#315 DataFramed x Alter Everything: Future-Proofing Your Career in AI and Data Analytics | Richie & Megan Bowers

Generative AI is transforming industries at an unprecedented pace. But as AI changes how you or your team work, one thing is clear. Your skills also need to evolve. At datacamp, we offer everything you or your team need to adapt and thrive with AI. Whether it's business users looking to get the Most out of ChatGPT and Copilot, or developers and data scientists looking to fine tune models, you can learn the entire AI skills spectrum on Datacamp. Power your AI transformation today. Start learning@datacamp.com.

Biggest thing for me when it comes to domain knowledge is like you need to know what your business stakeholders care about, what metrics are most important to them, and the why kind of thing. Understanding and asking good questions around. If you're dealing with data, it's like, oh, what does this mean when this number goes way down? Or asking those probing questions of getting at, you know, why do you want this analysis? We've seen people in ultrix pull in LLMs to enrich or to clean up their data, things like that. But then once that is incorporated into like a full automated workflow and it's schedule, it, it schedules on a run, it does this like I think it gets more into that AI agent category.

In every business there are going to be lots of use cases of AI that people have not yet thought of and it's important for CEOs to motivate people to like, yes, let's go and see what we can do here. The cynical side of me is like, well actually this is just a polite way of saying we want to do a hiring freezer where we want to make layoffs and this is just like a way to make it more publicly acceptable. Welcome to Data Framed. This is Richie. It's time for another crossover episode. This time I'm joined by Megan Bowers, host of the Alter Everything podcast. In addition to her podcasting duties, Megan is a senior content manager at the low code automation platform Alteryx. She also got a background as a data scientist and data journalist. I'm going to chat with Megan about how data roles are changing and what skills are becoming more or less important as the tooling gets better. I'd also like to have a frank discussion with her about what does it really mean to be AI first and how do you extract the value and separate it from the hype with AI agents? So let's hear Megan's perspectives.

Hi everybody. Excited to be here for a new type of episode. For me at least. I'm Megan Bowers and I am a senior content manager at Alteryx and I'm host of the Alter Everything podcast, which is podcast about data science and analytics culture. So excited to chat about data. Hot topics today.

Yeah, great to be chatting with you, Megan. So I'm Richie. I am a senior Data evangelist at DataCamp. So I'm also the podcast host. I host the DataBrain podcast about data and AI. So great to have a collaboration with you.

Yeah, definitely.

I want to talk a bit about changes in skills. So at datacamp we do a lot of education for people wanting to learn about data skills or wanting to learn some AI skills. And one of the big questions we have is, well, AI can increasingly write good code. Do I still need to learn about how to write code? So since you're working for a company that makes a low code tool, do you want to talk about how has that changed things?

It is really fun to work at Alteryx and talk to just the variety and like diversity of people that use Alteryx and like the divi. The diversity of roles in the business. So we see a lot of business analyst type roles jumping in with Alteryx, whether that's someone in supply chain who you know is getting drowned in the data and the spreadsheets and the supplier name challenges, or it's folks in finance who need more governability, auditability. And like, often these people aren't coming from a data background. Sometimes they've been pushed into it, sometimes they've raised their hand for something like, oh sure, I can help with an ERP migration. And then all of a sudden like they are a data person. Like all of a sudden that kind of like happens to them or, or they lean into it. They say, okay, I do enjoy working with data, but yeah, I mean, that's like one thing that I see all the time is that users come from more of like the business background, the domain expertise, and they are able to pick up the data analytics with the low code, no code tool that we have. It's cool to watch and it does kind of raise the question of do analysts need these coding skills? And it's hard to kind of come up with a one size fits all answer. But I do feel like it's moving away from, you know, you have to know R, you have to know Python to. You have to be able to pick up some of these tools and the tools might change, you know, you might get new software to learn. And so that's like, that's one of the things I've kind of observed. And I think, I mean, when it comes to more of like the data science Role. I have just been reading a little bit more on that and I think like, there are some shifts from going from as heavy on the coding, since AI can help with the coding, moving towards like the advising, the monitoring, the AI kind of advisor role. But I've seen, you know, I've seen literature on this or writing on this, but I'm like, ooh, I don't know if I've seen those job descriptions quite yet. But yeah, I'm curious what your take is.

