DataFramed
DataFramed

Episode 83 · 9 months ago

#83 Empowering the Modern Data Analyst

ABOUT THIS EPISODE

As data volumes grow and become ever-more complex, the role of the data analyst has never been more important. At the disposal of the modern data analyst, are tools that reduce time to insight, and increase collaboration. However, as the tools of a data analyst evolve, so do the skills. 

Today’s guest, Peter Fishman, Co-Founder at Mozart Data, speaks to this exact notion. 

Join us as we discuss:

  • Defining a data-driven organization & main challenges
  • Breaking down the modern data stack & what it means
  • What makes a great data analyst
  • How data analysts can develop deep subject matter expertise in the areas they serve

Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn.

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You're listening to data framed, the podcast by data camp. In this show, you'll hear all the latest trends and insights and data science. Whether you're just getting started in your data career or you're a data leader looking to scale data driven decisions in your organization, join us for indepth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in. Hello everyone, this is a dull data science educator and evangelist at data camp. The past few years have seen an incredible edition of new tools and frameworks that empower even the smallest data teams to do more. These tools are often what is referred to as the modern data stack. One aspect of the modern data stack is that it empowers practitioners like data analysts to deliver insights improve value at a much faster scale. This is why I'm excited to be speaking with Peter Fishman CEO of Mozart data. Mozart data empowers data analysts by providing them with out of the box data warehouses that allow anyone to connect their disperated data sources, easily, apply simple transformations and start analyzing data, all without any data engineers. Throughout our conversation we speak about his experience launching modes art data, the trials and tribulations most data teams face when trying to hit the ground running, the skills modern data alics need to have, the importance of developing subject matter expertise, analytics, rolls and more. If you enjoyed this podcast, make sure to subscribe and rate the show, but only if you enjoyed it. Also if you're interested in the modern data stack and want to transition your local notebook environment to a cloud based collaborative environment, I highly recommend checking out data camp work space, where you will be able to code and Python an R and use a bunch of templates and data sets that get you started in data science right in the browser. Now let's dive right in. Peter, it's great to have you on the show. I am excited to talk to you about the modern data stack, the skills that define a successful data analyst today and more. But before can you give us a bit of a background about yourself and how you ended up where you are today? Great to be here. I'm keep touchman. I'm the cofounder and CEO of Motz art data. Like many people in the data space. Something of a failed academic that transitioned into the world of sort of applying statistical experience and putting that into technology. So I've been working at startups for the last sort of decade plus, mostly in data functions, and ultimately decided myself to my friend day and decided to build basically ourselves as a service. Now we built motzart data, which we call the easiest way to spend up a modern data stack. That's great. So can you walk us through how these experiences that you've had across industry and academia led you to launch modes art data, and can you walk us through the challenges modes art data tries to solve? There's a long thread...

...there because it does sadly capture many, many, many years, but there is a lot of consistency in the theme. So what has basically happened is that data has become essentially like bigger over time, not just the sort of buzz word of big data, but basically the computing power ultimately has a lot of downtream effects, like people can collect more database, they can get more value out of that data. My sort of arc looks like I was really doing very early empirical work in Grad School when obviously statistics have been around for a very, very long time, but the first time where you could really use hundreds of thousands or millions of observations. Today, doing analysis with millions of observations is not just like trivial. People would I roll that, but for me that was kind of the size of the data sets that I was working with during my PhD program which at the time was almost unthinkably large, sort of exceeding whatever excel could do. But what ultimately has happened is that, you know, you find insight in the data and then companies figure out ways to take advantage of it and then you have to go find that next insight in the data. So I started my career in the sort of facebook game space where a lot of these companies competed over using data in novel ways. FACEBOOK had billions of users, so as a result, the data sizes and volumes were gigantic and you could make really novel insights and we started doing a lot of really really paid very close attention to cax and LTV is and the game was to build a virtuous cycle of buying people eyeballs very efficiently and then sort of feeding that into monetizing and getting more people onto your platform and getting a virtuous cycle going. I then saw the opportunity to deploy that into the betob world. So the sort of defining part of my career was this company, Yammer. Yammer, we took a lot of the B Toc approach to software development and then applied it in the betob world and the bottom up fast world, which didn't really exist at the time. But it calls for a lot of understanding what your users are doing and understanding sort of the attractiveness of the prospects as a function of WHO's actually using your product, and that required data folks, and not only that, data infrastructure. So I built a tool at yammer called avocado along with a really great team. Avocado today is really Mozart data plus mode analytics, and from there, you know, have had a lot of different opportunities to have similar data infrastructure at different companies before ultimately deciding to build it myself. That's very exciting and I'm excited to unpact this further. Before, though, I want to set the stage for the landscape data teams are working in today and the dynamics that really led to the launch of Mozart data. As you said, and I completely agree with this, notion, that data science has become table stakes and no longer and nice to have. So I wanted to start off our chat by first asking how would you define a data driven organization and how can an organization integrate data...

