Table of Contents
- • No silver bullets: Building the analytics flywheel
- • Identity Crisis: Navigating the Modern Data Organization
- • Scaling Knowledge > Scaling Bodies: Why dbt Labs is making the bet on a data literate organization
- • Down with 'data science'
- • Refactor your hiring process: a framework
- • Beyond the Box: Stop relying on your Black co-worker to help you build a diverse team
- • To All The Data Managers We've Loved Before
- • From Diverse "Humans of Data" to Data Dream "Teams"
- • From 100 spreadsheets to 100 data analysts: the story of dbt at Slido
- • New Data Role on the Block: Revenue Analytics
- • Data Paradox of the Growth-Stage Startup
- • Share. Empower. Repeat. Come learn about how to become a Meetup Organizer!
- • Keynote: How big is this wave?
- • Analytics Engineering Everywhere: Why in the Next Five Years Every Organization Will Adopt Analytics Engineering
- • The Future of Analytics is Polyglot
- • The modern data experience
- • Don't hire a data engineer...yet
- • Keynote: The Metrics System
- • This is just the beginning
- • The Future of Data Analytics
- • Coalesce After Party with Catalog & Cocktails
- • The Operational Data Warehouse: Reverse ETL, CDPs, and the future of data activation
- • Built It Once & Build It Right: Prototyping for Data Teams
- • Inclusive Design and dbt
- • Analytics Engineering for storytellers
- • When to ask for help: Modern advice for working with consultants in data and analytics
- • Smaller Black Boxes: Towards Modular Data Products
- • Optimizing query run time with materialization schedules
- • How dbt Enables Systems Engineering in Analytics
- • Operationalizing Column-Name Contracts with dbtplyr
- • Building On Top of dbt: Managing External Dependencies
- • Data as Engineering
- • Automating Ambiguity: Managing dynamic source data using dbt macros
- • Building a metadata ecosystem with dbt
- • Modeling event data at scale
- • Introducing the activity schema: data modeling with a single table
- • dbt in a data mesh world
- • Sharing the knowledge - joining dbt and "the Business" using Tāngata
- • Eat the data you have: Tracking core events in a cookieless world
- • Getting Meta About Metadata: Building Trustworthy Data Products Backed by dbt
- • Batch to Streaming in One Easy Step
- • dbt 101: Stories from real-life data practitioners + a live look at dbt
- • The Modern Data Stack: How Fivetran Operationalizes Data Transformations
- • Implementing and scaling dbt Core without engineers
- • dbt Core v1.0 Reveal ✨
- • Data Analytics in a Snowflake world
- • Firebolt Deep Dive - Next generation performance with dbt
- • The Endpoints are the Beginning: Using the dbt Cloud API to build a culture of data awareness
- • dbt, Notebooks and the modern data experience
- • You don’t need another database: A conversation with Reynold Xin (Databricks) and Drew Banin (dbt Labs)
- • Git for the rest of us
- • How to build a mature dbt project from scratch
- • Tailoring dbt's incremental_strategy to Artsy's data needs
- • Observability within dbt
- • The Call is Coming from Inside the Warehouse: Surviving Schema Changes with Automation
- • So You Think You Can DAG: Supporting data scientists with dbt packages
- • How to Prepare Data for a Product Analytics Platform
- • dbt for Financial Services: How to boost returns on your SQL pipelines using dbt, Databricks, and Delta Lake
- • Stay Calm and Query on: Root Cause Analysis for Your Data Pipelines
- • Upskilling from an Insights Analyst to an Analytics Engineer
- • Building an Open Source Data Stack
- • Trials and Tribulations of Incremental Models
Identity Crisis: Navigating the Modern Data Organization
As data analytics becomes a cornerstone for modern companies, understanding who owns what in the data organization has never been more confusing—or important. Adding to this “identity crisis” is the fact that many companies use names like “data analyst” and “data scientist” interchangeably, and the responsibilities, like data discovery or data governance, continue to evolve as the data stack matures.
Simultaneously, new roles, such as analytics engineer and data product manager, have emerged to fill very important gaps in this ecosystem.
During this panel, Barr Moses will lead a conversation between data leaders at companies across industries, at small to large companies, about this evolution, where we are headed, and other opportunities and challenges for modern data teams.
Browse this talk’s Slack archives #
The day-of-talk conversation is archived here in dbt Community Slack.
Not a member of the dbt Community yet? You can join here to view the Coalesce chat archives.
Full Transcript #
Jillian: [00:00:00] Hello and welcome everyone to today’s panel: Identity Crisis: Navigating the Modern Data Organization. For those of you who don’t know me, where have you been all day? I’m Jillian. I’m on the community team here at dbt Labs. And I will be both your host and panel moderator today, as we spend the next 45 minutes, getting intimate with our panelists, who much like all of us have their own unique experiences, evolving their identities at what often feels like the edge of chaos.