Yeah, yeah, I mean, I do think there's a big difference depending on what your, your job is. So like you mentioned, like, even if you have never wanted to be a data person, you've gone into business or you've gone into like some other role. Data is kind of evading everything. Right. So you do see a lot of people where like, I no intention to be a data person, but now I'm somehow a data person. And there is like, you really do want a lot of tooling assistance because maybe you don't care about the nitty gritty of data so much, you just want to get your job done. Whereas if you are actively a data professional, you know, you've gotten to this on purpose, you've been mindful. I love working with data, then yeah, you probably want a bit more control. And so coding is, is, is still incredibly useful in some cases. And maybe it's different between data analysts and data scientists. I think for a lot of data analytics roles, particularly business analytics, combination of like Alteryx plus BI tool, that's amazing. You don't need to write code, you will solve all your problems. But for data scientists, I think Jupyter notebooks are probably still the kind of standard interface. And so, yeah, a bit of Python, a bit of SQL, that's going to get you a long way.

Yeah. And something I think about too, just seeing some of the code generation or people using Claude or OpenAI to help with generating code, I think about, well, what if it doesn't work or what if you run into problems or yeah, you get some errors or you have issues six months down the line, you do still need to be able to troubleshoot and to be able to diagnose. And yeah, I think that there's that element to it as well.

I'm right there with you. And I think one of the things we've been thinking a lot about at datacamp as we create courses where it's like you learn to code originally, like the courses were very sort of focused on like, well, you need to learn the Syntax. And now it's less about that. It's like, well, you know, the AI can help you with the syntax. You need to understand the concept of like, what are you actually trying to do? And you also need to be able to read the code that the AI is generated and understand it just to make sure is this actually right or not? And be able to debug it. Because if something goes wrong, you need to have a clue about, oh, okay, what's happened here? What do I need to do to fix it? Being able to read stuff is more important, be able to write stuff. I think at the moment.

Yeah, yeah, that makes a lot of sense.

So, yeah, beyond this, I think you had a story about AI agents.

Yeah, yeah, I can jump in. I mean, we, I know we wanted to talk about like AI agents and like the hype versus reality. Are we there yet? Which is a hard question to answer. I appreciate like Gartner and other companies trying to survey people on this, but, but I did find some interesting, like, applications on this one Wall Street Journal article where they were talking about, okay, how are companies using AI agents today? This was like more beginning of the year. And there was an interesting one at ebay where they're using agents to like, write code and create marketing campaigns. It was talking about how they kind of have this like, agent framework where it kind of determines which AI models are used for which tasks. So to me, that follow you use case at the next level. It's not just we have some AI models we're using. It's like we're kind of orchestrating and deploying, sending out, I don't know exactly how it works, but you know, sending out the right agent for the right task, that kind of higher level of determining, you know, what is needed for which task. And I thought that that was, that was interesting. And they're, they're using it for code as well. To like, yeah, generate code, improve code, things like that.

Yeah, it just seems like there are a lot of use cases and maybe the definition of AI agent is kind of very broad because it's almost a marketing term at the moment. Because there's. Everyone wants to say, yeah, we're using AI agents. And so there's like some very simple stuff where it's like, it's basically just like it's a business process encoded in software. And like, maybe there's an LLM in it somewhere. And then you have all these companies promising, like AI workers. So you've got like this Cognition Labs has this Devin AI software engineer. You've got like Julius, AIs got an AI data scientist. EMA's got like an, a Universal AI employee, which sounds very ambitious. And I'm not quite sure yet, I'm not quite sure these work completely yet. Like, you can't quite replace humans reliably. I don't know whether you have a sense of where the sort of cutoff point is for what's an agent that actually works.

It is tough. I mean, what you said about it being sometimes marketing, there's almost this AI agent washing, like greenwashing, where it's like you take something that's close to an AI agent and you spin it out as we have an AI agent. So it can be kind of tough if you don't know the details to know, you know, what exactly truly is agentic AI being deployed at the companies. Some of these are still maybe more in experimental phases. There was this like, report by gartner that like 40% of agentic AI projects will be canceled by 2027. And that, that prediction. And I was like, whoa, that's a, that's a big number. And I think it speaks to some of the early stage ness of it all.

Although surprisingly low, actually, like only 40%. That means 60% is actually going to work. I feel like, wildly optimistic.

