 Hello everybody, I am your host Alex Friedberg and this is the Alex the Analyst Show. Thank you so much for joining me today. We're going to be talking about the state of AI and analytics. Now there is a lot to cover and I mean I have my notes here. I have pages of notes on just about everything. We're going to be talking about things like chat GBT's code interpreter, Microsoft's co-pilot 365, which should be released and not too long. We're going to be talking about the positives and the negatives that I personally see with just artificial intelligence in general and analytics. They're going to be talking about some of my predictions and kind of where I think a lot of people should be focusing a lot of their time and energy in the coming months and years as these things progress. So there's a lot to talk about. Again, if you haven't seen one of these shows, now this is not like my regular episodes where they're like 15 minutes or less. This is my Alex the Analyst Show where I just talk and we just have a conversation. You can grab some coffee. I just have water today. I usually have like tea or something but we just chat and this one to me is extremely important. Just to let you guys know, I had had something planned to talk about maybe two months ago. I had it all written out and I kind of started second guessing myself. I was like, I need to do more research. I need to really dive into this more because I don't want to come to you guys just with my initial knee jerk reaction and just kind of tell you what I'm thinking. I really wanted to have, especially for something as important as this, I really wanted to have some just a better perspective on things and really be grounded in what I believe and what I think in this area. It's been a very interesting past six months. I get a lot of feedback from the community, whether it's LinkedIn, Twitter, Instagram, YouTube. I get a lot of feedback and there's just been a lot of worry, a lot of what's happening is like, our job's okay. Should I still be studying, should I still be learning these things or should I just choose a totally different career path? That's my cat. Those are all very, very normal things to think about. Honestly, if I was at the very beginning of my career right now and I didn't know as much as I know now, I might have a very different outlook on things. I have a lot of information. My cat's being weird right now. She's like staring at me through the crack in my door, but I have a lot of information to cover, a lot. Just stick with me, hang out. I think it'll be a really interesting conversation. I'm going to post a lot of this stuff to LinkedIn as well when this goes live. With that being said, let's start off with just what are things looking at right now? As of this recording, it's May 8th of 2023. I'll probably release this next week, but it's May basically. Things have changed so much even in previous months. Two months ago when I was writing this, some of these things weren't announced yet. Things like Microsoft Co-Pilot 365, things like ChatGPT's code interpreter. These things are just recently coming out and there's other things like auto GPT, baby GPT, and some other things that we're going to talk about as well. Really quickly, I want to mention some of these technologies, what they're doing, why people are concerned about it, and then I'm going to really go into my positive negatives on these types of technologies and what I think is actually going to happen. Those are my predictions. The first thing I want to talk about is ChatGPT's code interpreter. Now, usually I don't do any overlays. I'm leaning forward so much. Normally, I don't do any overlays in these types of things. I just tell it how it is. I'm going to try to give you a little demonstration of what this is on the screen in front of you. But ChatGPT's code interpreter is ChatGPT's newest plug-in integration that they're releasing. A lot of people are saying it's basically a data analyst for $20 a month. It has a lot of enhanced abilities. You can feed it a lot of different things. Things like files are going to be able to start feeding into it. They're saying it's a data scientist for $20 a month. They're basically like, that's what it is. It's supposed to be somebody who analyzes data, which is really interesting. Then we have Microsoft Copilot 365, which is basically just ChatGPT inside of all of Microsoft's products. It's going to be in, let me see, Word, Excel, PowerPoint, Teams, Outlook, and more as they integrate these things. They'll probably be in Azure as well, Power BI, all their technologies, really. Then you have things like AutoGPT and BabyGPT. Now, this one's a little bit more niche. Not as many people know about this one, but basically the premise of it is that you are creating these little bots with ChatGPT's functionality and these little agents is what they call them. The agents go out and they do a specified task, and then when they're done, they are done. You can have multiple of them, and the agents themselves can create sub-agents to do smaller tasks. You can tell it, hey, go and do this entire task and do this, this, and this, and this, and it'll go and do all of those things, and it'll come back and it'll be done. So really, really interesting. I've actually kind of dabbled in that myself. I got it onto my computer. I set everything up. It was pretty tough in my opinion. People were making it seem really easy online, but I actually found it quite difficult. With that being said, these are all technologies that are like just kind of starting to come out, and these are the types of things that I think people are most worried about is some type of one product that can literally just be a data analyst. I'm going to be talking about that in a little bit, but that is, as of right now, that is like, that's where we're at. That's the state of AI and analytics is people are just worried. That's on the our side of things, which is the employees, the people who want to become data analysts, who are data analysts. Will they have a job in six months, a year, five years? Will there be jobs going into, if you're getting a degree in data analytics or a degree in data science or these things, are there going to be jobs for this in the future? Again, extremely, extremely, extremely understandable. What I want to go into right now is kind of the negatives. The things that I see are negative to a good outcome with these types of technologies for data analysts like us. I'm just going to preface this by saying I have a lot of positives. I have some really interesting predictions, but the negatives I think are very real, right? I think they're pretty obvious too. And I think once I start talking about them, you're going to be like, if you haven't thought about them before, you're going to be like, that makes a lot of sense. The first thing is automation of repetitive tasks. That's what a lot of chat GVT's ability to automate things is just these repetitive tasks. Think like an accountant or a lawyer or somebody who writes something. You're writing, you're generating these things and it's a lot of repetitive things. Something that chat GVT's really, really good at. And so one of the biggest things that a lot of people pointed to or the negative sign I can see this is that there actually is a lot of automation in analytics. I did it myself when I first got into one of my previous roles. I was like, man, a lot of this can be automated. That's why I started doing mostly with Python. But what a lot of people will say, a lot of people are arguing, and some of it I agree with is that there are a lot of automatable, if that's a word, there's a lot of things that can be automated in analytics for the good or the bad. Automation is very, very, very useful. You don't have to do all these things manually. And so now you can automate something to happen and happens over and over again with consistency. That's a fantastic thing. But one of the concerns is that data analysts are just so much repetitive work, so many things that can be automated that literally you won't need a data analyst anymore. And I'll get to that. I'll come back on that a little bit. The next thing, kind of the next negative, the next thing that I see is that ChatGBT can actually do pretty good data analytics. Now, I am going to come about it from my own personal perspective, some negative and some positive. The positive is that it can do advanced data analytics. I've seen it. I've tried it out. I've messed with it. I have a whole video out