...science as a table stakes practice today? Sure so. I think most people have a mental image of a data driven organization as one with lots of TV's all over the office. Now there are nonexistent offices and those TV's have time series of KPIS and people just walking around the building understand what's going on with the company by observing the time series of the KPI. I set up a strongman, but I very deeply disagree with that. So the first thing I'd say is very few sort of canned ways of looking at the data often provide the necessary insights that you're talking about. A data driven organization is one where data has a very important part at the key sort of decisionmaking tables. That can mean a very senior executive that's a data person. That can mean that data starts every meeting. That can mean that data analysts have access to all sorts of key decision makers or ultimately data becomes the key decisionmaker more so than then. The word strategic. I often find that non data driven organizations often talk about strategic investments, ones that almost can't be justified in the data. When you start a company, when it's when there's zero idea, zero data, zero people or one of each of those things, you end up really needing to actually be strategic. You actually need to you need to sort of imagine a world that doesn't exist, that cannot be justified by backwards looking and you need to essentially apply that your own direction and and thought and beliefs. Now data can inform that. I mean one of my favorite examples right again, worked at this facebook games company called play them. We used to sometimes run advertisements on games that were basically half finished and though you couldn't make like a statistically highly confident conclusion about how effective or successful the game might be, you could get a flavor for how difficult it would be to maybe a choir user. So your belief about that could be tested even before the game existed. So that's not to say at a super early company you have to only go on gut and strategy. But what I think is a data driven organization is one that data is a first class citizen, but not just that they collect data and they have dash boards and that they look at time series and they can go to bed at night because they know that their company is going up into the right but that rather key decisions are informed by data and cuts and dives and summaries and models of the data. So, to double down on your point here, a data driven organization is where data becomes a habit across the decision making life cycle rather than something to look up. Absolutely. So what are the main challenge is affecting organizations today who truly want to make the most of their data? The way that an organization gets to a place where it can be data driven it is by not being data driven. So...

...the success that brought you here is not going to be a data driven success. It's going to be a success that's driven by, often the founders, but typically sort of beliefs about the world that you know couldn't necessarily be justified at the time that end up actually proving out to be true. You typically have this headwind of the thing that brought you here was you weren't data driven. How organizations become data driven tends to be an underlying belief that our organization must be data driven, and not because a venture capitalist has sold you to be data driven, not because the world and the podcast you listen to tell you to be data driven, but rather because you ultimately truly believe that the signals that the world is giving you is going to be more informative one sort of aggregated and summarize the right way. You teach a class sometimes at Berkeley, words of my PhD, and I go back and I put up a bunch of different ads from games that we ran on facebook and I said which of these is the most effective, which is going to get the best clicks? People raise their hands indiscriminately, but they have some that they like some, and the ones that they like actually tend to be sort of the better ones. But when you show the add to a hundred million people, their opinion is correct more so than any true experts. And I think what you need to do is maybe developed that muscle over time. Now that is not to say that if you haven't done that, if you haven't had it beaten into you that you know, you really need to be thinking about the data. Think that they the right way and using the data, you can still very quickly adopt that. If I go back to my time at Yammer, we had two very strongly opinionated leaders to cofounders, David sex and out of his zone, who have a ton of intuition. They're they're famously very talented at product and technology and they would have a ton of intuition and it was in fact that intuition that made Yamor an attractive company for me to join. But early in my career, actually in the first three months of my career, we ran in a B test on the new user flow which went counter to both of their intuitions and we did it like almost haphazardly, by accident. But it really set my career up for success because the results were very clear and slightly counterintuitive, and you very, very rarely see that in technology. You know, I think even data people like to hype. Oh, you run these experiments and you get these counterintuitive results and your company becomes better. That happens rarely. Much more often you get no results off of things that you think are almost certainly going to work, rather than you get counterintuitive statistically significant results. That's happened to me not a handful of times of my career, but very, very, very few times in my career. And and it just happened that it was in an early part of my time at Yammer, which basically changed their whole perspective on how important it was to run a B test when releasing products and it became an essential part of the release criteria. And I think ultimately that was a little bit of chance, but...