The intention of this conversation is to create a safe space for all. To see that we are not alone in this crisis to feel connected in the deep loveliness of being surrounded by people that understand you. Just a reminder to join the conversation over in the Slack channel, #coalesce-modern-data-org-identity, where I would love to hear from more of the community about their unique identities and experiences over the course of their.
[00:00:55] Panel introductions #
Jillian: Before we jump into the dialogue, I need to steal about five minutes to [00:01:00] introduce you to our panelists and share a bit of the behind the scenes of how this panel came together. I’m going to introduce you to each of them in the order that I came to know them over the course of the past few years.
Okay. So first we have Barr Moses. Barr is the CEO and founder of Monte Carlo data, a data observability and monitoring platform on a mission to bring an end to the infamous and all too familiar data downtime. Barr and I met back in 2019 when she was doing early research for Monte Carlo. While at the time I was over a decade into my own professional career, I’d only been identifying as a data analyst for about three. And here I have this entrepreneur seeking my insights for a product that she’s bringing to market. I will forever remember Barr as the first founder to solicit my expertise, which is a leading indicator of what was to come.
Okay. Next we have Caitlin Moorman. Caitlin is the VP of data operations at Trove, a community leader and writer over in the Locally Optimistic community, which if you are not a part of, I encourage you to check it out. [00:02:00]
And so while I technically only met Caitlin in person this year, I’ve been reading her content is 2019. Her articles on glue work in analytics really left an impression on me. And I can’t tell you how many times that I’ve shared her article on career ladders in analytics.
Okay. On to Stefania Olafsdottir. Stef is the CEO and co-founder of Avo, a data planning and governance platform for product analytics. I met Stef earlier this year, shortly after joining dbt. We were both on a panel at Datafold’s data quality meetup. As a CEO, Stef caught my eye and attention immediately during her lightning talk on the data purpose meeting, it was really refreshing to hear a founder speaking so resonantly about the importance of collaboration between data producers and data consumers.
Finally, we have David Jayatillake, who is the senior director of data at Lyst. David and I met this past summer while I was interviewing community members to learn more about their experiences, introducing analytics [00:03:00] engineering, to their organizations.
What struck me about David immediately was the similarity of our own modern data experiences. It was remarkable to learn how he introduced dbt at Lyst during a high pressure migration from Redshift to Snowflake. This was something I had unsuccessfully campaigned for in my own organization. And it wasn’t until after digging in with David, four years after my failed campaign that I realized for the first time, how much the organizational environment and one’s role within it, influences situations like these. Dave has a really great story and was just recently a guest on the analytics engineering podcast, which you all should check out if you haven’t.
[00:03:39] How this panel came together #
Jillian: Okay. So those are our panelists as promised. I want to bring you a bit behind the scenes of this panel coming to you. Because it gives me an opportunity to share a bit more about my own grapples with identity and why having this conversation today has never felt more important for those of you who don’t know.
I joined dbt Labs earlier this year as a developer relations advocate, a new [00:04:00] role for me as somewhat of a former ops and analytics generalist. I’m used to being behind the scenes. Coalesce planning was already underway when I started. And it wasn’t long before submissions were rolling in. I was already acquainted with Barr and Caitlin who I knew from the Locally Optimistic community and both of them submitted pitches for Coalesce that I absolutely loved right around the same time I had been waiting for the right opportunity to build a relationship with Stef and asked her to pitch at Coalesce. I’m not a stalker, I swear, but suddenly I see that Stef and some of her team from Avo have joined the dbt community.
So from there, Stef and I have a few inspiring conversations and it’s right during the same time that I’m meeting with David while doing research on speaking with champions about building the case for analytics. So long story short, I’m coming into a completely new phase of my own professional identity, having to make some really tough decisions about final content, take the Coalesce agenda, which is nothing I have ever been [00:05:00] responsible for in my professional career.
You could say I was freaking out and I decided to just go for it. I met with Barr and Molly Warwick, who’s the head of marketing at Monte Carlo, and I tell them how much I love Barr’s identity pitch. And by the way, did she know that Caitlin Moorman had also submitted a great pitch on how we’re all still trying to figure this out? And then data’s still really hard. And no, by the way, had she met Stef or David, because I also think they would be a great fit. I basically came in like a total freight train to this awesome panel that Barr had pitched. It was my absolute pleasure and delight. She loved the idea. And would you guess what Lyst was a customer of Monte Carlo. So yes she did of course know David. So I tell Stef, and Molly helps me organize introductions with everyone.
