192: Will Data Analysts Survive 2026? 3 Major Predictions

2026 is here, and here are my three predictions of what you can expect as a data analyst this year. Number one, I think it's going to actually become easier to land a day job in 2026 than it was in 2025. Over the last few years, there has been a lot of false information, misinformation, and a lot of confusion about what's actually going to happen with data jobs. Now, I can't say that I'm a magic fortune teller, but I have been able to look at some of the data since 2019. Uh, and obviously like data analytics was really hot from like, what, 2015 to maybe 20 21, 20 22. Around 2022. Something crazy happened where we maybe got a little bit saturated. Um, and it's not that data jobs went down, it's just that they kind of started staying about the same. From 2022 to 2025. There wasn't a whole lot of growth. There wasn't a whole lot of decay, but it was kind of just stagnant where it was. Uh, with that, I still think that the data analytics and the data analyst profession was still being quite hyped. I mean, I understand why it is a really awesome career, but I think we've seen a lot of the hype die down. I think a lot of the hype has moved towards like, uh, AI and automation. And with that I think there's people who are probably less interested in becoming an analyst. Data analyst and more interested in becoming like an AI person or an AI engineer. I don't even know what the titles are for these AI roles. I don't think anyone really knows what the titles are. Uh, but I think a lot of people are less interested in AI or a lot of people are less interested in data and more interested in AI and automation. And because of that, I think you're gonna see less people applying for data analyst roles. Now I think this, there'll be like the same number of data jobs open in 2026 as there was in 2025. But I think there's just gonna be less competition. I think people are gonna try to get into AI and automation instead. I think that's great. I think AI is really cool. I think automation's really cool. I use both in my business. Um, but you still can't beat the bread and butter of data analytics. Uh, AI is definitely really cool, but it's also a little bit overhyped and we are for sure towards the end of some sort of AI bubble. Now, once again, I'm not a fortune teller. I don't know when the bubble's gonna pop, but the bubble's gonna pop eventually. Um. That's, that's not to say that I still wouldn't buy AI stock. I think AI is going to be huge down the road. Um, but data analytics is a lot more proven than AI at this point, and I think it's a really good investment for you and your career. Um, it's still going to be hard to land a data job, but I think there'll be less competition next year. So I think it'll be easier for people to pivot into data analytics just because it's not as hyped as it once was. There'll be less people kind of applying for those entry level, uh, data roles. Uh, and I think it'll just be a little bit easier. My prediction number two is that companies will start to adopt AI more, uh, to do data analytics. And that doesn't mean that there's gonna be less jobs. That doesn't mean that AI is coming for your job. It doesn't mean that it's all over. Uh, data analytics is here to stay. Now will it change down the road? Sure. I'm sure it will. But like what industry hasn't changed in like a 10 year period, right? Like is the automotive industry today the same? It was 10 years ago. We're still driving cars, but it looks completely different Ev self-driving. I can't even tell you like how different it looks. Every industry changes in a decade's time, and that'll be true for data analytics as well. I mean, 10 years ago we didn't even have Power bi, so we we even ignoring all of the AI stuff, like data analytics is obvious, obviously changed a lot because one of the most fundamental tools, data analytics, did not exist a decade ago. I think companies are. Pretty slow to adopt new technology. At least like the enterprises, like we're talking like the Fortune 500. Of course there's companies that are outliers that are gonna perform well, uh, using ai. Um, but a lot of companies are slow to adopt technology. They're slow to actually implement technology. And I know, 'cause I literally worked for what, like the seventh biggest company in the world at the time when I was there, I worked for ExxonMobil as a data scientist. I can't even tell you how much of their analysis at ExxonMobil was done in Excel. I'll say that again. Like a lot of our analysis at ExxonMobil was done in Excel. Python's been around for how many years? What? 1990. So 35 years. And we weren't even using a ton at ExxonMobil. Uh, is Is Python better than Excel? In my opinion, yeah. It's great, but it's hard to actually make progress in these big companies. It's hard to adopt new technologies. It's hard to roll out new technologies. There's all sorts of different problems and issues. Like even getting Python on