently be consistent with your content. If you are writing about things that you truly enjoy, that's when it flows and that's when people can pick up that you are doing it because you enjoy it rather than like chasing some external goal. So I started with it, like I think my first post was like, oh, I finished this like data
Data bros (08:14.734) That's good.
Megan (08:30.465) and it got like five likes or something. And then I one day wrote a post about, I don't know, like debugging code at midnight or something. And I released that post almost exactly two years ago to this date. And then I woke up the next morning and it had like thousands of likes. And I was like, oh, I didn't know that this could happen. And so...
Data bros (08:52.348) Mmm.
Megan (08:55.015) I, for a while after that, I thought that like all my posts would pop off just like that one. And so I was just setting myself up for disappointment, obviously, because that's just not how the LinkedIn algorithm works. And so for a while in the beginning of my writing journey, it was very much me chasing that feeling of hitting virality. But that's just such an unsustainable model motivator. So over time, as I became more consistent, it was like less of those huge spikes, but more like the tiny ones that were enough to keep me going. And then, um... you write enough and you like hit another one of those big spikes. But what I realized in retrospect is that like most of the followers or like most of the traction I gained is not from those huge spikes, but it's from the cumulative growth of like those smaller spikes, but you will never hit those smaller spikes unless you are actually consistent in the first place. So yeah, long story short, I don't. I don't think that anybody can really go and be like, I'm going to, I have this like picture perfect plan of what my influorship will look like. It's just not gonna turn out that way. And so I think my advice for anybody is just like, just take it day by day and like see where it takes you because it could open a lot of doors that you never expect. But if you had your sights set on like one very specific goal, those opportunities could totally pass you by.
Data bros (10:27.882) Yeah, so that makes perfect sense. And I think it's a consistent theme we had across the podcast. Like I think Zach said very similar things about how he got started and kind of, yeah, just finding your voice. That's great. So what's on your mind these days, right? So what are you using your voice for? What are the big things on your mind at the moment?
Megan (10:41.099) Yeah. Love, Zach.
Megan (10:50.435) Yeah, obviously to do my day job as a data advocate, it's a very different way of writing and creating content. From when I was doing it as a side hobby. When I was doing it as a side hobby, there was no rhyme or reason to whatever I put out on any given day. But now doing it as part of a company and being the only advocate at my company, it has to be a lot more structured because essentially the voice that I'm putting out there is the voice that will be one in the same as Deep Note's voice. And so have to be a little bit more curated there. And so what I have been working a lot more on these days specifically for Deep Note is providing value to my audience in the form of content. So not just like sharing stories, which is what I, like I share a lot of stories about like impactful career moments in my life, right? But getting the tangible value out of those stories and relating it to our product is the hardest part. And so I do that a lot via sharing projects that I'm doing within Deep Note and showing people what I can build in Deep Note. And that has the added benefit of like... me continuing to upskill in this journey, right? Like I am building in public and hoping that people get the same value that I did out of building that project and also learn about Deep Note along the way. I think building in public is so important and it's how I first got started writing on LinkedIn because I was like, hey, here's a Tableau dashboard that I shared into Tableau public like.
Megan (12:37.311) Check this out, right? And so that's how I got started becoming a data analyst. And over the years, kind of lost sight of that. But now working for a company that creates a product where people can build their projects, it's kind of a no-brainer for me to dive into that, to one, help my audience learn about the product, two, help them learn about how to build similar projects. And so.. tying all that together into my content is, it's tricky, but it's definitely something that I'm excited to dive deeper into because at the end of the day, really just wanna help my audience grow alongside me because my audience is very supportive. And so I just kinda treat them as my learning buddies along the way.
Data bros (13:28.962) Nice. Yeah. So it also feels like there's some tension there, though, right? So you started out not being a data advocate for Deep Note. How now actually also advocating for a product, a specific one, has changed also. how you approach kind of your like role then as a thought leader because I'm sure there's a lot of people who say, oh, like another post about deep note, whatever, like, are you trying to keep these things separate? Kind of does it mix together well? Like what are your thoughts there?