And here we are all today. So with that, I don’t want to take up any more of my time storytelling. I’m going to hand it over to the panelists to kick us off. And we’re going to start by having each of them take [00:06:00] two to three minutes to tell us what has been the biggest reinvention of your career and what did you learn from it?
And Barr, I would love to start with you.
[00:06:09] What has been the biggest reinvention of your career and what did you learn from it? #
Barr Moses: Hi everyone. Great to be here, Jillian. Thanks for recounting that. Oh man, it feels like it was just a few years ago. It feels like ages ago. And it actually has to do a lot with sort of my identity in forming and my reinvention. I’ll take you all back to maybe my first identity reinvention, and then I’ll come back to this one.
I was born and raised in Israel. My dad is a physics professor, and I studied math and stats and I was pretty sure I had to go be a stats professor as well. I basically would not be accepted at home if that wasn’t the case. And so I studied math and stats worked at a statistics department.
And actually I was really bad at it and I also hated my and I learned that after a few short months, which I’m happy about it could have been longer. It was an amazing place, but just not for me. And so that was my big, my very first fail my dad moment. I was like, it’s happening.
This is real. This is not my [00:07:00] identity. It has to be something else. And made a hard pivot from being in academia to actually working with the software companies in tech. And so that was the very first pivot. And I think throughout there, I’ve had lots of sort of pivots throughout not actually not always having a north star of what I wanted to be when I grow up.
That’s actually a question that I’m pretty obsessed with. So almost everyone that I meet, I like to ask what do you want to be when you grow up? And as someone who didn’t have that north star, I’m really curious to hear how other folks think about that. So for me personally, I had my career. I’ve always optimized locally if you will. Which is fitting always, at any given moment and sort of decision in time always asked myself what are the things that would make me happy and where I think it can make a ton of impact and follow that path.
And I have a lot of respect for folks who know, like when I want to be, when I grow up, I know I want to be something or, be something have some sort of title or achieve something. And reflecting on my career, it hasn’t been like that. So actually I want to make space for folks who are not always sure what they want to [00:08:00] be when they grow up.
It’s okay. If you’re on that journey with me, join me, we’ll figure it out together. And then the other sort of reinvention, if you will, of my identity was just before I started Monte Carlo. Prior to Monte Carlo, I was VP operations at a company called Gainsight where I was really fortunate to work with many companies that were on their path to becoming data-driven.
And that journey was long and hard and complicated, and obviously had very different meanings, just, five years ago when it was even harder. So I have a lot of respect for all data teams out there fighting the good fight to become data-driven it’s a worthwhile fight to do, but it’s a hard one.
And when we started Monte Carlo actually before starting the company, I interviewed a couple hundred data organizations from all different walks of life, including Jillian, who I was very fortunate to get a little bit of her time telling me about her challenges. And I learned so much from that. I learned a lot about identities.
I learned about the various roles. I think analytics engineer was up and coming. Then we see a lot more of that today. It’s definitely a newer thing. ML engineer didn’t really exist back then. There were a [00:09:00] lot of sort of new roles and identities data, product manager, maybe one of my favorite new if you will, if I’m sticking to titles for a little bit that has changed a lot, but basically interviewed folks across the board.
And ask them, open-ended, what’s keeping you up at night. What’s the thing that sort of is the biggest pain for you. And actually from that’s how Monte Carlo was born. Monte Carlo today helps companies become data-driven by eliminating what we think is the biggest hurdle, which is data downtime.
But that came out of the amalgamation of many identities that I’ve had. The opportunity to meet and I’m grateful for all of those for helping shape both who I am today and both what Monte Carlo is. So I’m very grateful for the community and for everyone who’s been a part of that journey. Those, I would say were a couple of the important reinventions of me and my identity.
Jillian: Thank you, Barr. Caitlin, we’ll pass it over to you. And then to Stef and then to David, that way I don’t have to jump in.
Caitlin Moorman: Awesome. Jillian, [00:10:00] thank you so much for bringing this all together. This is super fun. So I’m Caitlin Moorman. I lead a data team at a startup cultural pre commerce now. But early in my career, I started out in private equity, so I was working for a growth stage. Private equity firm, which have majority stakes in companies. And I had this kind of Jack of all trades role, which was really fun, learned a lot. I did due diligence company strategy, P&A, random consulting assignments, like designing incentive compensation plans, whatever needed to be done. And I worked with data a lot, but when I needed data, I literally just emailed within the portfolio company and said, "hey, can I have a CSV with these fields?"
Everyone’s favorite request. It’s like the best. And I was very data oriented, but like really had no idea what was happening upstream. And so after almost six years doing that, I decided I was ready to go work in a single company full time. And this was my big pivot, but [00:11:00] it was really accidental. Barr’s comment about not having a north star really resonates with me. I’ve never had a vision. So I happened to take an analyst role at a software company that I thought would be more of kind of an FPA role.