on ChatGBT for data analytics. And that's just like a basic, here's how you can kind of use it. But I've seen demonstrations. I've also kind of gone and dived into it myself to do some more complex things. And this is things like the code interpreter, ChatGBT's code interpreter. That's what it's really, really good for is doing these a lot more complex things. Where it is right now today, it's not perfect. It just isn't. And it can do a lot of things with basic information, which is good, right? It's a good thing that it can do this basic information. Now, in a year, three years, five years, it's probably going to get better. That's my worry is that it gets so good that at least that's like this kind of argument is that it gets so good that it's like most people wouldn't need a data analyst. You'll just go to this code interpreter, plug in the data that you have, ask a few questions, and you're done. To kind of support that, because I'm going to kind of play devil devil's advocate a little bit here, to kind of support that some companies, some businesses, that's all they would really need. They wouldn't need a full on, you know, a full on network of databases. They don't need a lot of these other things. They just need somebody to analyze some excelsis for them, or they need somebody to analyze some data sitting in a simple database. That's where I think this is actually going to come in really well for those people and might reduce the need for additional people to come in and analyze their data when they can do something like this. And their data is like not crazy, crazy complex. Or even if it is somewhat complex, like this stuff could potentially do that in the future. Now, I mess around with it. I watched a few videos on it and honestly, it's impressive, but I noticed there were several errors. I was having to prompt it to fix errors. I was having to prompt it to understand what the data actually was within the columns. So it's not perfect, right? It just isn't. But it still is really impressive. This is something that I could see a lot of people using even more than just the standard chat GBT. And especially as plugins come along, and that's something I didn't really talk a lot about. But plugins are, you know, allow chat GBT to plug in to these different applications, eventually most of the things on the internet potentially, or even applications. Like I could see Snowflake and I could see Databricks and Azure and all these things having these plugins where you can sit chat GBT on top of these things and it can pull in that data, read that data in, and do a lot of things for it. So that is definitely a potential negative, a potential bad thing when it gets so advanced, it could potentially get so advanced where it does a lot of this work. The next thing is cost effectiveness and efficiency. It is no surprise that data analytics and having a department on data analysts and employing data analysts and keeping up the software and everything is expensive, right? Most people are expecting around 60,000 at the lower lower level entry level. 60,000 a year in US dollars for employment. Most people want more. They want 80,000, 100,000. For senior levels you're going to get 125,000. Then you have managers, then you have all of your tech stacks and you have all of your cloud compute and you have your software license that you need. Like there's a lot that goes into that and there's recruiting for those positions. So there's a lot of overhead for data analytics, especially at large companies. There's just a lot of overhead. The biggest negative to something like this is that it's very cheap or negative to people like us, data analysts, people, data scientists, people that work in this data environment is that it's really, really cheap to actually run ChatGBT for open AI or ChatGBT or a lot of these other companies. It's crazy cheap and it can do a lot of work very quickly. So it's extremely cost effective. It's extremely efficient for a lot of the overhead stuff. Let's say right now it can do 50% of what a data analyst could do. Let's just say that's essentially $30,000 a year and they might pay like $1,000 for it to do all of these things. So that's just some random numbers. But if it's doing half of what you're needing to be doing and it's doing it extremely cheaply, that's a crazy thing for employers to ignore, for companies to ignore. That's really serious. So that's just, I didn't talk about efficiency so much, but it can do a lot. Let's say it can do it 1,000 times faster. And let's say it can do an end to end project that you would do for, it would take you two weeks and it could do it in 10 minutes. That, for example, would be a huge reason why someone would get this type of system in their company and they would start using it because they can get things done so much faster, incredibly fast. That's something that a human just can't do. So that is a huge positive for automation and a negative for data analysts. Next thing is continuous advancements and the technology. We're very early on. I'll talk a lot about this in my predictions section later, but there's a lot of things that are going to happen in the next several years. Just within the past six months, so much has happened. It's been really tough to keep up with, even for myself. And I keep up with it a lot. Like I'm always on top of it. I'm leaning in a lot today. I'm like this. There's a lot that's come out. And these are just to name a few, but things are escalating at a pace I really haven't seen before. I think it's just because this technology is so... It's not unique to one area. It's being used in all areas. I think that's what's so unique about it. It's every type of work, whether you're in paralegal, whether you are in accounting, banking, fintech, whatever. Everybody's using it in some way. And it's spreading so quickly. It's like everybody is trying to create the next best thing. And again, I don't know how to describe it. It's just fascinating how incredibly fast this is moving. And so as these things progress, they make it better and better and better and better and better and even better than they are today. I'm sure they will be in a year. There'll be a lot better than they are today. So that's a genuine worry because we can't keep up. Data analysts cannot learn as fast as AI is going to learn ever. That's a very genuine concern. So to summarize, at least the negatives. And I call them negatives, but they're negatives for us and they're positives for others. Small business owners, companies, they can save a ton of money. They can do a lot of things where you don't need the overhead of hiring somebody to do it. It'll be a lot faster, a lot cheaper. I mean, that's essentially what AI is promising. And that's what everyone's worried about. They're like, goodness gracious. I mean, if this technology does what people are hyping it up to do, I'm not going to have a job. So it's a really tough pill to swallow. And I'm trying to be as real as I possibly can be here just because this is a very serious topic. I'm smiling, I'm laughing, but this is an extremely serious topic, legitimately. And so now I kind of want to go away from the negatives for just a little bit. It'll kind of be in there, but I want to talk more about the positives of AI just for job stability in general as a data analyst, not as an employer. As a data analyst, here are some of the positives when I'm looking at AI, when I'm working with these technologies, and I've been testing them out a ton. I want to reiterate that. I'm not just seeing stuff online and kind of like spitting it back out. I'm testing all of these things myself. Here's one of the biggest positives for us is right now AI is not perfect. It's going to make a lot of mistakes. And when I say a lot of mistakes, on the surface level, from everything that I've seen, everything that I've tried, not just tried, but all the demonstrations that I've seen, it always makes some mistake. On the very bottom level, like the lowest level, simple analytics, it gets it right. And it's not even, it's not unimpressive. It's still impressive. But that type of technology has actually been around for a while. And I have seen it out there and people even before ChatGPT were worried about these types of technologies coming into the main area and taking over. And I'm like, I've seen these things. And they've been around since I've been in details for the past six years. They've been around where they'll ingest some data, excuse me, they'll do some analytics, they'll