192: Will Data Analysts Survive 2026? 3 Major Predictions

Speaker: 00:00:00

Avery Smith-1: 2026 is here, and here

are my three predictions of what you

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can expect as a data analyst this year.

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: 00:00:07

Number one, I think it's going to

actually become easier to land a

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day job in 2026 than it was in 2025.

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Over the last few years, there has been a

lot of false information, misinformation,

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: 00:00:20

and a lot of confusion about what's

actually going to happen with data jobs.

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Now, I can't say that I'm a magic

fortune teller, but I have been able

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to look at some of the data since 2019.

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Uh, and obviously like data

analytics was really hot from like,

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what, 2015 to maybe 20 21, 20 22.

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Around 2022.

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Something crazy happened where we

maybe got a little bit saturated.

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Um, and it's not that data jobs

went down, it's just that they kind

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of started staying about the same.

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From 2022 to 2025.

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There wasn't a whole lot of growth.

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There wasn't a whole lot of decay, but it

was kind of just stagnant where it was.

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Uh, with that, I still think that the

data analytics and the data analyst

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profession was still being quite hyped.

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I mean, I understand why it is a

really awesome career, but I think

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we've seen a lot of the hype die down.

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I think a lot of the hype has moved

towards like, uh, AI and automation.

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And with that I think there's

people who are probably less

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interested in becoming an analyst.

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Data analyst and more

interested in becoming like an

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AI person or an AI engineer.

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I don't even know what the

titles are for these AI roles.

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I don't think anyone really

knows what the titles are.

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Uh, but I think a lot of people are

less interested in AI or a lot of

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people are less interested in data and

more interested in AI and automation.

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And because of that, I think

you're gonna see less people

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applying for data analyst roles.

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Now I think this, there'll be

like the same number of data jobs

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open in 2026 as there was in 2025.

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But I think there's just

gonna be less competition.

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I think people are gonna try to

get into AI and automation instead.

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I think that's great.

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I think AI is really cool.

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I think automation's really cool.

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I use both in my business.

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Um, but you still can't beat the

bread and butter of data analytics.

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Uh, AI is definitely really cool,

but it's also a little bit overhyped

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and we are for sure towards the

end of some sort of AI bubble.

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Now, once again, I'm not a fortune teller.

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I don't know when the bubble's gonna pop,

but the bubble's gonna pop eventually.

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Um.

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That's, that's not to say that

I still wouldn't buy AI stock.

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I think AI is going to

be huge down the road.

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Um, but data analytics is a lot

more proven than AI at this point,

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and I think it's a really good

investment for you and your career.

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Um, it's still going to be hard to

land a data job, but I think there'll

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be less competition next year.

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So I think it'll be easier for people to

pivot into data analytics just because

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it's not as hyped as it once was.

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There'll be less people kind of applying

for those entry level, uh, data roles.

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Uh, and I think it'll just

be a little bit easier.

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My prediction number two is that

companies will start to adopt AI

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more, uh, to do data analytics.

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And that doesn't mean that

there's gonna be less jobs.

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That doesn't mean that AI

is coming for your job.

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It doesn't mean that it's all over.

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Uh, data analytics is here to stay.

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Now will it change down the road?

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Sure.

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I'm sure it will.

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But like what industry hasn't changed

in like a 10 year period, right?

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Like is the automotive

industry today the same?

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It was 10 years ago.

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We're still driving cars, but it looks

completely different Ev self-driving.

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I can't even tell you like

how different it looks.

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Every industry changes in a

decade's time, and that'll be

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true for data analytics as well.

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I mean, 10 years ago we didn't even have

Power bi, so we we even ignoring all

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of the AI stuff, like data analytics is

obvious, obviously changed a lot because

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one of the most fundamental tools, data

analytics, did not exist a decade ago.

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I think companies are.

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Pretty slow to adopt new technology.

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At least like the enterprises, like

we're talking like the Fortune 500.

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Of course there's companies

that are outliers that are gonna

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perform well, uh, using ai.

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Um, but a lot of companies

are slow to adopt technology.

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They're slow to actually

implement technology.

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And I know, 'cause I literally worked for

what, like the seventh biggest company in

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the world at the time when I was there, I

worked for ExxonMobil as a data scientist.

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I can't even tell you how much of their

analysis at ExxonMobil was done in Excel.

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I'll say that again.

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Like a lot of our analysis at

ExxonMobil was done in Excel.

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Python's been around for how many years?

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What?

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1990.

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So 35 years.

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And we weren't even using

a ton at ExxonMobil.