So my first big project was building out a revenue projection model, in my sweet spot, living in Excel, going to get really into this modeling exercise. And I get there on my first day. And I learned that the first step is getting my own data.
So we had no user-friendly tools or dashboards. We had a bunch of saved raw SQL queries that I had to learn how to run and modify, read, figure out what I needed. So I had the super steep learning curve. And then within a few months I’m like automating, dropping CSVs and to share folders so we can update the dashboards in Excel, like modifying PHP so that we can change what our daily revenue email includes.
Kind of, I got pretty deep really quickly. And for the first [00:12:00] time I started to consider myself a technical person, which was a really big identity shift for me. So I went from somebody who like, I considered myself a math and econ person. I can analyze data. I can find the patterns, I can figure out what to do based on the patterns.
But suddenly I started to feel like I can build new things which was really empowering. And so then the role after that, I ran into actual modern data tools, Redshift, Medallia, and Chartio, this is still a few years ago. Eventually Looker and I was off to the races and that combination of being somebody who can find the insights and the data, but who can build the platform to empower the people, to find the insights and the data, has been a really powerful combination for me ever since then, and has allowed me to feel like I can really maximize my impact in a way I really couldn’t before that shift. So that’s again much like Barr, [00:13:00] I’ve got a few of those kinds of big pivots, but I think that’s the biggest one and certainly the most relevant ones to this audience. So Stef, I’ll pass it to you.
Stefania Olafsdottir: Thank you, Caitlin. I also want to say, hi everyone.
Thank you, Jillian for bringing all of us together here and I guess Barr as well for pitching this panel. And I’m really honored to be here among these cannons. I have to say, but okay. Let’s start with the light questions. What’s been the biggest reinvention last year in your career? Easy. No, I’m kidding.
That’s a huge question. So I think like Barr and Caitlin, I’ve gone through a few shifts and it started actually when I was very young because I moved around a lot. When I was a child, I had young parents, I was raised by the families of those people, like my grandmothers and their siblings, my uncles and aunts.
And we moved around a lot. I’ve moved like 30 times when I was 16. And so I think [00:14:00] that sort of group, like I grew up in an environment where I was constantly identifying myself with the environment that I was in. So I really relate to what Barr was talking about. I didn’t have a north star, I don’t really relate personally at all to as a child, knowing what I want to be when I grow up and then just go on that trajectory. I’m always fascinated by that. And I think that takes a lot of self-discipline not throughout that I went into. I then also like Barr, I went and studied mathematics. But I did it with philosophy.
So I started a double major. And I think probably one of my first identity crisis was just, accepting the identity that I would not be a student anymore and be an employee somewhere. First major shift and it felt like such a big decision to decide, like, where are you going to work stuff?
What are you going to do? And there was a lot of pull for mathematicians in the banking [00:15:00] industry or they were always like treating us with some nice things and trying to get us to work there. And I was never, ever interested in that. And ended up applying for a few sort of Icelandic sort of more established startups that were like startups back in the eighties, but had grown into like bigger companies, but they were inventing something cool.
And I went into a company that was in an academia path. It was a genetics company. I joined there as a statistician, but almost had an identity. I had an identity crisis when that, in that company as well, because academic environment can be very chaotic. And for example, I had five bosses and there was no sort of synchronization between like them, like who was my boss.
And if they would give me conflicting projects at any given point and I’d come to them. I think I have a conflict here about these two huge six months project that are supposed to happen at the same time. They’d be like this is an opportunity for you to learn how to say no, [00:16:00] Stef. I was like, all right, are you good?
Because I’m a grownup now. And then I would say a major turning point was going away from that academia path. I was on a Ph.D. Trajectory when a startup reached out and asked me to apply as the founding analyst. And I thought to myself, that Ph.D. can wait. When do you get an opportunity to build a data team from scratch?
And so I joined this mobile game called QuizUp, which had that then just reached 1 million users in its first week which was the fastest growing up in the app store at that time back in 2013. And for anyone who’s listening, this record was later beat by Flappy Bird. I don’t know if anyone also remembers Flappy Bird. So that sort of shift from, just, I dunno, becoming like an internal researcher or a Ph.D. or professor type of thing into all of a sudden, like moving into this hugely fast moving world.
And then I think, the most recent one is the [00:17:00] moment when I became a founder. I remember I thought I knew what it meant. To be a founder just from working close to a founder in a startup. And then I became a founder and little did I know, and I think that has also been a major identity shift for me.
And obviously there has been like a bunch of ones along the path as well. And I’m sure we’ll get to some of those all along of topics of this panel. So over to you, David.
David Jayatillake: Thanks, Stef. Thanks, Jillian, as well, for having me on this panel. It’s been fun. Yeah, so I think I’ve been to a few pivots as well.