...but a lot of open mindedness of those two folks, both of whom are now investors in Mozart data. But on top of it, I think it takes like either it's at your core or you get a very clear lesson, and then that's how you become sort of a data driven organization. That's awesome and I wish we can dedicate an entire episode just to in fact, your experience at Yam or and working with people like David Sacks. Now, of course, a key component of becoming data driven as an organization is the set of tools and supporting infrastructure that enables faster time to insight. This is what often is referred to as the modern data stack. I'd love it if you can break down what you think is meant by the modern data stack and what are the characteristics that differentiated from the previous set of tools data teams are used to. The modern data stack is not really all that modern. The modern data stack is a modernisation of existing data tools and data pipeline tools that have been around for a very long amount of time. The branding on it is great, because I hear the words all of the time and it is well deserved in some level, which is to say cloud data warehousing as become like ubiquitous in the sort of users of data space. So the first thing you know, I'd say, is that there are these powerful calumners that are sort of able to crunch again giant amounts of data, and not the types of the data sizes that I was working with twenty years ago, but like real joins on huge data sets. So what that does is that enables you to use data from multiple places. So what a modern data stack is effectively not too different than what a V lookup and excel would be, which is to say it's joining data from multiple places. The stack that gets you there is an El tool, powerful data warehouse and a tea a transform layer. So at a layer to essentially clean up your dam. So you have to extract and load data from many different sources and then you have to clean and transform it. So elt the data. So when people talk about the modern data stack, they are talking about Lt. but t now has a big meaning. Cleaning. Everybody, everybody's always known that cleaning is a huge part of what a data person does. My old boss at Microsoft was Ronnie Cohab who has a joke, and I don't know if it's his joke but I know he loves to use it, which is to say, you know, Ninety five percent of data science is cleaning data and only five percent of data science is complaining about cleaning data, which is to say he he lands the Punch one a little bit better than I do. But what he's saying is that actually he will think, Oh, you know, it's all building these incredible models off of these beautiful data sets that you compete on or given. And in practice actually so much of the work is cleaning and making sure that data is right or consistent and and so little of the sort of work that a data person does is real data analysis. Then certainly not zero percent, but...

...the joke lands better when you expect the answer to be five percent, but only it turns out it's actually just about complaining. Is the rest of the time. If I think I was sort of what the modern data stack is is now all of these tools that represent the sort of cleaning layer, and it's not just essentially scheduled tables. It's it's a variety of different hearts of making sure that the data that you are looking at downstream, whether that's in your bi tool most likely, is actually from, has essentially traveled without sort of any problems. An exciting part of the modern data stack for me is really kind of the emergence of new categories within the data stack. For example, last year we interviewed Bar Moses, CEO of Monte Carlo, and how they're trade blazing the data observability category. What are some of the categories and tools you've seen emerge over the past few years that you've been excited about? Sure, of course I'm going to say a manage to pipeline. I think it's the coolest category and I, and I haven't to love one particular company in that. However, beyond that, there are a variety of tools that get a little bit more what I call like upstream market into larger companies that have larger data teams that are using their data in a variety of ways. But ultimately, once you have loaded your data into your where house, there's a variety of things. There's data observability, there's data cataloging. I kind of remember way back in the day. We know, we USTI have columns, revenue, underscore, final underscore, the one to use. Underscore. You really you want this one V six. You know, like and what I think is obviously the ability for larger data teams to come in and understand the world, typically quickly, which is you actually you know, what you find is once you actually have a mature data organization, it might take someone's weeks to come in, or months even to come in and understand the stack. and Dj Patil has a line about his time at linked in, which was so much about being successful as a data scientist that linked in was about getting a win in your first ninety days. And if it takes you ninety days to get up on the stack or eighty nine days, you better, like be amazing, you better be able to find something incredible in one day, whereas if it takes you a week or a day or an hour to get up on the stack. Well, now you have a real chance to be successful at that company. So there's a proliferation of tools that sort of really savvy companies, like a Linkedin, like a Yammer, you know, all built and used. Obviously Air BB has built a number of the famous ones, and with those tools were about were making data people effective. So you and now a lot of companies have sprouted up in terms of building those toolings that these companies spent countless everything be probably spent hundreds of millions of dollars on, not that it mattered, but they spent countless millions of dollars developing now making that accessible to companies that don't have the budgets of Air BINGB or facebook or whomever. So I see a lot of sort of development in that...