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Uh, is Is Python better than Excel?

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In my opinion, yeah.

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It's great, but it's hard to actually

make progress in these big companies.

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It's hard to adopt new technologies.

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It's hard to roll out new technologies.

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There's all sorts of

different problems and issues.

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Like even getting Python on

your computer at ExxonMobil was

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probably a two week process.

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It probably takes me, if I were to like

get a computer, it maybe takes me 30

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minutes to get Python installed on it.

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Right?

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At ExxonMobil, it was like a two

to three week period, just because

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you had to ask for permission.

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They do all these security checks, you

had to download it, it would break.

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It was so hard to even download Python.

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Uh, and so these larger institutions

like Humana, Wells Fargo, chase Bank.

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Like, I'm sure they're gonna want

to adopt ai, but it, it's going to

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happen over years, if not decades,

where that rollout actually comes out.

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Now, I do think a lot of enterprise

companies are going to make some progress

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on that this year, and I think mainly it's

going to be because of the integrations

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with the companies that are already using.

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So, for example, a lot of enterprises have

a pretty good relationship with Microsoft.

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They're paying for enter

enterprise, Microsoft, uh, plans.

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And I think they're gonna do a

good job with copilot and kind

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of mingling that with chat GPT.

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So I think that will probably

be something that you see these

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enterprises doing over the next year.

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Uh, I think Google's made a lot

of progress with their AI products

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in the last, like quarter alone.

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So those who have a.

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Google Enterprise plans will probably

start to use AI a little bit more,

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but I think there's a lot of stumbling

blocks for enterprises to use AI

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that has been existing in the past.

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I think that'll, uh, become a

little bit less of a barrier this

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year, uh, but still a barrier.

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Nonetheless.

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The way I predict this actually,

like rolling out to companies,

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the way I see it is it'll probably

be at an individual level.

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So a lot of like data scientists

don't even really have a

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corporate AI plan right now.

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Um, but I see a lot of

that changing this year.

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There's a lot of solutions that

have made a lot of progress.

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You guys have seen me do

sponsorships with Julius ai.

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Um, they've made a lot of

progress with their connectors.

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The, the biggest thing is it's

really hard to have secure and

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connect connected data, and so Julius

has made a lot of progress there.

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I think Hex has a, a really good

product that will make some progress.

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Like I said, Chae, Claude and Gemini

from Google have all made a lot of

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progress in the last little bit.

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That makes it easier to connect to

your data and have your data be secure.

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So I think a lot of like individual

data analysts and data scientists

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will start to get access to ai.

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Augmented tools, and I don't think

it's gonna be replacing them.

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Like it's literally just a

tool for them to be using.

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And if you think that AI's going to

replace you, to me it kind of shows

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you haven't really used AI to analyze

data yet because it's not there.

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It's definitely not there yet.

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Um, and like for me the other day,

uh, I, I was analyzing some data

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and I was just using AI to do it.

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And like, you still have to think.

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There's so much thinking,

there's so much planning.

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You have to know what to do.

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You have to have the idea.

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You know, AI can spit out 10

ideas, but like seven of 'em

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are usually really stupid.

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Three of 'em you can't even do.

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So 10 outta 10 ideas like don't even work.

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Uh, so they still need you to, to

be thinking, um, you're going to be

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also more of the bridge between the

analysis and the actual business.

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We were at ExxonMobil, we automated

a lot of stuff that humans were doing

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using Python and machine learning.

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You think, you think that just magically

the people who were doing their job

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lost their job and just got laid off?

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No, that's not what happened.

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It was just a tool to help

them do their job better.

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And a lot of the times they actually

overruled our decisions, our, our

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decisions as in the algorithms decisions.

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Um, this was for buying crude oils,

like deciding what crude oils from

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around the, the world we were gonna buy.

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This was for deciding how much.

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Gasoline we should send to

your local Exxon gas station.

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Like I created a machine learning

algorithm that would basically

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predict that, and I thought it

was pretty good, but a lot of

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the times it was missing context.

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A lot of the times, uh, like

these traders knew best.

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And I think that's still

gonna be true today.

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Like, AI is really smart, but

it's not replacing a human.

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And, and if it is, then why?

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Like, if it is, like why has, has

it like it's just not good enough.

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I have tried AI to make

social media content.

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To do data analytics, to make video

scripts, to make thumbnails, and it's

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helpful, but it's never, ever, ever

gotten it right on the first time.

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So I don't think AI is coming

for your job, but I do think that

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companies will start to use ai.

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I think your job as a data

analyst is gonna change.