your computer at ExxonMobil was probably a two week process. It probably takes me, if I were to like get a computer, it maybe takes me 30 minutes to get Python installed on it. Right? At ExxonMobil, it was like a two to three week period, just because you had to ask for permission. They do all these security checks, you had to download it, it would break. It was so hard to even download Python. Uh, and so these larger institutions like Humana, Wells Fargo, chase Bank. Like, I'm sure they're gonna want to adopt ai, but it, it's going to happen over years, if not decades, where that rollout actually comes out. Now, I do think a lot of enterprise companies are going to make some progress on that this year, and I think mainly it's going to be because of the integrations with the companies that are already using. So, for example, a lot of enterprises have a pretty good relationship with Microsoft. They're paying for enter enterprise, Microsoft, uh, plans. And I think they're gonna do a good job with copilot and kind of mingling that with chat GPT. So I think that will probably be something that you see these enterprises doing over the next year. Uh, I think Google's made a lot of progress with their AI products in the last, like quarter alone. So those who have a. Google Enterprise plans will probably start to use AI a little bit more, but I think there's a lot of stumbling blocks for enterprises to use AI that has been existing in the past. I think that'll, uh, become a little bit less of a barrier this year, uh, but still a barrier. Nonetheless. The way I predict this actually, like rolling out to companies, the way I see it is it'll probably be at an individual level. So a lot of like data scientists don't even really have a corporate AI plan right now. Um, but I see a lot of that changing this year. There's a lot of solutions that have made a lot of progress. You guys have seen me do sponsorships with Julius ai. Um, they've made a lot of progress with their connectors. The, the biggest thing is it's really hard to have secure and connect connected data, and so Julius has made a lot of progress there. I think Hex has a, a really good product that will make some progress. Like I said, Chae, Claude and Gemini from Google have all made a lot of progress in the last little bit. That makes it easier to connect to your data and have your data be secure. So I think a lot of like individual data analysts and data scientists will start to get access to ai. Augmented tools, and I don't think it's gonna be replacing them. Like it's literally just a tool for them to be using. And if you think that AI's going to replace you, to me it kind of shows you haven't really used AI to analyze data yet because it's not there. It's definitely not there yet. Um, and like for me the other day, uh, I, I was analyzing some data and I was just using AI to do it. And like, you still have to think. There's so much thinking, there's so much planning. You have to know what to do. You have to have the idea. You know, AI can spit out 10 ideas, but like seven of 'em are usually really stupid. Three of 'em you can't even do. So 10 outta 10 ideas like don't even work. Uh, so they still need you to, to be thinking, um, you're going to be also more of the bridge between the analysis and the actual business. We were at ExxonMobil, we automated a lot of stuff that humans were doing using Python and machine learning. You think, you think that just magically the people who were doing their job lost their job and just got laid off? No, that's not what happened. It was just a tool to help them do their job better. And a lot of the times they actually overruled our decisions, our, our decisions as in the algorithms decisions. Um, this was for buying crude oils, like deciding what crude oils from around the, the world we were gonna buy. This was for deciding how much. Gasoline we should send to your local Exxon gas station. Like I created a machine learning algorithm that would basically predict that, and I thought it was pretty good, but a lot of the times it was missing context. A lot of the times, uh, like these traders knew best. And I think that's still gonna be true today. Like, AI is really smart, but it's not replacing a human. And, and if it is, then why? Like, if it is, like why has, has it like it's just not good enough. I have tried AI to make social media content. To do data analytics, to make video scripts, to make thumbnails, and it's helpful, but it's never, ever, ever gotten it right on the first time. So I don't think AI is coming for your job, but I do think that companies will start to use ai. I think your job as a data analyst is gonna change. More into that connector from the actual data analysis to the business, I think it's gonna be more important to know what to do versus how to do it. So like for example, like you can do a pivot table in Excel, you can do a pivot table in chat GPT, but you need to decide when to do a tip pivot table when it's