Okay, Okay. I don't know. I was just like, oh, if we're, if people are investing billions of dollars, that's a lot of. 40% is a lot of dollars that are just gonna disappear because of like unclear business value or risk. But, you know, I, I appreciate the glass half full. Take 60. Yeah, you're like 60% of these agents like that that could translate to people losing their jobs. Because if the agents are like a digital workforce, it's true. 60% is pretty high.

Definitely. I mean, I think there's like the very simple agents and maybe like where most of the action is at the moment. So we've got like one of my colleagues in the sales team created this agent where like after every sales call, the salespeople is supposed to write about, like the state of the deal, like what happened in the call. And there's this framework called medpic, which I don't really understand. It's a sales thing, just basically says these are the attributes, like where we're up to with the deal. And all the account executives, their business development reps, they really hate doing this because writing what happened in a call is tedious and it's not like going after new deals. So there's an agent now. It takes the transcript, it summarizes it according to format, it uploads the, the details like whatever they're archived and sales for somewhere. So it's doing a task that like humans just really, really hate doing and they do it badly because they hate it. And so automating that kind of task is like, it's fairly straightforward, it works and you don't even have to, doesn't have to be perfect, you just have to be better than a human that doesn't care. Um, so that's, that's like just a really nice example. I don't know whether you've seen similar examples of just like simple things that you can automate.

Yeah, I mean I think that it's, it's the automation piece that kind of moves into the agentic. Like you're not the one copying and pasting the transcript, sending it to ChatGPT, getting it like sending it out. If there's like that automation of the task. I feel like that is the first thing like that I've seen where it's just like the next, it is kind of the next logical step where, okay, you know, we've seen people in Ultrix like pull in LLMs to enrich or to, to clean up their data, things like that. But then once that is incorporated into like a full automated workflow and it's, you can schedule it, it schedules on a run, it does this. Like, I think it gets more into that AI agent category. And I've also seen it seen kind of the agents starting to be used in cases where just you want to, you want to get to answers, but you're trying to pull from like many data sources, not just a web scrape out the Internet. You want your business sources, you want your salesforce data in there, you want your customer data, or maybe not customer data, but you know, all these different data sources at your company and having an interface where you can interact with all of that and even like automate some reporting things like that I think is where I've seen some, some companies moving into.

Okay, yeah, so I'm, I like the idea of agentic or at least automated data cleaning because that's like the part of analytics no one really likes. Like, no one's like, yeah, I love cleaning data. So yeah, that's a great idea for just, yeah, automating the bits of your job that you don't like and it's good for your mental health anyway, even if, even if you don't care about the productivity benefits.

Right. Yeah. Creating lookup tables, not good for Your mental health. So being able to automate the data cleaning and swapping and all of that. Totally. It's much better as long as the. Yeah, memorizing coding syntax like I don't know, people already are googling syntax. You just can't remember it all the time. So anyway, yeah, I like that.

Absolutely. And I guess so maybe one of the bigger developments over the last few months has been that these deep research tools are getting quite popular and they seem to be sort of getting to the point where they actually work what they promise to do, which is just think a bit more carefully and so you can have sort of slightly more complex reasoning of all. So if you build these into agents, I feel like that's the next step of having agents that can think a little and do and make smart decisions. I don't know. Have you played around with the deep research tools at all? Have you seen whether they work or not?

I've seen some colleagues play around with them and show some kind of things around. Say they have a new prospect or a new customer and they really want to know, like I want to help their business with our software. Like I want to know all about their business and the amount of information. 12 page to 20 page research report on, you know, this is the business. These are the challenges, like some really interesting information. And like I was kind of impressed by just the volume of it all as well as the amount of time that this was through ChatGPT that that ChatGPT took to think about all of this. Yeah, I haven't played around, I like love using ChatGPT for research, but haven't played around as much myself with the deep research. But what about you? Have you seen some interesting results from that? Yeah.

So it does seem to be one of those things where the theoretical sort of really good use case is doing customers for research or competitive research where you're like, I don't really want to go and read through like 20 blog posts from this competitor. I just want to see what they're doing they're doing. But then yeah, the deep research tools, they go away, they spend like 10 minutes, they, they come back with a 20 page report and they're like, well, do I want to read 20 pages on this? Then you have to use AI again to summarize what the deep research tool did. That is lays a bit.