And I really identify as not having a north star, at least losing one. So when I left school, I thought I wanted to be an engineer and I started off studying electrical engineering and quickly realized this wasn’t what I expected. I didn’t really enjoy it as much. And I don’t know if [00:18:00] I enjoyed the math.
Pivoted and started studying math, but again, leaving my notes out, wanting to become an engineer to studying maths for some general purpose that I have no idea about the future. And during my time at uni, I had an internship at a Big 4 Accounting Firms. And it was in the tax advisory area, which I really enjoyed because it involved analysis and modeling the bags of money maybe. And I thought, great, this sounds interesting. And I took it. I took them up on the graduate scheme into the same area. Once you become joining the graduate scheme, you end up doing a lot of audit. And that really wasn’t for me. So pretty quickly changes again, realize what I really enjoyed about it was the analysis side.
And that’s how I ended up becoming an analyst at a company called Ocado which is the UK’s first online supermarket. And that was a really good place. And I’d had no idea that being an analyst involved using a database. I had to encounter [00:19:00] databases before at school, but didn’t know that this would be the first week, learn SQL, learn Excel or more than I had already and stuff.
And you basically start doing analytics engineering, using Excel and VBA and string manipulation and cells and things like that other than having dbt, which did not exist. And from there, I gradually progressed into doing more analyst work and then specialized a bit towards pricing and a big payments company.
And that again, I ended up being quite niche and wanted to reinvent myself again, then moved back into mainstream data and that’s led to the track where I’m now looking after, unless some that’s essentially, as in data sciences.
Jillian: Thank you, everyone for sharing, sharing more about your backgrounds. One, one theme. I think I heard come out of each and every one of you is that absence of a north star coming into your careers, our [00:20:00] careers not exactly knowing where we were going and having an open mind and a sense of adaptability for what opportunities get put in front of us? Which, which brings me to my next question, which is lately I’ve been seeing the argument across, the data Twitters and Substacks that perhaps we should be focusing as an industry more on the jobs to be done within the analytics space, as opposed to job titles.
So I wanted to open up the conversation. Yeah. The question for you all is what are the jobs to be done in analytics work and who is in the best position to define them and execute on them?
[00:20:45] What are the jobs to be done in analytics work and who is in the best position to define them and execute on them? #
David Jayatillake: I see this like a flow of data and this sort of companies I’ve worked at, you have applications which generate data. And then that data has to go and live somewhere. So someone has to be [00:21:00] building something to capture that data and store it safely and in a structured way.
And then from there that data has to be manipulated and made available to users. And then analysts can go. What’s the title. So other people have to take that data and present it in a way that derives information and recommendation. And I think that’s, I think that roughly translates into beta engineers as engineers and analysts, that those, the roles that I see in that.
Caitlin Moorman: Yeah. I think that does a really good job capturing the general arc of jobs that have to be done. And I think we’ve always known that job titles are only a loose approximation of the job to be done. If you go look at 10 analysts, job titles or 10 data engineers, Job titles doing 10 different roles.
[00:22:00] And I think in terms of defining it, it’s really important for. But the leadership of the team to be really thoughtful about the work that needs to be done in some organizations, there’s much more on the collection and there’s much more kind of heavy what we consider data engineering work in some works, there’s not as much of that.
And so if there’s not, is it a whole role? Is it something you want another person on the team to lean into? Because you can use off the shelf solutions like. It’s less important, what your title is. And more that you’ve been really thoughtful about what work does need to be done, that you’re hiring for that skillset and the right perspective for that job and that the team you pull together can really bring that.
And on some teams that might look like six people with six different job sites. And on some teams that might look like six people who are all called analysts, engineers, or data analysts, but they know their area of [00:23:00] focus and they can play to their own strengths and lean into two different pieces of that kind of more or less heavily, and then also communicating kind of sets the edges. So where do we, as a data team overlap with the finance team. We need a revenue forecast for the year who owns what pieces of that. And are we sure that we’re filling all of the gaps to make sure that all the jobs to be done are owned by someone though?
There is no one whose job at most, I work in mostly startups and most startups. There’s no one whose job is financial model. You’re bringing together a bunch of different people, and it’s all about really focusing on the problem to be solved in your company and not insisting that you take someone else’s job description and reiterate that across your challenges. You’ve really got to be thoughtful about what you do.
Jillian: Thank you, Caitlin and David. I also, Caitlin, I heard you mentioned in earlier in your intro how you picked up [00:24:00] your technical skills on the job. That’s very similar to myself.
And so this question I want to pose to Stef and Barr. If we think about technologies, having the power to transform our identity.