appropriate. Like when do I wanna aggregate data based upon categorization? And group buys, right? That's something that you're still going to need to do as a data analyst. And that leads me into my third prediction, which is that your domain experience is more important than ever in 2026. And what I mean by that is like when you look at a data analyst, they're analyzing data, that's half of their job, but then the type of data is their other half of their job. What's the data about? Is it healthcare data? Is it financial data? Is it music data? Is it marketing data? Is it sales data? Like there's always half of the domain in a data analyst role. And I think that's gonna matter more than ever because once again, the how to do your analysis is becoming less and less important. The actual skills, like the actual analysis skills to, to do your analysis are becoming less and less important. What's more important is knowing what to do, when to do it, and what the results actually mean and, and how to translate that to the business. So if you've been a teacher before. Like, you know how a classroom works. You know how a school district works. I've never worked in a classroom. I might be better at data than you chat. GPT might be better at data analysis than you. I don't think it is. But let's, let's just for this argument's sake say that it is, but it definitely does not know your personal classroom, your personal school district, or our really, how a classroom or a school district work in real life. Like you've actually been in the front lines and understand. The industry, and that's gonna be really important for the rest of 2026. And moving forward, you're gonna get really deep and different, uh, data niches or I guess industry niches, and your knowledge is going to matter. And I've, I've told the story before, but when I worked for ExxonMobil, um, at the time I didn't have my master's in data analytics. I had a bachelor's in chemical engineering and I, there was these competitions, they called them hackathons where they would basically take everyone in the company and say, Hey, here's a data set. What can you do with it? Like, what type of results can you get for us? What type of insights can you pull? What type of tools can you make for us that would be useful for our company? And I'd enter these competitions. And some people in these competitions were literally like PhDs in computer science, PhDs in mathematics. These people were a lot smarter than me in terms of computers, statistics. Machine learning data, like these people were really, really technically and academically smart, but I was able to actually win one of these competitions because no matter how much smarter they were from like a computer algorithm, mathematics sense than me, I knew the business and I knew the domain better than them. They had spent all this time studying. They didn't know anything about chemistry. They didn't know anything about manufacturing. They didn't know anything about engineering. And that's something that was my domain. That's what I studied in school. I had worked for the company, like I understood. I was like really hands-on with like refining and manufacturing of gasoline and jet fuel and stuff like that. And I actually knew what was going on. And so when I was analyzing the data, I was able to analyze faster than them because I actually knew, oh, like this is what sulfur is, this is why it's good, or this is where it's bad. That would take them a long time to actually figure that out. Uh, and so I was able to work faster. I was able to interpret my results faster, and I was able to actually just come up with better insights than they were despite them being more talented than me. And I think for all you career pivoters who are listening, that's really exciting. That's really refreshing because your pivot actually isn't a disadvantage in 2026. It's an advantage. It's what puts you above the rest of the people around. You like the stuff you studied in school 20 years ago, the stuff you've been working on the last seven years that you, that you kind of hate, you wanna get outta that job. That information that you learned isn't meaningless. You can hold onto it and actually becomes an asset to. You all this to say, I think 2026 is gonna be a great year for you. I think you have a great opportunity to pivot in analytics to level up in analytics. I think people are kind of sleeping on the analytics right now because let me tell you, it is the bread and butter. It is proven and there's so many companies who are still under utilizing how much they're doing data and analytics. So while everyone's kind of interested in AI and automation, stay true to data analytics. And you can use your previous domain experience to pivot in and use ai, but don't be afraid of it. Like AI is going to be a tool that you're going to be using down the road, but it's not replacing you anytime soon. Data analytics is far from over. I think we're just getting started.