Megan (14:00.439) Yeah, that is such a good question. I think about that tension. Tension is the right word here. Or like maybe balance, I don't know. That's something I think about every day. I remember once I made a post about Deep Note and somebody commented, they were like, you are selling out and is this part of your daily job to like promote? Deepnote and I was like, yes, it's literally in my job description. But the way that you do it, um, it can be very, the way that you do it has to be like on an individual by individual basis. Like I know some other developer advocates, um, in this space, like all of their content is related to their company. Um, and I, you know, I sometimes envy them. It's like, they have created a consistent, um, like they've created a reputation where they are known for their product and their audience knows what they're going to get out of that, uh, that individual's content. Um, it's going to be materials about their company. And I don't think that's a bad thing. Um, developer advocates, like their job is to educate, um, people about, or it's like to provide resources for these people to be able to use their product better. Um, and so if you are somebody who is, who, relies on the channel of like social media to distribute those resources, then that is absolutely a way to do it. The problem with me is that like I built my platform off of not doing that in the first place. And so if I were to switch into being like 100% all deep note and all my note, all my contents, my audience would be very alarmed. And it goes back to the whole authentic voice thing, right? Like they would know that is not what I used to write about. And like, let's be honest, a lot of people don't like to be.
Megan (15:56.339) sold to or like have marketing with like marketing in their face at all times. And so that tension, um, is something I've had to figure out for myself. And I, what I end up, like right now what works for me is one or two posts a week that I tie back to deep note. It doesn't necessarily have to be, um, it doesn't necessarily have to mention deep note or I have to tag them, but rather talking about the concepts. and the principles that deep note espouses. So that is first and foremost, like notebooks and how we believe that notebooks are the perfect medium for data scientists to do all of their work. And so my content can center around that without being too like deep note in your face. And so that is kind of the balance or like the answer to that tension question that I have arrived at. It's like to... What our company likes to say is like to wave the notebook flag. And like we literally have a flag in our office that says notebooks. But it doesn't say like deep note anywhere. Right. And so I really like that. At the end of the day, our company is like, not just pushing for our own specific company, but like notebooks in general. And that is something that I can easily get behind without rubbing my. my audience the wrong way, especially because I do believe in notebooks and like how powerful they can be so it's easy for me to create content around it.
Data bros (17:25.826) There's another thing, I think like, cause people think sometimes that advocacy is consulting. And I think there's something very beautiful in advocates that have strong passion on specific products. Not only that, they go and work for those products. Think about it. Most people go work somewhere. I hope they're passionate about it. I hope they pick that place because they could. And
Megan (17:33.519) Hmm. You would hope.
Data bros (17:54.422) you're going to work somewhere, you should be proud. And I think your followers should be proud of you as long as you're authentic and you are. And that whole, right, you need to manage and there's this confusion and tension, but there's also one bigger thing, which is passion. On the subject, like, and the fact that you're there allows you to be also passionate about the company you work for, it's okay. And being authentic doing that is perfect. So I don't see anything wrong here. And trying to keep a balance between everything will just make you not authentic and confuse everyone. And that's how people lose it. And yes, there are advocates that are very strong at being neutral. Like they've taken neutrality, product neutrality to a new level, which is its own niche. But I personally like interacting with people that do invest. Time and effort in specific companies, on specific products, take the time to do it. So yeah, so actually well done to be able to combine those two worlds together.
Megan (18:57.859) Thank you. Yeah. Yeah, no, I think that there are ways for me to go even deeper on that opinionated approach and it's just that I have not been at the company for long enough for me to. exactly like be the picture-perfect spokesperson for it. Yeah, and I think my company knows that. And my company appreciates that, you know, I talk about other things besides notebooks, but when I do talk about it, I hopefully am bringing a level of genuineness that others cannot. And so I think that, yeah, like you said, that authenticity is a strength that people should lean into. than shying away from it.
Data bros (19:46.898) Exactly. Awesome. So let's talk about notebooks for a bit. Right? Like, I for once, I'm not a huge notebook expert. Like, sure, I know like Jupyter notebooks, right? All of that stuff. Like, what's new in the world of notebooks, basically?
Megan (20:01.164) Yeah. What's new is precisely what we're trying to push out. It's not just notebooks, but a medium where data practitioners can come together to tackle the hardest data problems together. And so with traditional Jupyter notebooks, what you're used to is something that's hosted on your local machine where if you were to do an analysis and you were to have the environment set up for your workflows, that is all limited to your local machine. And so if you were to try to, A, replicate the environment that was required to run that analysis in your notebook, and B, also replicate the actual contents of your notebook, it's really hard because that was done in isolation. And so with Deepnote and more modern notebooks in general, it's all cloud-based now, which Jupyter , Jupyter lab so they understand that and so but We wanted to take it to another level, especially the collaboration aspect, by allowing for synchronous collaboration where multiple people can be in the same notebook at the same time. And what we have found is that the people who benefit from that are not only data scientists who have to work together, but also data scientists who have to work with people outside of their team to be able to collaborate with those citizen data scientists and business personas.