[00:24:12] How this factors into how you think of a bringing your product to market? #
Jillian: As a founder, transform our identities in the sense that we potentially, as practitioners can take on new roles because of the evolution of the tools that we have. I’m wondering how this factors into how you think of a bringing your product to market.
Barr Moses: Yeah, I think, it’s such a good question and a very difficult one. I think when it comes to bringing the teams together or bringing a team together I think Caitlin spoke very wisely about the different phases of a company and the different needs. I think there’s are three fundamental questions. The answer, first of all, is what problem are you trying to solve?
That’s the first one, let’s figure out what we’re actually, what is the problem? What is the thing that we’re after, right? What does success look like. The second thing that we need to ask ourself is where is this person’s energy? Are they drawn [00:25:00] to solving that problem? Are they excited about doing that?
I think, even if they don’t have the skillset and they need to learn it on the job, it’s way easier to do that, if you’re actually excited about doing it. So I think there’s when we have that fit and that’s actually the third question, is there a fit between one and two? So what’s the problem we’re trying to solve?
Is this person excited and is where is their energy? And is there a fit between one another. When those, these two things happen and come together, I think that’s where the magic lies and that’s where things really can, the impossible can be done in those instances. So that’s how I think a lot about team building and I think there’s a lot in flux in terms of what’s the title, what the skillset how do you add value, et cetera?
All of those things actually figure themselves out when we have a strong match there. The second thing, to your other question around technology and what that means when you bring that to market.
When you were talking about this, I was remembering something that we had a conference in Monte Carlo a couple of months ago. And I was speaking with Bob Muglia, former CEO of Snowflake. And he talked a lot about how [00:26:00] actually a company’s values, a company’s DNA find themselves into the product, which I found so interesting that actually like the values of a company.
I’ll just say it again. The values of the company are actually embedded in the product that the company brings to market, and that makes it even more important for us to be intentional about what these values are that we have for our company. We think about bias and everything that goes into actually building great.
How do we make sure that our values as a company are the ones that we can stand behind? Because by definition, if they’re going to be in the product, they’re gonna be in the hands of so many other people. And so actually we’re influencing so many other lives by building a product and then bringing it to market.
And I thought that was such a powerful thing to say and one that I carry with me today as well, when I look about our PR at our product and how people actually use money.
Stefania Olafsdottir: Thank you, Barr. I really relate to the bringing the energy and the energy of the person together with the need of the role. That’s a great identification, [00:27:00] but we’ve gone all over and there are so many things I want to talk about now because Caitlin and David and Barr said so many great things but your question was around if technology has the power to transform identities we’re talking about how I, as a founder, factor that into our go to market.
Jillian: Yes. And maybe building off of what Barr was saying, I loved the way she spoke about that, your values, right? Bringing the company’s values into the product. Maybe you can speak to that if that’s something.
Stefania Olafsdottir: Yeah, absolutely. So the backstory of what I’m doing today, I’m the CEO and co-founder of Avo today and all of that comes from building the data culture at QuizUp. And I was hired in as the first analyst and obviously.
A lot of the first things that you have to do as the first analyst is what they would was referring to earlier. It takes a lot of data engineering to be there, but it also takes a [00:28:00] lot of sort of relationship building and driving cultural shifts. And I often talk about this journey that we went through at Avo from being a data team of one and two being like a centralized data team and to being a self-serve analytics organization and to being a social analytics organization with some sort of a centralized governance for the analytics stack that we have into being.
A really well operational product organization, which has empowered both with self-serve analytics and also with self-serve analytics, governance which is a really strong and important backbone off self-serve analytics, in my opinion. And I think, that journey of taking the company through, through all of those different stages, it required a lot of cultural shift, but also a lot of technological shift. Once QuizUp was acquired back in like 2016, I think I was like, my energy from the data [00:29:00] space was drained. I was like, I am never going to do anything in the data space again, like that’s never gonna happen and look at me now. But I took a break of one and a half year and started another company and started writing a book about data and all those things, and then got pulled into other projects. And then, in this new company, it took us like five months to ship a product update with incorrect data. And all of a sudden I was just pulled back in. And I felt my energy come back, so I was so passionate about applying technology to make the cultural shift within all of those organizations.
So I think, from my perspective, there are two really important things that we’re focusing on with Avo. We are helping people collaborate, we’re helping product managers collaborate with product engineers and with data scientists or analytics, engineers, or data experts or data engineers, or all of the different titles, as we’re talking about the identity crisis, we’re helping those roles collaborate and [00:30:00] we’re using technology to make that collaboration much easier.
And I think that makes me very personally passionate about doing this and applying technology to make the cultural shift within the product organizations.