It occurred to me when, when this 20 page document was shown I was like, woo, gonna need an executive summary.

Yeah. So maybe like it's a work in progress. I feel like at some point it's probably gonna get very useful, but now it's kind of used with caution and you really do have to spend quite a lot of effort, I think just prompt engineering to make sure you're getting exactly what you want there.

Right. And something that made me think when you said, you know, the next step of maybe incorporating this mode into AI agents, like it is more resource intensive and if, if there are issues with really defining the true business use case, the true ROI and of an AI agent and then you put in this insane costly or like resource heavy computing with the deep research mode, it's like, oh yeah, definitely proceed with caution. In my mind of you definitely would want to kind of work your way up maybe to, to incorporating a model like that to make sure that, that, that you truly need that power. And they are not just kind of making it advanced research, just to make it advanced research. That's just something that comes to mind.

Absolutely. I mean you can research things like forever, but unless you actually act on that or you've got like the right information in there, then it's just a sort of a waste of everyone's time. I suppose.

A lot of this, we're talking about a lot of AI applications and there's been headlines around companies saying like they're an AI first company. So I'm curious like what your take on that is, on what that really means.

Oh yeah. I mean I have very mixed opinions on this. So I think on the positive side is like, yeah, in every business there are going to be lots of use cases of AI that people have not yet thought of. And it's important for CEOs to motivate people to be like, yes, let's go and see what we can do here. The cynical side of me is like, well actually this is just a polite way of saying we want to do a hiring freezer, we want to make layoffs. And this is just like a, a way to make it more publicly acceptable. So there are definitely going to be some sketchier cases where this is just a sort of PR front around layoffs or around hiring freezes. But in general I think it is good for almost every business if you start thinking not just about AI, but in general about how can you go about re engineering your processes. Because just the fact that tooling has got so much better just for like automating things with software and doing new things with, with LLMs, do new things with computer vision, it doesn't mean there are a lot of use cases for making your process better. And as you Start thinking about changing your processes. You might go, okay, well we don't actually even need a technological fix. It's just something simple like, oh, well this workflow is stupid. Let's do things a different way and we're going to see some improvements there.

Yeah, yeah, definitely. And I think that, you know, I, I can also be a little on the cynical side with the top down mandates or the we are AI first. And it's interesting to see there was one example with like the company Klarna where they, they went AI first for their support service, chat bots and, and went from humans to AI and then they ended up rolling that back and starting to hire back in sub humans because the customer feedback just wasn't it. They were getting, yeah, lower satisfactions. Like I think that at the end of the day, like the AI that they implemented, it wasn't truly what the customers wanted. And I think that that's like an, a good thing to keep in mind for or like. Yeah, to keep in mind for all these companies when you're saying AI first. And I think it's important to start considering whatever business unit you're in, wherever you are, like, how could, how could I use AI to make this better? But then, you know, if you're putting it in your products, your offerings, your services, okay, is this truly what the customer wants? Does this really solve their problem? Does this really improve their experience? I think that that is going to be like a more important question moving forward.

Yeah, definitely. No, I do find that Klarna case very fascinating because I mean with everyone saying we're going AI first now, it's like Klarna started this process like back in 2023. They're very much ahead of the curve and I think they did have some successes. Like they sort of had this sort of chatbot that was hooked. It was like I think a GPT powered chatbot that was hooked up their recommendation engine. So giving product recommendations and that kind of worked. The area where it didn't work so well was with trying to completely automate customer support. I think it's like, careful, it's good to think carefully about where do you want your humans in the process still. And like, are there any absolute limits on like we will not get rid of these humans as you are starting to think about like incorporating AI.

Yeah, yeah, definitely. Where's the human in the loop? Where is the review point kind of no matter what process you're looking at improving with AI.

Yeah, definitely. Although actually as I say out loud, you're probably not going to know that in advance. Right. You probably just have to do some experimentation and just say, okay, well maybe we take it too far and we roll it back like Clara did. Like, you can always undo these cancer decisions. Right? You can always hire more people. Not the most efficient way of doing things.

Yeah. Not without some reputation damage in this case. But yeah, you're right. I mean you have to have some sort of testing at least to get a sense of where you might need review points. But in my mind, ideally that testing is done either as a beta test or internal tests or whatever before you launch it out to all your customers.