...space. Obviously, other categories that are popping up that are you know, reverse etl is a really great example of a downstream one that we had built bottom up SASS world and at like subscale. Right. So now having services that will do this or having services that do extract and load, I think are really really important for companies. Where does Mozart data fit within the modern data stack and how does it solve? You know some of the challenges we've discussed US far and can you walk us through some examples of Mozart Data and action? Mozart data basically is an all in one data platform. So what that means is in under an hour you can start connecting multiple data sources. We spin you up snowflake data warehouse and you can start writing your transforms and connecting a bi tool or vers etl tool and start to get inside. So the real sort of magic is that this used to take months and a number of data engineer hires, or you do a lot of vendor assessment and then pick your pot Pourri of vendors to do it, or you hire a consultant to do it. Today this can all be done in effectively no time and by the time you sort of done with a demo, you could be up and running and clearing your data and your favorite bi tool. Really there is this challenge of this speed to insight and Mozart wants to empower not just like very savvay data engineers, but rather everyone in the sort of data landscape to be up and running with this modern data stack, all very quickly, all without being gated by engineering. What I love about Mozart Data is how much it empowers data analysts and citizen did analyst to get started quickly with data and provide value quickly without depending on data engineering or infrastructure work. You know, you're someone who's led data teams work with a lot of data analysts while developing Mozart data and more. I'd love it if you can break down how you think the data alyst role has evolved over the past few years and where do you see it heading in the future. Right at the time where the term data science again, like Jeff Imrbucher and and you different tell, sort of kicked off this term data scientists, and then the incredibly rapid growth in that profession happened. The title data scientist was being applied everywhere in the data space and the reason was because working as a data scientist basically met that you got paid a lot more than working as a data analyst. So everybody started coopting the term and then you saw it to represent you had folks that were doing ml engineering all the way to folks that were maybe just out of college working with data for a first time all holding this title data scientists, and it's sort of represented a vastly different set of skills, all encompassed by the same title and different it meant a different thing at different companies. Today you see much greater granularity of them. You see people that hold red ops or Biz ops titles. You See folks where their specific expertise is distinguished. So an...

...analytics engineer is someone that's very different from a data engineer. And you know, a data scientist today has a specific role within a company. A data analyst tends to have a specific role. Now we still see a lot of if you had a ven diagram of the skill sets, a lot of that would overlap and I think actually the best I don't think that one title is there's no greater than sign. I think a lot of the core skill set ends up being the same. Like what makes for like a really great data scientist actually makes for a really great marketing ops analyst, which is to say sort of a deep understanding of causal relationships, of inference and like. It's a different set of technical skills. It's obviously it's a different role within the company and the organization. You do different things on a day and day out basis, but the core is still about sort of data thinking and data capabilities rather than specific technical expertisis. I completely agree here, especially since there's a layer of skills as a certain extent in variant as the role evolves over the time. What do you think are the defining skills data analysts to cultivate to become successful in a modern data team today? I'm a little bit biased because I spent a big part of my s sort of thinking about really causality. So I did a PhD in economics. I studied behavioral economics and what was typically true was you would get great data sets that we're not generated by experiment. So data sets where some that you know, you measured things over time and you sort of had an understanding of an individual with an idea over time, but you didn't necessarily have what you really wanted, which is to run a scientific experiment. But people in conditioning and condition be and then have I popsis and see which one wins out. When you don't have that, you have to basically do almost statistical tricks. You have to think about, okay, what is something like an experiment, and I think often that this is one of the most like underrated skills in data to really think about, you know, what you're trying to do with your data is essentially a sign, a causal relationship based on the past that you think applies in the future. For a number of reasons, right, you think that there was a mechanism that brought it that still exists today. So I think people that have really that deep thinking about like understanding causal relationships and understanding what typically is wrong with data. So the classic example is he's okay, well, drowning deaths are always up in the months where ice cream consumption is up, and said obviously, all novices say, well, that's because the warm months people are eating ice cream and they're going to beach or they're going to the pool and of course, and they they realize that that's actually not the causal mechanism. But then then you divorce it from that specific joking context and then you bring it into a world where many things are going on and your job depends in some sense, the value you bring to the company depends on identifying a relationship that you think moves maybe the companies, whether it's their marketing, their business,...