spit out some visualizations. That actually is not new. ChatGPT is just making a lot more center stage, right? And so when you watch something like, and I suggest you look into these things and I'll try to leave links in the description for all these, like demos and stuff like that. But ChatGPT's code interpreter is really impressive. But when you get your hands on it, when you start using it, you'll notice it makes mistakes. And that is a really, really strong positive for us, right? As data analysts, as data scientists, people in the data world, it's going to make mistakes. And let's say in five years, 10 years, it's making a lot less mistakes. It's still going to make mistakes. And for a lot of companies, these little mistakes could be huge. If you rely so much on this automation, these analytics, you're just going to blindly trusting it, that's a problem. Because if it starts making any type of mistake and you start blindly trusting it, it could lead to millions of dollars. I mean, literally millions of dollars and lost revenue in not just revenue, but we're talking about client retention, we're talking about saving money, spending money, how you allocate your resources. There's a lot of things that analytics goes into. So just the fact that it's making mistakes, even on not crazy, complicated stuff, or doesn't fully understand it, or you really have to dig in to make it understand or to make it know what you're trying to tell it, it makes mistakes and takes effort. The next thing that's kind of a positive, at least I definitely see this as a positive is that AI can't do everything. And it needs a lot of context and understanding from someone like me who's inputting it. It needs a lot of understanding to really understand it. Now, again, I'm pointing back to the code interpreter because that one's actually somewhat new, but I was able to get my hands on it. And the code interpreter, it doesn't need a lot of context. It tries to extrapolate as much information as it can from the information and the data that you've given it. Now, if that data is well-labeled, if it comes with documentation, and you give it a little bit of context, it does a really good job. The funny thing is, though, is I have not really ever worked in an environment where that is the case. And that's the positive, right? The positive is just humans. Humans are human and we make mistakes. Therefore, AI is not going to just be really good at this stuff until it's AI everywhere, in every department doing everything, which I just, I don't see that happening anytime soon, right? So what I'm trying to get at is that humans are feeding chat GBT. They're feeding AI what it needs to know in order to perform these analytics. And humans just get things wrong. There's dirty data and things like code interpreter can actually clean data, but if they don't understand the nuances of the data, they can't do it well. And it makes mistakes. That's, you know, just going back, it makes mistakes. And so, you know, it's really interesting that, you know, probably the biggest impediment, the biggest roadblock for AI is just humans itself. We just make a lot of mistakes. And, you know, humans are always going to be part of the process. I worked with healthcare, right? Humans are what's inputting data. That makes mistakes. Humans are the ones, you know, working on the front lines and putting in notes and putting in things and doing these codes and all these different types of things within healthcare. And they just make a lot of mistakes. And so you can train it on what to look for and automate certain processes, but there are always going to be other errors. There are always going to be different things that they need to figure out and learn from and train from. And then those analytics, there's a lot of nuances in business. This actually may be my next point. It's actually not. So I'm going to talk about it is that every business or every use case within a business is different. So from what I've seen from a code interpreter is you can give it the data, you can ask it the questions and it'll give you some information. But when you start getting a lot more specific on certain information that you're wanting to get out of it, it sometimes is it needs more information and you're trying to ask certain questions and you're like, okay, I need more data. I need more data. I need data from here. I need data from there. So it can't give you everything just you have to feed it the right information. And with that being said, a business like the one I was working at, we had 100 different clients all wanting unique things. They all wanted different things to be done with their data. And so let me see much time I got. I'm at 25 minutes or so. So I'm just checking the time. But it cannot solve for all of them and all the data so intricately intertwined in databases and Excel spreadsheets and all these things from a client that you're gonna have to tie that all together, put it in a manageable state. You're gonna have to understand the business use case for that data to make sure you store it properly to make sure that you are are cleaning it properly for that use case. And so if you just ask chat you need to clean the data and you'll give it a ton more information about exactly what they're looking for exactly what they want. It's gonna have a really tough time doing it. So a lot of that limitation is actually just human error and humans having to be there to help them understand the business context for the business use case. Make sure that the data is there. Now I can see some of those pieces AI really helping with. You say, Hey, I really want to extrapolate this information. And you say, Can we get that with the data we have? They're gonna be like, No, we need this other information. So you have to go to the business. You have to say, Okay, we need this type of information. Let's start a data collection process. That's stuff that I worked on a lot with. We need to collect this data. Let's set this up. Let's work with our data engineers. Let's work with our database developers. Let's streamline this process, get the data in, and then you could feed it into something like AI and say, Okay, we have this data now. Let's loop back around to this business issue. Let's work through it. But again, it's it just can't do everything. It can't. And even if it does do a lot of things, and it does a lot of things well, it doesn't do everything perfect. So that's kind of a positive thing. The next thing is human instinct and intuition. It's actually, in my opinion, quite important. I think AI is going to help with this a lot. It's going to help us kind of extrapolate information and kind of I use it a lot for things like content creation or generating ideas. I think this is actually going to be a really helpful tool for that. But I think humans, they just have something that AI doesn't, which is just un just knowing things, having that intuition in their back, their head, that just AI doesn't, you know, it's this, it's this database, it's this pool of information that's pulling from and it's doing its best, but it doesn't understand things, right? It's not like it's understanding what you're saying. It's not coming up with these new ideas, these new things that nobody's ever thought of. And in some ways it might. But for things like, you know, analytics, there are going to be things where it's going to tell you, Hey, go do this, because this is what we know to be best. But your intuition is telling you something different and then you have to prompt, you have to tell it, you have to work with it to be like, no, I don't think that's right because of these things. And I think a lot of teams, not just analytics, are going to figure this out. You know, AI is going to be really confident and say, Hey, here's some options. But I think this one's the best. And you're like, that doesn't sound right. That doesn't, that doesn't make logical in intuitive sense. So just being human, having that human instinct, I think is actually really important. Something I don't think AI is going to have anytime soon. The next thing I'm going to read this word for works. I liked how I wrote it. Every company is extremely different. There's no one solution, which is what people think will happen. Every company, every company's data is different. Their needs are different. There will be hundreds of AI tools for analytics and companies will need multiple companies will need multiple AI tools for different uses, but no one tool will solve