Yeah, absolutely. You're probably going to want to like just trial the AI first process at least a little bit and see does it. Do your customers hate it or not before. Before you roll it out to everyone. Okay, so I guess the next thing to talk about is the, the changes in data roles. So for a long time there was this idea that the data scientist was the sexiest job of the 21st century. I really had that phrasing actually, because, you know, it's not really true. A good adjective for most data science work. Yeah, spending all day swearing at a jupyter notebook is not really that sexy. But anyway, data engineering has kind of arguably claimed that crown. So data engineering, super hot at the moment. And then also you've got analytics engineering, sort of more recent role as well. Um, so yeah, I guess let's talk about, let's talk about those roles. Do you want to talk us through how you feel about those roles?

I feel like the, the more of the focus on the data engineering, the analytics engineering, it's, it's all this more focus on getting those, getting the good clean data in pipelines like so that it can be used for AI. So it makes more sense to me that as we start to automate some of the, maybe some of the modeling or AI assisted modeling and then, you know, AI data viz, all this stuff, it's going to be just even more important that we have the reliable data because if you feed bad quality data through these AI systems, then you're left with something even worse. I don't know. That's been like a theme I think on, on our podcast. Just the importance of good data, of the right data for your business, of all of those aspects. And I just think that, that like obviously there are data scientists who have these hu. Really broad roles that kind of are already doing this and maybe they will just like shift their focus a little bit more or see job descriptions that Shift more into the data preparation and data pipeline element. But yeah, that's just what I've been seeing of just the importance of having the right data set up for these AI models so that you can actually get the value out of it. And I think that that will continue to kind of shift. What, what is like wanted in the market for, for data type roles? What have you been seeing?

Yeah, I mean it's really interesting the idea that sometimes you really want like high quality control in your analyses. And I figure with a lot of analytics, that's half the job and the other half is just like very much ad hoc analytics. So the stuff I do is like, oh, it's trying to work out like which podcast episode more popular, very little quality control needed. It's like a quick look at the data. But if my colleagues in the finance department where it's like, okay, we need to know exactly how much like ARR. The company has, like how much cash flow we have. They do paranoid data science. So everything has got to be absolutely spot on. And they're the quality controls there and you need to have those pipelines in place. And that's where the data engineering, the analytics engineering, making sure every I is dotted, every T is crossed, that's where it becomes incredibly important, I think.

Yeah, yeah, definitely. It's important to have that trust element and like the, the quality control I think that you were talking about.

Definitely, absolutely. So actually with the analytics engineer, I think it's incredibly interesting because a lot of the job there is that when you've got different teams having different definitions of things, their job is to kind of synchronize that. So I guess like a common thing is like maybe like one team you say, okay, anyone who has registered on our website, they're a customer. Another team might say, well, okay, they actually have to buy something and pay us some money in the last financial year and that that counts as being customer. And so different teams having different definitions of stuff cause a whole lot of confusion. And analytics engineers, one of the job is to create this semantic layer of like what are proper definitions of any business metrics that are used by all teams. And I love that kind of synchronization, just reducing confusion throughout your business, which is incredibly important as you particularly as you scale your business and you want more teams who are in different locations.

Yeah, yeah, I think that's awesome. I think that is like kind of an age old problem of oh, these people in this business unit but mean this by this metric, but someone else in another business unit Will see that on a dashboard and freak out. And so I really love that having that, that, that business layer, like have been data dictionaries and you know, like that layer to describe. Okay, these are the, the, the rows and columns you're looking at. But like having that for, for the business I think is really important. And I guess that does kind of transition into what I wanted to talk about next because it gets into this like domain knowledge. Like what happens with domain knowledge, the business knowledge, as we move into, you know, in the data space, as we move into more of the AI powered, more of the automation.

Oh yeah, absolutely. Because just thinking about it, if you've got seven different definitions of what is a customer and you're asking your AI chatbot, how many customers do we have? It doesn't know which one to use. You're going to get different answers, different times. It's kind of probabilistic or a little bit random. So you really want that single definition. So you need that semantic layer, that single definition of any given business metric, otherwise the AI is not going to be able to give you a good consistent answer.

Yeah, definitely.