...their product, their users forward and then you start abandoning that critical perspective. So in general what I like is a set of almost dismantling of good work, thinking about all the ways in which a good insight or good work could be flawed. Maybe somebody did a robustus check that sort of proved that it wasn't flawed. But at the very least when you read it, can you be you know, or look at the work that was done, can you be skeptical and say, okay, well, maybe it's mostly driven by something that won't necessarily repeat itself, because a lot of these when they do replication studies and you know, when I worked at Microsoft, I worked at Bing and and being, you know you had the huge luxury of not just millions, I just bill is trillions of observations and you know you could keep test running and get inference from there. So I think like inference is the big skill, but then also inference with small data is also a real skill. It's a little confusing because typically you can't make inference with small data. So you know, if you see one observation or UN of one or two literally you can't make a valid statistical inference from that. But really having a deep thought about mechanism and how you would set it up to actually learn that answer. In Espace, where you're pretty constrained by database what you find is, and and we found this a being that even when your data size is infinity, you always want to cut it and cut it and cut it and cut it and cut it to a smaller and smaller cohort to make a more and more precise inference. And without fail, you run out of data, even when the data seems like the the size infinity. I think two skills to me, often are the most underrated. It's the ones that that I think people should develop and work on and it's also the ones that we interview, for not just that modes are but at a lot of the places that I've worked and being able to make these inferences and spot these console relationships within the data set requires a lot of subject matter expertise. You know, oftentimes with smiths in the discourse around upskilling and breaking into tech, is subject matter expertise and domain knowledge especially to be able to succeed and and it tics roles and data roles. Can you comment or expand on the importance of subject matter expertise in a data role and how it has helped you in your career. Well, just literally picking this picks up great like you mentioned off of the last question, which is if your key insight is thinking about the right mechanism that is driving the causal relationship you're ascribing to your data, then actually understanding what your users are doing and what motivates your users is critical. So again, I worked at Yammer. We were, we were the biggest per capita consumers of our product, you know, as a company. So it's not surprising Dan and I, my cofounder of motes, are data. You and I thirteen years ago started a hots US company. We were also the number one consumers of that hot sauce. So subject matter expertise is one hundred percent.

I gut a table stakes thinking that you have to bring in order to understand those relationships. Now the flip is sometimes that deeply works against you. So it's not linear up it's not necessarily just concave as in. As you get more and more suchage matter expertise, this first derivative remains positive. You can find that sometimes you are so deep in your world you're missing what the typical user is doing and actually a lot of times and past jobs we've had that problem where we are the right tail of usage and expect everybody to understand kind of some of the subtle things that are going on within the tool, and what you find is that people at both surprisingly surface willingness to pay attention. You know, you're the most important thing to you and a lot of times you can build software and to you it's incredible, but to the typical user that isn't willing to make that same investment into learning all of your nuances, it might not be the case. So subject matter expertise, first of all, is the table stakes to get started. You can't reasonably understand the mechanisms that are driving your user base without understanding your users in the first place. That's why you often see companies like AIRBNB and Ubert consumer companies. People that work they're just nuts about using those products. Brian Chesky famously stayed in their BMB for one whole year, didn't have an apartment, and that was a critical part of essentially developing domain. Next for it's yes, it's about empathy for the customer, but also it's also about tuning that the expertise and everybody I knew that worked and Ryan sharing was taken ride shairs everywhere they had to go across the street, they'd take a ride share. I think it's developing not just that some matter expertise but also the real kind of getting to you users mindset. So, given your experience and startups and working in smaller organizations, how do you instill that subject matter expertise in early stage startups when they don't necessarily have these massive user bases when they make hires, for example? So I worked at a company called open door, and open door when I was there was largely buying and selling homes and Phoenix and I had no desire to buy or sell. I didn't own a home in Phoenix, but I didn't have a desire to buy a home in Phoenix. Obviously now they're in many, many, many more markets and I didn't have the ability to gain expertise in essentially that buyers journey because I never went through it. You're not always gifted. The situation that I discussed with the consumer companies, where you're a data scientist that say facebook your dog footing it all the time. I think the key is one. Obviously, if you can do that, it is a huge advantage. And if you can't, I think you really want to disproportionately invest in talking with you know, sort of y see tropes which are talk to customers, talk to customers, stuck to customers. So I do think that sitting down observing customers, talking to customers, talking to prospects that rejected you, all of those things are trying to up your you know your knowledge. Now the flip is I'm now selling a product that, the fact, I've worked on for twenty years. So you know, your subject...