[00:30:13] How does your identity influence your ability to be effective in your organization or in your organizational context? #
Jillian: This makes me very excited as a practitioner and for all of the practitioners in our community, that we have thoughtful founders who are considering these values as they’re introducing tools for practitioners to use. So to pivot back over into more practitioner direction to Caitlin and David, I want to pose to you the question of how have you observed identity, since we talked about jobs to be done and that titles don’t matter, it’s more about the problem you’re trying to solve. But consider for a moment as a practitioner, how does your identity influence your ability to be effective in your organization or in your organizational context? How do you see identity being critical there?
Caitlin Moorman: Yeah. [00:31:00] I think, identity can be very empowering or very self-limiting you really need an expansive sense of who you are and what you can get done in order to actually get those things done. I think, like a lot of people who have been working in data for a long time, I started out feeling like I was just a data analyst. Like I’m just an analyst. I don’t know how do you get.
Barr Moses: Most important roles! What are you talking about, Caitlin? Most important roles.
Caitlin Moorman: A lot of my career has been working in small organizations where there were a couple of us and there wasn’t necessarily a really clear role model of what does it look like when you’ve got someone who comes from being an analyst and actually.
It takes a leadership role, not just a data team leadership role, but a company leadership role. What does that look like? How do you start to change things?
And I think that’s an identity that that didn’t come naturally to me. And [00:32:00] I am not a person who hasconsistently raised my hand to take on bigger and bigger roles, but I’ve been hold sometimes against my will and to bigger and bigger roles. I have actually had to have the talk. You can take this promotion or we’re going to hire someone over you. So it’s up to you, what you want, which is one way to get someone to take a promotion, but it’s really important to really see that. See data as a role that can have huge impact on the success of a company. And in some places that’s really obvious.
You got, the stitch fix model of a company built on data. And then you’ve got a lot of roles where you come into a company that doesn’t see data that way, and you have to really build that path. And you need role models. And that’s why I love the dbt community, the Locally Optimistic community. And seeing people out there who are doing [00:33:00] that where you can identify and say oh, I’m that kind of data leader. That’s who I want to be. And I am not the chief algorithms officer that I won’t ever be. That’s not me. Who are some other, lots of other identities that can still have impact.
Stefania Olafsdottir: I like to just say that I questioned things for a living. I find that like a simple identity,
Barr Moses: Sorry to barge in there, but I just felt very passionate about that as someone who, I think we’ve all been in, in some shape or form self-deprecating if you will. And I actually think it is literally one of the most important roles in the company and we need to give a voice to that.
Stefania Olafsdottir: Yeah, plus one. Sorry, go ahead, David.
David Jayatillake: I was just going to say I think identity is important in the data world because where I came from as a when I entered it, there were really just two roles. You’re either DBA or you’re an analyst. And what that meant was that you ended up wearing many hats [00:34:00] and often.
Especially as an analyst, you’d end up doing what you now call, unless it’s engineering, a bit of data engineering, a bit of data science and you, because you didn’t know what you are, you could often end up doing lots of things. Not very well. And I think if you knew, oh, this is the path that I really liked doing, this is the part that I’m most comfortable with, and I think I can deliver the most value, you could probably excel more. I think that’s one reason identity is important and maybe not the role title, but just understanding that the main is your domain. Yeah. And then as Caitlin was saying, going into leadership yeah.
There’s identity and leadership as well. I’ve recently hired a director of data science. I do not know those algorithms on the mathematics behind them. Enough to be that director of data science, but I can be a more general data leader for company.[00:35:00]
Caitlin Moorman: Yeah. That idea of specialization, I think, we all saw it across the industry when the job title analytics engineer. Flew up and everyone had this moment of oh, that’s me. That’s the thing that can call me. Now there’s a word for it, or I’m different from an analyst, not more than, having something that you can identify with and feel like you, you understand how to best play to your strengths rather than feeling like you’re limited by something you don’t do is super, super powerful.
Stefania Olafsdottir: Can I jump in, Jillian, with a quick one?
I host a podcast called The Right Track, where I talked to product leaders and engineering leaders and data leaders about data, culture, and how to build up their data culture. So I’m really intrigued by this and I always ask them about their org structure and who reports to who, and it’s quite diverse.
It’s really diverse what this [00:36:00] org structure looks like. And obviously we can turn this into a conversation about the hybrid model versus. The the centralized model versus the centralized data team, all that stuff, but I won’t go there. But one of the things that I went into when I was talking to Maura Church, for example, who is the director of data science at Patreon, was this interest gatekeeping of the titles as well. And I think what you just mentioned like Caitlin and Barr, like I’m just an analyst. There is this really weird, almost like a caste system of titles within the data space and like data scientists want to call them data. Scientists want to call themselves data scientists rather than analysts, because it’s maybe cooler and we’ll give them access to more machine learning.