Yeah. And so you were talking about the importance of domain knowledge. Do you have a sense of like what data practitioners need to know about their, their domain or their, their business? Yeah, it's a big question, right?

It's a big question. That's a good question. I'm going to go with the classic it depends. I think it can really depend on whether like the data practitioners are kind of in this, in like a center of excellence function where they are, you know, they are the data analytics, data engineering, whatever, team servicing for a bunch of different teams versus like being a finance analyst or being, you know, a mar, the analytics person in the marketing department. I think definitely the threshold for those, that second type of role is higher. Like you're expected to have more of the, of the domain knowledge. Overall though, the biggest thing for me when it comes to domain knowledge is like, you need to know what your business stakeholders care about, like what metrics are most important to them and, and the why kind of thing. Understanding and asking good questions around. If you're dealing with data, it's like, oh, what does this mean when this number goes way down? Or asking those probing questions of getting, you know, why do you want this analysis? Because occasionally, sometimes stakeholders will come and, and think that they want one thing and, and as you dive deeper, you realize, oh, there's actually, I know about the data at this company, there's actually more data. We could use to really get you some more insights. But they don't know because they're focused on their own roles. And so asking those questions and having a feedback loop with the business side, no matter how embedded your role is, I think is just a crucial skill, especially if we're going to, if some of the technology skill pieces might be automated.

I do love the idea that it's incredibly important to be able to translate between business questions and data questions. It's like going backwards and forwards between the two. I think that's a pretty timeless skill. So, yeah, definitely one to work on for a lot of people. I was really interesting too. You talk about the difference between being in a central data team and being embedded as a data practitioner within a commercial team or some other team. So I think that's definitely a trend I've seen is that for data analysts and data scientists, they are increasingly becoming embedded. So unless you're a large company, it's sort of fairly rare to have analysts in central data teams. Though. Morgan is like, my job at title isn't data analyst, it's sales analyst because I'm in a sales team or I'm a marketing analyst because I'm in a marketing team or I'm doing data plus product and I'm in a product team. So I don't know whether this is something you've seen as well. And do you think that's changing the nature of the data analyst role?

I do see that at smaller organizations, I don't know, in my mind, the centralized function where they're kind of taking in tickets and they're managing things that can be maybe a more mature kind of data structure, but I'm not sure. But like, I do see a lot of embedded analysts and even, I guess going back to the very beginning of the episode, I see people who, their teams need an analyst and they step up and they, they become an analyst, even though that's not their background. And something interesting like a conversation a few months back on the podcast was with, with an author who talks about like this role of citizen data scientist. And I, I, I guess it's something that is also kind of a new industry term that I think is really interesting of like those people who are almost, you know, 50% business, 50% data scientist. And they're able to do that because of the low code, no code tools that arise. And so, yeah, I think like also going back to what you said on there's data everywhere, like you have to, you, you know, no matter what role you're in, Data. My data is going to find its way into, into your work. So there's kind of some evolution of, of these roles and I'm curious to see how that'll continue to play out. It's hard to. Hard to know.

Yeah, definitely. I have to say I'm totally there with you that even if your background isn't data, a lot of these, the tooling is there now. So it's very easy to do at least some analyses even if you have no time to be a data analyst as all your job. But just like yeah, I'm picking up some simple like alteryx workflows. Draw some dashboards in Power Bi or Tableau or Looker or whatever the platform is. These aren't terribly difficult skills to learn. So it's something that you can do in addition to having like your, your sales, marketing, product, whatever knowledge. So yeah, you can do it as like a kind of part, as part of your job rather than it being like a full time everything.

Yeah. But then I do think it gets a little trickier as you move into the more traditionally like more technical data science, data engineer, you know, what is that? Are there citizen versions of that that can get a little, a little harder to balance with the business side? Probably.

Yeah, definitely. I think once you get into. I'm trying to think what's a more difficult thing. So maybe like time series forecasting is a very useful thing for business analytics, but it's also, it's a pretty technical skill. You are going to have to spend like a lot more time learning that. The thing is with statistical stuff, so hypothesis testing, um, it's one of those things where like when people take courses on datacamp they we have to push it further back in the curriculum because it's the first thing that's like really conceptually tricky. Like everyone gets the hang of like drawing plots pretty quickly. Well, not everyone, but most people get hang of drawing plots pretty quickly. And manipulating data is fairly easy to learn for most people you just got to kind of spend the time. But then statistics, you really got to stop and like think carefully because a lot of it's counterintuitive. So yeah, there are definitely some conceptually harder things that maybe you do have to put in a lot of time to do well. Although maybe AI will make it easier for everyone there.