...matter expertise is not necessarily one that happens the second that you sign your offer letter. Your subject matter expertise. Hopefully you're leveraging you know, in my case, or forty years of subject matter expertise. But beyond that you want to be able to really understand your customer, whether that customer is you, whether that's exhaustive research. You shouldn't think of your title as well. My title says data in it, so I've got to be in a back corner doing data. A lot of the term that I like to use use your feet, which is talked to the product or customer, facing folks in your organization or if you can talk to customers. That's great. And flipping the question slightly, if I am a data analyst breaking into a new vertical, whether at a start up or an enterprise, what is the fastest way for me to develop subject matter expertise? So I do think adjacent problems can be helpful. I mean, I've loved reading matsilver for the longest time and I do think sort of reading folks that think about data the right way. I started my career in the NFL as a statistician, not as a player. You know, I had been into sports statistics for my whole life and, you know, I think that there were a lot of parallels to the thinking about baseball. Famously had solved a lot of these sort of real like problems of figuring out what had tight relationships with performance and when had, you know, predictability, etc. But that thinking then very deeply motivated and I was excited about I just had a passion for it. I read up a lot about it and I think if there's, you know, an analytics in a space that you love now, for me that was baseball and football and there's now tons of material. At the time there was just sort of limited amounts of material. But if you can find those people that love writing, really Saville in the spaces that you love, I think that you're going to find good analytical thought, questioning breakdown and that going to apply to whatever discipline that you're going to do. Mean Breeding Money Ball. My favorite book from Michael Lewis Really is the same type of thinking that I would give early stage startups essentially about. It's the fact of the same you know advice y see gifts, which is write down the equation of success and then breaking it into its component parts and then measure those parts and then dive into when one of these pieces is not working, cohort and cut and summarize, and that's how you start analytics anywhere. But it certainly was how the AIS did. It went the someond years ago when they were trying to compete with bigger market teams. That's awesome and as we close out our conversation, I'd love if we can think about the future for a bit and what you think are some of the trends that are really going to shape how individuals and organizations work with data. I'd love it if you can list some of the trends that you're particularly said about when it comes to the modern data stack and what and how it will affect data driven organizations. I think we touched on one of them, which is the real rise of this is and data scientists. First of all, you see a bunch of savvy people that write sequel that don't have data title,...

...you know, Biz ops, marketing APPs, all of these sort of writing sequel or our python or something like that is just not uncommon in roles that were almost exclusively non Tac I think like that is a really exciting moment for anyone in the data space, because the data space is now opening up to many, many, many more roles at companies and many, many, many more people have the chops to do something, to be a little dangerous with their data. I think this is a great trend for companies trying to solve the data problem, for SMB's, and obviously I'm very excited about one of those companies. motes are data. The other thing, the other part of the trend that I'm excited about that sort of also relates to Mozart. Data is it used to cost you, like hire a couple of data engineers and buy a bunch of expensive infrastructure and you might be out two, three, four, five million dollars just to get started on your data journey. Today it's a six dollar swipe of a credit card and you're off to the races. Now it's metered and your bills become significant, your investment and data ultimately become significant, but the fact that you can get started for close to nothing is incredible. It is a huge different. So, if you think about the types of companies, those are companies that could really afford a multimillion dollar investment in data so that they could have that advantage. Were the largest companies. You could only get jobs at the biggest companies because those are the coming is that had the data teams. Those are the companies that were leveraging the data and could effectively take advantage of their scale and applying those data insights. Today this is becoming table stakes earlier and earlier. So more and more companies like ours, not just ours but like ours, are really empowering and enabling the SMB to use data infra, the types of data tooling that I see more upmarket. In fact, generally I find data stacks to actually be stronger downstream. Before there's a dozen sources of truth. You know, it's actually kind of a little bit paradox school, which is actually almost the the more constrainer budget, the more likely you are to end up with effectively a tighter data stack. That's great and I love that. First Trend, especially in this is something that we've definitely seen a data camp, with the hybridization of jobs and the emergence of data skills and, you know, traditional roles like finance, marketing and a lot more now. Finally, Peter, I had an awesome chat with you today. Do you have any final call to action before we wrap up? Yeah, obviously rooting for so many people in their data journeys and we love helping small companies at the start of their data journey get up and running on their data infrastructure, all in under an hour without really needing any day engineering support. So if you're interested in that, we love to talk to you at Mozart data so I'm pete at Mozart Datacom. That's awesome. Thank you so much, pewter, for coming on the PODCAST. You've been listening to data framed, a podcast by data camp.

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