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More into that connector from the

actual data analysis to the business,

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I think it's gonna be more important

to know what to do versus how to do it.

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So like for example, like you can

do a pivot table in Excel, you can

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do a pivot table in chat GPT, but

you need to decide when to do a tip

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pivot table when it's appropriate.

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Like when do I wanna aggregate

data based upon categorization?

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And group buys, right?

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That's something that you're still

going to need to do as a data analyst.

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And that leads me into my

third prediction, which is that

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your domain experience is more

important than ever in: 2026

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And what I mean by that is like when

you look at a data analyst, they're

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analyzing data, that's half of

their job, but then the type of data

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is their other half of their job.

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What's the data about?

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Is it healthcare data?

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Is it financial data?

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Is it music data?

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Is it marketing data?

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Is it sales data?

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Like there's always half of the

domain in a data analyst role.

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And I think that's gonna matter

more than ever because once again,

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the how to do your analysis is

becoming less and less important.

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The actual skills, like the actual

analysis skills to, to do your analysis

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are becoming less and less important.

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What's more important is knowing what

to do, when to do it, and what the

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results actually mean and, and how

to translate that to the business.

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So if you've been a teacher before.

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Like, you know how a classroom works.

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You know how a school district works.

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I've never worked in a classroom.

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I might be better at data than you chat.

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GPT might be better at

data analysis than you.

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I don't think it is.

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But let's, let's just for this argument's

sake say that it is, but it definitely

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does not know your personal classroom,

your personal school district, or

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our really, how a classroom or a

school district work in real life.

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Like you've actually been in

the front lines and understand.

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The industry, and that's gonna be

lly important for the rest of: 2026

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And moving forward, you're gonna get

really deep and different, uh, data

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niches or I guess industry niches,

and your knowledge is going to matter.

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And I've, I've told the story before,

but when I worked for ExxonMobil,

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um, at the time I didn't have

my master's in data analytics.

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I had a bachelor's in chemical engineering

and I, there was these competitions,

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they called them hackathons where they

would basically take everyone in the

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company and say, Hey, here's a data set.

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What can you do with it?

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Like, what type of results

can you get for us?

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What type of insights can you pull?

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What type of tools can you make for us

that would be useful for our company?

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And I'd enter these competitions.

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And some people in these competitions

were literally like PhDs in computer

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science, PhDs in mathematics.

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These people were a lot smarter than

me in terms of computers, statistics.

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Machine learning data, like these

people were really, really technically

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and academically smart, but I was

able to actually win one of these

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competitions because no matter how

much smarter they were from like a

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computer algorithm, mathematics sense

than me, I knew the business and I

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knew the domain better than them.

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They had spent all this time studying.

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They didn't know anything about chemistry.

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They didn't know anything

about manufacturing.

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They didn't know anything

about engineering.

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And that's something that was my domain.

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That's what I studied in school.

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I had worked for the

company, like I understood.

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I was like really hands-on with like

refining and manufacturing of gasoline

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and jet fuel and stuff like that.

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And I actually knew what was going on.

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And so when I was analyzing the data,

I was able to analyze faster than

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them because I actually knew, oh, like

this is what sulfur is, this is why

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it's good, or this is where it's bad.

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That would take them a long time

to actually figure that out.

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Uh, and so I was able to work faster.

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I was able to interpret my results faster,

and I was able to actually just come

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up with better insights than they were

despite them being more talented than me.

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And I think for all you career pivoters

who are listening, that's really exciting.

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That's really refreshing

because your pivot actually

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isn't a disadvantage in 2026.

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It's an advantage.

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It's what puts you above the

rest of the people around.

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You like the stuff you studied

in school 20 years ago, the stuff

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you've been working on the last seven

years that you, that you kind of

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hate, you wanna get outta that job.

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That information that you

learned isn't meaningless.

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You can hold onto it and

actually becomes an asset to.

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You

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all this to say, I think 2026 is

gonna be a great year for you.

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I think you have a great

opportunity to pivot in analytics

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to level up in analytics.

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I think people are kind of sleeping on

the analytics right now because let me

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tell you, it is the bread and butter.

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It is proven and there's so many companies

who are still under utilizing how

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much they're doing data and analytics.

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So while everyone's kind of

interested in AI and automation,

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stay true to data analytics.

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And you can use your previous

domain experience to pivot in and

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use ai, but don't be afraid of it.

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Like AI is going to be a tool that you're

going to be using down the road, but

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it's not replacing you anytime soon.

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Data analytics is far from over.

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I think we're just getting started.

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