Maybe it will. Yeah, it's, it's kind of, it's a crazy time. I even just preparing for this episode, like just looking at all of the articles out there on AI this AI first that like these companies are doing this. This is, this is not getting value. This is getting value. Like I, sometimes I feel like I can't keep up a little bit. That is, it's challenging to keep up with, with the pace of all this sometimes.

Absolutely. I mean I think it's like it's you and 8 billion other people trying to figure out how do you keep up. So yeah, I think continuing to learn stuff, especially when you've got a day job and you've got deadlines of things, it is difficult and part of it is just about building that habit of spending time to learn. So whether it's like you block off like half an hour on a Friday afternoon to just sit and read stuff or whether you try and make it like a 10 minutes on your commute every day. Yeah, building the habit of learning is incredibly important. I don't think of any tips for like how to build learning into your day to day life.

No, I mean I think it is important to incorporate as, as you can in small bits. And for me it's been important to when I do have a little bit of extra time, just taking a bit of time to like experiment if I hear about a new tool, if I hear about somebody else's use case, you know, do kind of the learn, learn by doing at least with just like generative AI for me been that's been helpful to kind of play around with it and see what it is in action. But yeah, I'm sure that if people want to learn more, the, the data camp courses I think could be super useful to, to get into that as well.

Absolutely. And so I think one of the things I've found especially helpful is like, you know, there's all these like papers on archive about new technology developments and frankly I can't bother to read most of them. So it's like okay, download the PDF, throw it into one of these AI chatbots. Okay, can you just tell me the executives tell me like a bit more, get a bit more detailed via abstract maybe but like just ask a few simple questions to try and understand what's going on. I found that incredibly helpful.

Yeah, that makes me think like maybe the logical next step of this podcast is creating an AI agent that sends you automated summaries on data hot topics if we, if we go through the next steps of what AI agents could do. But anyway, more of a silly example.

I mean beyond this, I have to say with my LinkedIn feed I have to follow a lot of like AI influences and so it's very difficult to like go on LinkedIn without just seeing someone talking about, like, whatever the hot new thing is. So, yeah, it is definitely fairly easy to passively consume this when it's being thrust at you.

Yeah, yeah, definitely.

Do you have any, like, final advice for people who want to, like, build their data or AI careers?

What's interesting is typically for, or sometimes on our podcast, it's like advice for people getting into data and for that, like, my biggest thing is like, find the data where you are in your role now and start experimenting, start trying to use these new tools and, you know, whether that looks like automating your Excel analysis or downloading a visualization tool or experimenting more with an LLM, I think that's a great place to start in terms of like, building, you know, an already thriving career and data as we see AI more. I do think a piece of it is trying to keep up with some of these announcements and like, understanding when certain new technologies come out when that could be transformative, like for your work. And instead of maybe shying away from, oh, gosh, that tool looks like maybe it could replace my job in five years or something, like, leaning into, how could I use some of the stuff I have now to show and demonstrate that I am, you know, automating, I am improving with, with AI and be that person that people start coming to at your company when they have some AI questions or, or use cases or ideas. Like, if you can get to that, more of the AI advisor role, whether unofficially or officially, I think that's huge. How about you? What, what is your advice?

Yeah, I know. I, I love that idea of getting the point where other people ask you for advice just by understanding use cases and having like, at least like the first clue about what to do next, having developing an interview for, like, what's, what's sensible, what's not. For me, I think a lot of the stuff we talked about on the show today about the idea of having a mix of technical skills and business skills, and beyond that, I guess it's the soft skills, it's the communication skills, sense of ownership, all this kind of stuff. But those is a package that's really going to help your career, I think. And beyond that, I guess the one thing we've not really talked about is the idea that everyone's going AI first and it's all about process engineering. So understanding process analytics, that's the one area of analytics I think is like, dramatically underrated. No one to care about, but, like, it's the future.

Yeah, that's awesome. Yeah, thanks. So much for this conversation. This is super fun.

Absolutely. I really enjoyed chatting with you, Megan.