Megan (21:35.589) because what people are normally used to doing is sending screenshots or PDFs of their Python file. And nobody wants to see that stagnant document. Right, so, right, or yeah, or a snapshot of a dashboard, right? So collaborating not just within the team, but also.
Data bros (21:50.527) snapshot of a dashboard.
Megan (21:58.579) Intra team is super huge. But also what we found is another party that really benefits from this are educators and students. And not just like educators and students in like academic settings per se, but also like people who are more junior developers trying to, who need to like. code with their superiors in the same environment, or even like, um, people who are administering like live coding tests, right? There's this educational component to notebooks, um, that with notebooks, um, that kind of linear, um, format is, is really conducive to helping like develop those building blocks and knowledge. But also when you add that collaboration component, it just takes it to a whole other level. And so we have found that by bringing the collaboration aspect to notebooks, you can really elevate it and expose it to parties that normally, you know, would not have thought about using notebooks in the first place.
Data bros (23:03.522) So base Notebooks is outgrowing its initial purpose of serving data science teams in a very isolated environment, on the laptop in many cases. And now it's becoming a much broader thing and data gets involved, data warehouses, SQL gets involved, formats change. I love it. I've never used the Notebook by the way, exactly because of that. Like...
Megan (23:09.423) Sure, yeah.
Data bros (23:32.13) towards this data science thing and it runs on a laptop so...
Megan (23:34.379) Yeah. And I, you know, you, we can't blame you because notebooks were originally developed for a very niche, like mathematics, um, very like maybe mathematics, um, and like scientific, uh, personas. And so when we introduced this collaboration aspect, it was not only supporting features that allow you to like bring in other people into your workflows, but also supporting other languages and other formats of coding, like no code visualization blocks, SQL blocks, things that at the end of the day, break down the barriers so that it's not just data scientists who are working with notebooks. It's what we like to call those citizen data scientists who may not have been trained to be. Pure data scientists by education, but they have to interact with data science concepts in their day-to-day job. And those are the people that we're trying to bring into the fold. Because if we were to only focus on the super niche, siloed off data scientists, there's an upper limit to that.
Data bros (24:43.254) So how does this relate to BI, right? So you're saying, okay, like you want to get more people in the organization involved. What does it mean the person where I as a data engineer am now like building a dashboard for us, suddenly I would be building a notebook for that. Like, is that something where you would actually replace dashboards with notebooks or those are two completely kind of different things that they coexist? How do they coexist?
Megan (25:06.263) Yeah, so very great question. We have what we call apps or applications that are a layer of curation and polishing on top of the notebook. And it's supposed to be a one-to-one reflection of the contents of your notebook where you can also configure like, whether you wanna show the code or whether you only wanna show the outputs. And that app... Since it's a one-to-one reflection of the notebooks, it's going to be automatically updating with any changes that you make to the notebook, but the app feature is similar to BI tools and that it is supposed to interface or interact with those business personas who may not care about the underlying code and the inner workings of What gets them that view? But the difference between how we think of apps and dashboards is that with dashboards, there tends to be this problem where like, you spend all of this work putting all the pretty plots and charts in there only for the dashboard to be viewed once. Maybe you make a couple rounds of edits and then like that dashboard is no longer used.
Data bros (26:23.21) If someone is using that dashboard more than once, something is wrong with that dashboard. We're in the recurring theme of there has never been a dashboard with positive ROI, which keeps coming up in every podcast episode.
Megan (26:27.337) Exactly, right?
Megan (26:35.155) Okay, this sounds like you guys are onto something here. We'd love to hear your guys' thoughts on it, but like-
Data bros (26:39.875) I will take a defense here. I will defend the dashboard for a second. It's when you really need it. That's like, sometimes dashboards are there when you really need them. So you shouldn't just count how many refreshes a day you get for that word. You should count when that dashboard was meaningful. And we've had, like I've had a few occasions in my career where.
Megan (26:44.072) Okay. True. Yeah.