all their problems. And there will always be more problems. This is more just, I wrote this mostly because, and again, I like, I'm trying, I'm really trying to grasp AI as a whole. How is it, how it's going to impact the business? And, and that's all my predictions will come up in just a second. And that's where I think a lot of these things come from. But I think that there's not going to ever going to be one solution. I don't think chat GPT is going to grow, grow, grow, grow, grow to where they're the one solution for everything. I think for small businesses, like one to 10, maybe I'm just like 50 people, there will be one or two solutions that they'll use for a lot of their work. But I don't think there's ever going to be something like chat GPT employee, who you can just go to as a black box for everything in analytics and get the answer to what's going to connect all your data sources, it's going to do all the data cleaning, understand the business use case, it's going to do everything and just get it done. Realistically in the future, and I'm going to start kind of getting in by predictions, realistically in the future, every company is going to need something different. There's so many completely unique companies out there. Now I run my own company, and I know that chat GPT does not solve all my problems. And actually I use chat GPT quite a bit. I like it. I really do. And I see a lot of these other tools that are coming out. I'm like, man, they're really impressive. They're really good. It just doesn't solve all of my problems. I think when you start thinking about it from a business perspective, not just a data analytics perspective, from a business perspective, there's going to be a lot of issues that you run into a lot of issues. And this is kind of the thing I'm positive about. I'm actually, I think this is a good thing for data analysts in general as a whole is that I'm going to take a step back. Businesses don't want managers. They're not going to want managers doing this type of work, using these AI tools. Even if there was a perfect data analyst tool out there, they're not going to want managers doing that. They're going to hire somebody to do that. And most likely, if they're a larger team, they're going to want multiple people doing that, fact-checking, fixing prompts. They're going to make sure that the data quality is high, that these data pipelines are wrong or are correct and aren't wrong. There's going to be a lot of things that you're going to want to double and triple check for in these things. So, I mean, I think just as a whole, as a whole, I think every company is very, very, very different. Very different. I was going down somewhere with that, but I started looking at my predictions and I wanted to get to these now. Okay, prediction time. This part, I've been adding to this over the past several months. Every so often, I'll run into something new I find and I keep running into these things, and I keep adding to it. So, here are my predictions. I'm going to start, I don't know how I'm going to talk about it, but I'll start. My first prediction, I think every company is wanting to utilize artificial intelligence and even more so in the future. For analytics, I see that as a huge plus and an upside for data analysts like you and I. I see that as a huge plus because these companies don't have data teams and they now feel they need to create one to stay ahead. So, one of my predictions is that a lot of these one man shows, small companies, small businesses are going to want to start using AI. They are not going to want to do it themselves. This technology is useful if you know what you're doing. It is very difficult to use in my opinion still. It's very difficult to use if you don't know what you're doing. Now, when I come into this, I'm coming in from a data analyst perspective, I'm like, wow, this is really intuitive. This is pretty easy to use. But that's because I'm very biased towards analytics. I understand it. I've been doing it for several years, right? And so, I understand what it's doing. I understand what it's telling me. I understand all these things. So, I've been doing it. With that being said, a lot of these business owners, they don't use data. They don't use these AI tools and they're going to want somebody to come in and do that for them. So, I think that overall that's a really good thing. Actually, I think this is another point that I was talking about. Yeah, I'm going to skip. This is a totally different point that I have, but I'm going to kind of merge these two. My number five on here, and I'll just go back to two, three, four. Number five is every department in a company is going to start using AI in some way and want to optimize their data in that department. So, I think we're going to see, and this is, this one I thought is really interesting. And I absolutely am like, I'm like really convinced about this one. We're going to start seeing a lot more specified analytics roles. And what I mean by that is, is right now we have things like healthcare analysts, marketing analysts, financial analysts, right? Those are specific. I think we're going to get even more specific. I think we're going to go start going down to like the department level. I'm talking mostly at like these really large companies. So, at a healthcare company, you're not just going to be a healthcare analyst or a healthcare AI analyst is what they might be called in the future, like five, 10 years, who knows. I think you'll get even more specific, right? Within healthcare, you have claims data. You have certain other break offs from that marketing within healthcare. And typically, you know, you have these different healthcare analysts, financial analysts, like I said, I think we're going to have more specific AI focused analytics in all these departments that didn't have analytics before. And people who weren't analysts before are going to become, whether it's called an analyst or it's called an AI prompt analyst or something like that, somebody who's working with the data that AI is working with, helping prompt it to get the right answers, troubleshooting its errors, you know, doing these data quality checks, whatever you want to call it. I can absolutely see that happening in the future. Whether that's 10 or 15 years down the road, just every department's going to want to use AI in some way. AI needs data and every big company has a lot of it. So, I think we're going to niche down a lot more. That's kind of like it. I wrote down HR AI analyst. I don't know if that one's going to happen, but that's kind of one of my predictions. I think we're going to get a lot more specific with what we're doing. I don't think the term data analyst in the future will be as broad as it is now. I think you'll see mostly like, you know, specific to industries. My next prediction is that I think data freelancing is going to become more popular. More companies wanting AI, not knowing where to look, and the look to consultants and freelancers to come in and help them build their systems and maintain them. The maintenance piece is, I think, going to be very important in the future. I think, you know, as I talked about companies are going to want to use AI. A lot of companies are going to want to use AI. I think that freelancing and consulting in AI is going to be huge. I think it's going to be really big. And as these tools, as more and more tools come out, because there's so few tools right now that are, and let me rephrase that, there's so few tools that people have ever used or known about in the AI world, but there's hundreds being built. So I've seen a lot of them, but I've used a handful of all the ones that are out there. So every company is going to be very different. And something I talked about earlier is that we're going to have AI tools for different industries, very specific to their industries, to where it has more domain knowledge, more understanding of what's going on. They won't be perfect, but they'll be much more specific to that area. And so you're going to need somebody to maintain those systems. Troubleshoot, all of those things that we've been talking about, you're going to need somebody. So I think freelancing, I think consulting actually is going to become more popular in the data and AI world. And just a note on that, and I'm looking at my notes a lot right now, because I don't want to, like, say something that I didn't write down. I want to make sure I'm sticking with what, because these are like things I've like kind