But at the same time you are maybe more interested in analytics and analytics is really important. That needs a lot of thoughtful business work. And I think this conversation about why is it somehow more prestigious to be a machine learning engineer or [00:37:00] a data scientist than being an analyst is a really, it’s a question that we should be asking ourselves.
Because I think it’s a really important job to be done is bridging the gap between the product engineers and the business side of things. Really.
Jillian: I think that’s a great opportunity where as we’re coming close on time here. I want to give everyone a chance to answer a final question around if we consider that crisis originates from the Greek word to decide and defines a turning point a stage and a sequence of events at which the trend of all the future events is determined for in this moment.
Now, it sounds like it’s possible that we are in this moment. We’re all experiencing, can turn out in our favors. So I’m wondering, to all of you panelists what does that, what’s a good outcome look like.
Barr Moses: I can go ahead and say I think she has some thoughts on it. I think the last couple of years have been, [00:38:00] has definitely been, a crisis or a deciding moment. For many of us with the pandemic and altered the lives and many other sort of social events along the way.
And I think for companies and for folks in data, I was also in a defining moment, if you will. I remember at the start of the pandemic, I wasn’t even sure what’s going to happen to the data industry. I was literally like, what is going to happen? Is this going to, what’s going to happen?
You’re going to have a job tomorrow what’s going to happen. And actually fast forward to today, I think the data industry has never been stronger. Whether that’s with the explosion of amazing people like this panel who, have strong opinions and are leading teams or practitioners influencing decisions.
Whether it’s amazing companies who changing lives and doing it really well and giving many people opportunities. So I’d hope to think that, sort of these crises, there’s a silver lining to it. And actually at Monte Carlo at our company, we often talk about how we are fortunate, we earn the right to solve problems, so if we’re really lucky we will have many more problems to solve and there’s actually a [00:39:00] thrill in being able to be handed a problem or be faced with a problem or a big challenge and overcome it. And it’s that process that actually makes us. As a company, think about this as we have each function and every time sort of one function pulls forward and makes a big sort of breakthrough moment, then everyone else needs to catch up with that function, for example.
And as a data team, sometimes we’ll make an advancement in some areas. Maybe we know, made a breakthrough with the marketing function and we’re now, we’ve actually came up with some ways to empower them in a self-serve way. And now we’ve got to figure out how to do it to the rest of the company.
And so every sort of crisis, if you will, or every challenge like that actually brings us closer to this north star, this elusive north star that we don’t know what it is. And we haven’t figured it out but it is. And in a way would actually like to be grateful for our chance, for our ability to earn the right, to solve these.
And I hope we’ll have more of those and more opportunities to improve.
Stefania Olafsdottir: Awesome, Barr. I love the earn the right to solve [00:40:00] problems. I relate to it. I have this thing where when you’re growing, it’s painful. So you should enjoy when it is painful because it confirms to you that you’re growing.
And then just this morning, I was on a call about a customer that’s going through onboarding and they are like pushing our boundaries. And our perspective is, they are pushing our boundaries. That is fantastic because this will make the onboarding of our next customer better.
I think I agree. Like I think crises can lead to something great. And I think it’s obvious that there is something really cool happening. I assume you’re talking about the data space. Maybe you’re just talking about the world, Jillian. I don’t know. I don’t know.
Jillian: For once I’m keeping it tight. Just the data space.
I think it’s obvious that something is changing here. And like to see the shift in all of the plumbing that is being built so that we can build really cool stuff around the world and finally empower people to, make good decisions ultimately fast, I think is really exciting.
And remove a [00:41:00] lot of the grunt work that, many people enjoy, but most people are like, are excited about the outcomes that they’re trying to create. And we’re all empowering them to do that.
Thank you, Caitlin, David quickly. Do you want to chime in?
Caitlin Moorman: Yeah, I’m an eternal optimist, so I think it’s every crisis leads you to somewhere that you wouldn’t have been. And hopefully you can find a way to see that as a better place. But I think it’s not even really a debate here.
This stuff, use the word empower, which is like the core of what is happening in the data space. You’re empowering really smart people to do things they never thought were possible. And you’re empowering them to not do things that they don’t want to do over and over again in a repetitive and not helpful way.
And so what we can unlock with this massive new. Sort of level of creation and ability to build things is not yet certain, but I’m really excited about it.
David Jayatillake: Yeah, just briefly. [00:42:00] I’m very optimistic about it. I think we’ve come a long way even in the last three years and having clearer identities to to take hold of that we didn’t have before.
I feel like it’s come a long way and when maybe nearly there, but maybe not.
Jillian: Yeah. It’s hard to tell. That’s what I’m always trying to figure out. Where are we on this wave spectrum? It does feel like we’re coming to a point though, and it feels like a good one.
Last modified on: Apr 19, 2022