Data bros (27:05.494) Like without that useless specific dashboard that nobody cares about, we would be in a shitty situation, in a shitty spot. So have respect to the dashboards, but I think that as you said, the dashboards like are kind of serving themselves now and it's becoming, you know, yeah, we need a, it's like fashion. We need to put it back and move to something better.
Megan (27:09.006) Ha! You're selling it. You're really selling it.
Megan (27:19.728) Hahaha! Fashion.
Data bros (27:37.598) And if notebooks is that platform and if we can kind of communicate, present, because dashboarding is all about presenting, it's all about, like engineers that build dashboards, they spend the energy on that and they format them. It's their assets, it's their careers. It's like their project. So being able to package that in a better way that reaches more people in the company.
Megan (27:44.503) Yeah. Right.
Data bros (28:05.186) that makes it part of a bigger story.
Megan (28:08.119) Yeah. Right. And so
Data bros (28:10.395) What can I say? Highly... it's needed. Definitely.
Megan (28:13.739) Yeah, and so I mean, our answer to that is not just notebooks, but also that app feature on top. And for all intents and purposes, like some people call our apps a dashboard as well. And for all intents and purposes, they are interchangeable. But. We like to think of apps as like, rather than being a standalone thing, it is an extension of all the work that's been put into the notebook. And the app is like the cherry on top that is meant for that presentation layer. And we are putting our bets on apps being what we transform or where we go from dashboards. But I think yeah, there's definitely a place and a time for dashboards and it's probably not going to go away anytime soon. But we do start, we do have to start thinking about alternatives.
Data bros (29:08.994) So this app layer then sounds really like kind of your gateway to less technical users, right? Or less technical stakeholders in the organization. One thing that we're thinking a lot about is customer-facing analytics, right? And kind of really taking the data you have in your organization and showing that back to your customers.
Megan (29:15.359) Exactly. Yep.
Data bros (29:26.686) Is this also, I assume notebooks don't really cater to this at the moment, but is there a path there, right? Where actually like this app layer then goes to your customers and you start generating kind of really that customer value as well from these notebooks.
Megan (29:41.299) Um, sorry, can you ask that question again?
Data bros (29:44.05) Sure, so say... So I put it in a simple way. We have basically two ways to open data to outside users. Most cases like when we go from internal BI, which is, as you said, rarely refreshed and you switch to... customer-facing data apps that's frequently accessed and that's kind of the whole different story, the quality and the challenge is at a completely different level. The thing is you still serve that either as a dashboard or you have a bunch of engineers writing code and building the UI. I think Data bros's question is. Is there a future where kind of that notebook evolution can go from being internal only, right? We collaborate internally within the company across different roles to collaborate externally. So that dashboard journey goes beyond the boundaries of the company and goes into the customers. And, and again, it's all about, uh, dynamic format that fits the, fits the audience versus the other way around. And, uh,
Megan (30:53.567) Hmm. Yeah. I okay. Yes. I understand your question now. So I think there's also another component that is a limitation in bridging those two sides, which is the level of data literacy and data skills that those customers have. You're not always going to be serving your dashboards to people who know the fundamentals of data. And so that's going to be a factor that limits. That... connection or like the ability to interact one-to-one on both sides and so I think our answer that we're betting on here is AI to be able to boost the data literacy and skills of those customers who currently are not at the same level on the internal side. And so using the power of AI to enhance coding abilities or to get insights on a plain English level, that is kind of like the answer that we're going towards to not only be able to bridge the gap between internal and customers, but also people across all. skill levels on the data science spectrum.
Data bros (32:18.786) Nice, I love that. Awesome. Hey, I learned a bunch about notebooks today. This was super interesting. Awesome, Megan. It was great having you on the show. Are there any closing words from your end that you wanted to share with our audience?
Megan (32:24.217) Bye! No, I really enjoyed our conversation about the dashboards and I definitely need to tune into some more episodes to see what other people are saying because I think that's a much broader conversation and I'm sad that we didn't get to dive deeper on that. But no, I'm just really glad that we did get to talk about the topics that we did and hopefully you guys learned a couple or you guys in the audience as well learned a thing or two about notebooks.
Data bros (33:02.262) Sounds awesome. I recommend you start with the episode with Vin Vashishta. He had probably the most controversial opinions on Dashboard. So that's a good one to start. Exactly. That should be a good one. Awesome. Thank you so much, Megan. It was great having you on the show. Thank you. See you around. Take care.
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