of curated, really, like genuinely thought about. The next thing I noted is just that regular people are going to try to do this and fail. As it gets more complex, they're going to hire people with AI and analyst skillset to fill in those gaps. I think the perception that I get is that this is going to make data analysts redundant, or they're going to make data scientists redundant. I've even seen data engineers being redundant, database developers, like anything data related. They're like, it's going to be made redundant. I don't think that's accurate. What I think is going to happen is the data systems are going to get really good. They're already pretty good. Excuse me, give me one second. Need some water? They're already really good. What I think is going to happen, I can genuinely, like, I just see it. I can absolutely see this happening, which is a lot of regular people, like your neighbor, or your HR guy. They're going to try to start using these technologies, and they're not going to be as intuitive as they think it is, especially when you're working with complex data. It's going to be, it's going to be more complex. It's going to be less understandable to a lot of people. Like, again, I'm trying to come out it from somebody who isn't an analytics who really understands creating dashboards and reports and the data and the data collection process and all these things. These things are tough. These things are really tough, and AI is going to help simplify those things, but these things are going to be tough. People are going to have to become data literate, understand the data a lot more, and so people who already have those fundamental skills that have been doing it, that have been studying, creating projects, all these things, I think that is really going to lend itself to transitioning towards these types of jobs that are going to be created, just because people want them. People need them. They see the need for AI in their company, and then they're like, whoa, I don't know how to do this. I need somebody to come in who understands this data, who understands the business use cases to help me prompt these things and get them all situated right and maintain them. That's what I see. My third prediction is I think it's going to lower the barrier of entry for analytics. Whether it's good or bad, you can go both ways, but I think this is going to increase expectations about the customer or this stakeholder is going to get. What do I mean by this? I think the first thing is self-explanatory, but I think that the barrier of entry to getting into analytics is going to be somebody who just understands the data well. I think domain knowledge is going to become way more important than it did in previous years. I've always told you that technical skills are really important, and they are. I'm going to talk about that in a little bit. Technical skills are still going to be important, but I think if you really know the domain knowledge of, let's say, healthcare, I come from healthcare, if you really know the healthcare side of things and you have some data background, you'll be able to get a job faster. Somebody who was a doctor before, somebody who was a nurse before, somebody who has worked on the data side of these things or has first-hand experience with these things are going to be able to transition into analytics easier because they understand the business practicalities that you need to know. I don't know if practicalities was the right word, the business practices. If they understand the business practices, they understand how the data is collected, how the data flows, and then they start using these tools, they start learning the data side of things. I think the barrier of entry for those people transitioning in is going to be a little bit lower because of these AI tools. The expectations of the customer are going to increase. This one, I think, has always been true, so I don't think this is anything new. I'm not saying anything revolutionary here, but here's what I will say. When you start using these AI tools, the expectation is that the quality is going to go up quite a bit. They're paying for this technology that is supposed to be state of the art best that you can buy and their expectations are going to go up. You as a data analyst out there, we're coming into a company, they're going to expect that you know how to use these things really well to get exactly what they need out of them. I think that is part of my prediction in this. This is a subnote. I think this is actually going to cause a lot of frustration in the data world because I think people are going to have such high expectations for AI in general and it's not going to get them 100% there. As of right now and where I see in the next five years, 10 years, it's just not going to be perfect. There's a long way to go a long way to go for it to be really, really good where it doesn't need as much oversight and hands on experience or hands on oversight is what I was saying that you wouldn't need something like a data analyst. It needs a lot of work and even then our company is going to trust that. It's really tough to say. The expectations are going to get a lot higher. The next one, I think that my number four is knowing analytics and the tools and become very important. As AI starts utilizing tools, if something goes wrong, you're going to need somebody who knows the systems and software well to troubleshoot and get it right. Now, this is happening already with things like auto GPT, baby GPT using these tools, things like plugins within chat GPT, which I think are going to be even more so, and things like GitHub Copilot, Microsoft Copilot 365 that's going to be integrating chat GPT into a lot of these things and a lot of their Microsoft applications. What happens when something doesn't go right? You need somebody who knows these tools well. So what I foresee is people are going to hire people who already know these tools to come in and use them, monitor them, use them with AI. So if they're wanting a power BI visualization specialist, or let's say they're a very common job right now is a data visualization specialist. Data visualization specialists in my opinion are some of the easier jobs to automate in the data analyst sphere because data visualization from everything that I've seen is something that AI gets pretty right or gets it better than others, things like data cleaning, it gets wrong quite a bit. It doesn't understand all the business side of things. So having that domain knowledge, but data visualization, I feel like it gets right more often than not. Now, you still need to prompt it for other things. You still need to create different types of visualizations for different things. So I don't think that job entirely is going to go away. I do think that that job specifically is more susceptible to being automated in my opinion. So I think knowing these tools though is still going to be extremely important because as you get into it more on a base level, you could come in somebody who knows none of these technologies and they could kind of get by I think right, which is impressive with AI, they could kind of get by doing a little bit of data analysis. But as you get into the more complex things, and you're starting to integrate different tools, like let's say it's you know, writing all your Python for you, it's trying to create all your visualizations for you in a different tool, it's creating data pipelines, it's you know, cleaning data, you don't want just some random guy doing that. That is still very specialized. So you knowing these tools, knowing how to use them, how all these pieces fit together and helping these AI systems do that properly, I think is actually going to be really important. I don't actually see that happening to that level, like just somebody kind of knowing the tools and kind of piecing everything together for AI to do it. I don't see that exactly happening for several years. Even then I, you know, it's it's tough for me to imagine AI being that good, where you just need somebody to supervise. I think it's still going to require human intervention going in, helping with this work, helping prompt them, help them understand things. Again, I just don't see that happening. And I'm going to skip to number six. And I kind of I kind of mentioned this already. But again, this is like my thoughts over the past several like months. Number six is I think more and more companies are going to try to use AI on their own and realize it's not that easy. It's not just a plug and play. And you get everything you need. You need good data. You need good pipelines in place. You need someone who knows what they're doing. As this continues to happen, more and more companies will turn to data professionals to come in and help maintain these solutions that they thought they could do themselves. It's just a general sentiment that I've seen online is that people are going to do everything themselves or they're going to have AI come in and do everything for them. I just I personally don't see that happening. I think that a lot of and again, a lot of it comes back to like time and accuracy. Managers, small business owners, people like me, I can't do everything myself. I can't like I'm growing my business. And there are things that even if I wanted to, I couldn't automate it to be done. I need to have someone, even if I have them use an AI tool, I need them to come in and use it for me. At least right now, right? Let's imagine, and next thing I'm going to talk about is it's five years, 10 years, 15 years, one of my predictions. But as of right now, I would need to hire them to come in and kind of manage that system. I just I'm not going to do it myself. That's ridiculous. It does not make sense for me to do that for my company. And it does not make sense for other people to do that in their company. And so as long as AI needs some oversight, and it will need it for a long time, I'm sure of that. As long as it needs oversight, there's a lot of work to be done. And that's not going to be something that people just like regular people who have no idea what they're doing are just going to figure out. Unless they study it a lot. It's just I don't see that happening. So this is what I'm going to get. I'm going to talk about predictions five years, 10 years in a little bit. But I want to kind of bring us all back to about like five or six months ago. Many of you guys may not know this. How much time do I have? Okay. Many of you guys may not know this. I'm building a company right now. My consulting company, right in analytics. I'm also building a website called analystbuilder.com. That's going to have courses and a lot of analysts focus things on there. And about five months ago, I literally like stopped. And as all these things were developing, and I was like, Oh my gosh, I don't know if I'm going to have a job in two years. Very, very genuinely, like I, I, for about almost a full week, I was just like, I don't want to say depressed. I don't want to say that's the word. But I very much was like, Oh my gosh, I was having like an existential crisis. It's like, what do I do? And I really more or less started worrying about my audience. I was like, I was like, does this mean everyone who's like following my channel? Does this mean, you know, there's no future for them? I mean, I really, I had this like really like serious moment where I was just like, I don't know if this is, I don't know what's going to happen. I really don't because things were advancing so quickly. I couldn't keep up. And it felt like at every turn, things were getting better and things were getting better. After that week, I started, I was like, okay, I gotta stop sitting on my butt and feeling sorry for myself. I need to like really dive into this and seeing how viable this is. Because I, I have, I believe I have a duty to you guys to inform you the best I possibly can. Like, if I thought analytics was going away forever and they were all going to be automated, I would bring you the honest truth. As I dug into it more and I dug into it a lot as somebody who works, who's worked on kind of every process and analytics from data collection to data cleaning to business side, developer side, visualization. I feel like I've worked on a lot of different areas. You know, the more I started using it and trying it out in these different areas, I'm like, man, this is extremely impressive. But I can already see a lot of cracks, a lot of cracks and something that I kept finding over and over and over again over the past four or five months that I really been diving deep into it is that AI is just not, I don't see it taking over. I don't see it. Let me rephrase that. I don't think it's the right word. I don't see it replacing data analysts fully. I see it being a huge tool and asset, but I also see a lot, you know, I see those fears and concerns because I was in that exact place like four months ago, five months ago. I was in the exact same place, very much worried. I was like, not just for my, not just for me. It was more like, I mean, I feel like I'm a teacher on YouTube. That's how I feel. I feel like I teach people with data analytics to help go out there and get a job that they want and that they love and that they'll enjoy doing. I'm like, is everything I've built, is all the people that I've been teaching, are they just, is there no future for them? And I, from my personal opinion, as of right now on March 8th of 2023, I really don't see that happening. I don't think, I don't think we're looking at mass layoffs soon. What I genuinely think is going to happen is it's going to be a somewhat slow rollout. Even though technology is advancing very quickly, I think the rollout of it being integrated into all these companies is actually going to be a lot slower than you'd imagine. And I think as they roll out a lot of these artificial intelligence, chat GPT, LLMs, whatever you want to say, as they roll these out into companies, we're going to find a lot more issues. We've been using this personally on a personal scale. Once you start integrating into companies, you're going to find a lot more issues with privacy, ethical concerns, errors, hallucinations, which have not gone away, and I haven't even touched on it, but that's part of the mistakes. It still makes hallucinations. I think that we're going to find a ton of issues, a ton. And over time, those will get better. But my prediction in the next five years, from now till five years, so we're looking at 2028, I see AI systems starting to be integrated at most companies within the next five years, or integrated in companies in the next five years. I don't see many data analyst jobs being replaced fully. I think that there may be a slight slowdown in hiring an data analyst in the future. Now, there are people that retire. There are people that come in. There's already, there has been for the past six years since I've been in data analytics. There's already been a tough barrier of entry for entry level who have no experience. I think that'll always be there, so I don't see any different in the next five years. What I will say is data analysts who have experience, entry level, mid-level, senior level, you're going to find more opportunities and I think more companies hiring for data professionals with these AI systems in the next five years. I think we will see an increase in demand for that. With that is a lot of opportunity from people who aren't data focused, people who were, like I said, a nurse, who wants to break into here. I think it's going to make it easier. So if that's you, that's fantastic. But if you're coming out of something like high school and you have no experience, you have no experience in any job whatsoever, I think it's going to be tougher for you. So people with domain experience, people who have already worked jobs that wasn't data focused, but understand the business side of things, I think it's going to be even better. So that's five years. Now let's talk about 10 years, 15 years. I'm going to kind of lump that just into the future. Like what does the future of analytics look like? I see AI being integrated into a lot of processes in analytics, just like I think most of them. I think there will always be need for humans to have their hands in the analytics side of things. I think analytics is here to stay. I don't see analytics going anywhere. I really don't see analytics being automated fully ever, at least not in the foreseeable future. I couldn't imagine in the next like 10 or 15 years, I just can't, I can't see it, especially with all the issues that I know are going to arise in the next five years with AI. There's going to be a lot of companies that just straight up ban it. Another prediction. I think there's going to be a lot of companies that ban a lot of AI tools for a lot of different reasons. And I don't think, I don't think, just to preface this, that in the next 10 or 15 years that we'll see an extremely dramatic layoff like we think about in the news. We're thinking Google is just going to be automated. Entire departments are going to go away. It just, it seems in my opinion right now, somewhat unfathomable because I just know how I've been doing so much research on these systems. I just feel like I know they have so many downsides. They have a lot of upsides. There's a lot, a lot, a lot of opportunity out there, but there's a lot of downsides as well. I think a lot of companies will see that once they start using them. They're going to be like, wow, this is amazing, but I'm already seeing some of the cracks in the downsides of using AI. That's, that's kind of what I think. So I'm going to, I'm going to, in just a second go into what I think you should be doing right now as somebody who's breaking into analytics, as somebody who is trying to get an entry-level analyst job. Before I do that, I just want to say I've been in the headspace in the past of like Doomsday, Prepper. This is like the end of analytics, we're about to see mass layoffs and mass. I've kind of went down that rabbit hole for a little bit all the way to AI can't do what humans do. It's just, they can't do it. They're just robots, you know, train on whatever they can't do what we do. I've kind of been on both sides at different times now, kind of in the middle where I think that there are some very real concerns for people who are trying to break into data analytics. And I think those concerns are extremely valid. And I hope I've kind of, you know, validated those fears because I don't want you to feel like you're the only one thinking about these things. I also see a lot of positive things about AI. It's going to speed our work up a lot. It's going to allow us to do better work, faster work, and get more done. And I think that's a fantastic thing. In general, I think pro, I am AI, I'm pro AI. There are areas where I'm like, if this were to go south, if AI started doing everything somehow, or it started, you know, like in the worst case scenario, it starts like, you know, taking over your systems or, you know, whatever, like Skynet or something, right? Or Terminator, you know, however this goes. I could also see that being very bad. But again, I've been talking about, I've been talking for like, feel like an hour. I feel overall positive about it. I am skeptical. I'm hesitant, as well as others, right? I'm just trying to, you know, I'm trying to be as honest as I can. That's my genuine take on it. So what would I do if I was just starting out today? Now, I have, what, six, almost seven years of experience now, working in healthcare, IT, consulting. I feel like I have a lot of experience. So I feel like for me, I have a lot of these tools, I have a lot of these skills. I have a good reputation. I think for someone like me, it's not going to be crazy hard. A lot of other people who have experience like I do, who have experience in analytics and have for several years, I think most of you guys are going to be okay. I genuinely do. I don't see a huge mass layoffs for senior level, even mid-level analysts. I think the entry-level analysts is going to be the new mid-level. So here's what I would say is that I would do. And this is my personal belief. You don't have to do this. This is what I would be doing. If I was starting from scratch, I was like, I just want to break into analytics at some point in the future. I would learn the basics first because even with all of these, specifically, let me go back. What's it called? Microsoft's Copilot 365. ChatGPD is going to be integrated into things like Excel very soon. Something that data analysts work a lot in. I would still learn the concepts for using Excel. And the reason for that is because when you start using ChatGPD for Excel, it's going to start popping a lot of these functions, a lot of these pivot tables and visualizations, and you should know what it's actually doing. I am not a huge believer that you should just let it do what it does and blindly believe it. So I would still highly, highly, highly recommend learning the basics of Excel, SQL, Tableau, Power BI, Python, and I think that's really, really important. I think that Python, and that's a lot of what ChatGPD is built on and used for and in data analytics and stuff like that. Python is super popular. I would learn Python. Probably I would learn that one maybe sooner than I have said in the past where I'm like, you don't really need to know Python. I personally think Python is going to stick around and there are going to be different programming languages that are built off of that specifically for AI, like an AI language that one of my buddies just sent me called Mojo, which is literally built to be red like Python and it's for AI languages. So something like Mojo, those are things that you, if you know the basics of Python, you're going to be able to pick that up quick and learn that. So I still highly, highly, highly recommend learning the basics, even up to intermediate and advanced levels if you can. The reason for that is after you start, after you know those things, I do believe that you need to start learning AI. You don't need to be an expert in it. You need to start dabbling in it at a minimum. I think that is going to be extremely important in the future. So I would start dabbling with chat GPT, start trying to do some data analysis with it, start testing it. You know, that will only help you learn, right? You're going to learn, but you're also going to make yourself more job ready for the future. Five years down the road, I think a lot of data analysts are going to be using AI on the daily. So I'm kind of adding that to my like a bucket of things you need to learn, learn AI, start with chat GPT. I think it's like one of the simpler ones. Then, you know, this co-pilot is going to come out not too long. So you can start messing around with it in like PowerPoint and Excel and, and, you know, different tools and then plugins with chat GPT into your, into these tools and start messing around with those things. Again, this is May 8th of 2023. In the six months, chat GPT can have plugins into Power BI, which I'm sure they're already going to do that through Microsoft, but it could have plugins into Tableau, into Looker, into any tool that you're using. Start learning the basics now. Start learning chat GPT right after, or AI. And then, oh yeah. So I basically said, learn those skills, learn how to integrate AI into them, and then learn how to solve business problems. I think understanding the business side of things, understanding your client's needs, your client's wants, your stakeholder needs and wants and translating that into actionable insights. You cannot go wrong. And that's tough to do when you're first starting out. Find business use cases, you know, find projects online. I have tons of, so something that I would do, I have tons of projects online that I've done for free on my YouTube channel, right? You can take those and try to replicate that using AI. Try to do what I did in those projects, and you can use AI to help you build that. Do it faster than I did it, right? That is something I think is a very real use case. And I think in the next two to three years, if you're doing those things, you are going to really set yourself up well. Again, I don't, I, I, maybe I'm just a sucker. I'm just really a positive person. I just don't see analytics going anywhere. I think that we're going to have more data and more opportunities with the data that's being generated in the future. AI is going to help with that. I think it's going to be a huge tool, a transformative tool, something that, you know, I couldn't have comprehended even 10 years ago thinking about like, like college. Like it's, it's a huge transformative tool. It's going to transform a lot of things, but it's still in my opinion a tool. It's not somebody who's going to come in and do everything for you. So that's, that's what I'm thinking. Now normally during these, these episodes, these long, long episodes, I do two things at the very end. I do a question of the week, which I'm not going to do. I'm not going to do, because I think this is a really serious topic. I'm not trying to, I've run, I've run a long time. So I'm not going to do a question of the week this week. What I will do is a vegetable of the week. And the vegetable of the week is just saying, Hey, I watched all the way through. And it doesn't mean it necessarily agreed with me. It just means you're taking this seriously. So I'm going to reuse a vegetable. And because this is so serious, in my opinion, such a serious topic, I'm going to reuse one that has a real close part, holds a soft spot in my heart, which is from one of the very first episodes I ever did for the Alex the analyst show. And the vegetable of the week is going to be jalapeno. So if you watched all the way through, and you were, you could be concerned about AI, you could be hopeful about AI, you could just be getting into analytics, you could just be, you know, looking for a senior position in analytics, you're watching this because, you know, either you're, you're nervous, you're excited, or you're not sure what to think. And that's what this is all about. And so if you stayed all the way to the end, put jalapenos in the comment section below, I will know that you were taking this very seriously. You care about your future. If you don't put in a jalapeno in there, I'm going to assume you don't care. But seriously, this is a heavy topic. I genuinely, I put weight, not way too much thought. I put a lot of thought just because I care so much about it. This topic means so much to me. You guys mean so much to me. I wanted to make sure I was bringing you good information. I hope I brought you good thoughts that maybe you haven't thought about before, good ideas that you haven't thought about before. And I hope, because I know there's a lot of people out there that are really worried. I hope that I put some people at ease. In my heart, I am at ease. I am not crazy worried, just because I feel like I have a pretty good understanding for how companies think and work and operate. I feel like I have a pretty good instinct on this, just on how they're going to respond to AI, how it's actually going to go with implementing these AI systems. So I feel like I hope for the most part, AI I see as a very big tool. And I'm hoping I put some people at ease. It's a very serious topic. It's people's livelihoods. It's people's futures. It's crazy important. I can't say this enough. This technology has the ability to transform lives for the better for the worse. And for the majority, I think it's going to transform it for the better. I don't know if I refuse to believe it or refuse to think it, but I'm not going to succumb to the doomsday prepper end of the world, end of data professionals in general. And there are people out there like that. I just think they maybe haven't been in the industry long enough or understand it enough, or they're just crazy negative people. I don't know. It's hard to say. I kept saying the date on here, and I kept saying that because I want to come back to this in one year. I'm making a note. I already have the note on my calendar for when this launches, which should be next Tuesday. Let me see. Why is my thing not here? What the heck? Here we go. Next Tuesday is the 16th. So on May 16th of 2024, I'm going to come back and see how accurate it was. A lot of things are going to change. A lot of things are going to change. I have a strong hunch and all of my research is pointing to that it's going to mostly be for the better. That's what everything is telling me. I hope I'm right. I really hope I'm right. If I'm wrong and in five years, you know, analytics is completely gone, you guys can be mad at me, and I want you to be mad at me. But with all the information I have now, I'm giving you the best. I'm giving you the best I got. I really hope, I really hope if I'm wrong, you guys can forgive me. I hope if I'm right, you guys can celebrate with me. But this is, again, now I'm just rambling. You guys can go. You already typed jalapeno down below. I'm just going to hang out for a little bit. This is a long episode anyways. Nobody cares. Nobody cares if it goes an extra five minutes. Just a really, I don't know. It's really been weighing on me too. It's really been weighing on me. It's really been weighing on me. Now, if you're staying, if you're sticking around to the end, I'm going to cut you down some secrets and some other things that I've been thinking about. I'm starting this company or starting this platform and website called Analyst Builder. It's not released yet. It'll be ready in a month or two. I've been working on it for over a year, and it's for data analysts to learn data analysis, to practice data analysis, and I'm going to grow the website to be something incredible. I think it's going to be one of the best data analysts websites that is out there, like legitimate. It's already incredible, as is. It's simple. It hasn't even gotten to the stuff that I want to get on there. Four or five months ago, when I had that moment of, oh my gosh, it's over. I was like, AI won. There's no use in me building this anymore. I should stop. Tell the developers to stop building. I legitimately had that thought. The more I looked into it, the more I'm like, this needs to happen. This is going to be more important. This will be my platform to help usher in the new data analysts of the future, where I'm going to start teaching. I am diving deep right now into AI. One of my goals is to integrate AI into the platform, do courses on AI, teach people how to use AI, as well as on YouTube for free, of course. This website, of course, will be some things that we paid for. These are the things that people are going to need to learn, and someone's got to teach them. Universities are not going to do it anymore. Random rant. But with chat GPT, AI, LLMs, all these things, the need for higher education is going to go down substantially in certain areas. I know it. In 15 years, I think it'll be like, you went to college? That's pretty cool. Nobody does college. None of my friends went to college. It's going to be like that. It's that transformative. I don't think waiting for education to catch up is the way to go. I think platforms like mine are going to be, I don't want to say the future, but for people who want to break into analytics, my goal is to be on the front lines and creating content on YouTube and on Analyst Builder to kind of keep people up with technology and what's out there and what's being done. That's where my heart is. My heart is still in YouTube. It is still very much in my audience and my community. I feel like I owe it to them. You guys have given me so much. I feel like I legitimately, I feel like I owe it to you guys to keep going. That really pushed me through to be like, okay, if that's what I'm thinking, I need to get out of this funk. I need to really dive into this and see if this is all true. You'll see claims on Twitter and LinkedIn, like AI is ruining data science, is ruining analytics. There's no jobs. Don't do it. The more I looked in, I was like, that's just not what I found. I'm really happy I kind of got out of that funk. I'm still here for you guys. I am still going to be creating content. Now I'm just looking at myself. I'm still going to be creating content. I'm still going to be creating courses. I plan on staying as up to date on this technology as I can so that I can then teach you guys and I want you guys, my audience, my community, I want you guys to be the first ones learning these things that you can then go out there and get these jobs because there are going to be jobs. It's not going away. I'm just so certain about it. With that being said, I've talked for long enough. I am now getting headache and I also have blood drawn this morning for life insurance policy. I had some blood drawn this morning, so now I'm a little lightheaded. So I've been sipping on water. So with that being said, I hope that I brought some positivity or positive thoughts on it around. It's just a heavy topic. It's a really heavy topic and I can't, I could try to make it fun, but then I just, I'd feel disingenuine because this is a very heavy topic in my opinion. So yeah, that's how I'm going to end it. Just it's a heavy topic, but I'm overall positive. I keep saying it, but you know, I just keep thinking about it. I'm overall positive. Thank you guys for joining. I do appreciate it. I really do. If you're out there listening still, I mean you guys are the real heroes. You guys are the real heroes. So with that being said, have a good week. I'll see you on May 16th on this lunches. I'll see you then. All right. Well, take it easy. Have a good one. Enjoy your day. Give your significant other, family member, friend, child, a hug. Not for me. That'd be weird. Just from you. If you really want it to come from me, you could. Don't do that. Don't do that. All right. No for real. I'm out of here now. Have a good one. Goodbye and I will see you in the next video.