---
title: 'Is Data Analyst a Good Career in 2026?'
source: 'https://youtube.com/watch?v=NWsU7lRAGr8'
video_id: 'NWsU7lRAGr8'
date: 2026-07-31
duration_sec: 1402
---

# Is Data Analyst a Good Career in 2026?

> Source: [Is Data Analyst a Good Career in 2026?](https://youtube.com/watch?v=NWsU7lRAGr8)

## Summary

This video addresses the heated debate about the future of data analyst jobs amid AI hype. The creators, both with 10+ years in the data industry, combine official reports from the World Economic Forum, the U.S. Bureau of Labor Statistics, and MIT with interviews of data analysts and hiring managers to offer a balanced, evidence-based outlook. The key takeaway is that while AI is not eliminating the role, it is transforming it, shifting demand from tool-focused 'Data Analyst 1.0' to strategic, business-savvy 'Data Analyst 2.0' professionals.

### Key Points

- **The Core Debate** [00:07] — One side claims AI will eliminate data analyst jobs, the other predicts a surge between 2025 and 2030. The video aims to provide definitive clarity on whether data analyst is a good career in 2026.
- **Methodology** [00:49] — The video is built on three pillars: general market studies from major reports and job portals, ground reality discussions with analysts and hiring managers, and the creators' 10+ years of industry experience.
- **WEF Growth Projection** [01:16] — The World Economic Forum projects that data analyst and scientist jobs will grow 40% between 2025 and 2030, based on a structured methodology.
- **US BLS Data** [01:56] — The Bureau of Labor Statistics predicts 21% growth for operations research analysts and 34% for data scientists between 2024 and 2034, with data scientist tasks overlapping heavily with data analysis.
- **MIT Reality Check on AI** [02:27] — MIT found that 95% of GenAI pilots at companies are failing, serving as a counterpoint to claims that AI can fully automate data work.
- **Computer Vision Automation Limit** [02:58] — MIT estimate that only 23% of wages for computer vision-related tasks are viable for AI automation, illustrating that data analysis is fundamentally different and less automatable.
- **Industry Interviews: AI's Impact** [03:57] — Raguan, a senior analyst at Ford, says AI shifts the analyst's role but does not replace it—LLMs help with things like DAX formulas, but the analyst must verify outputs.
- **AI as a Multiplier** [05:34] — Nate, VP of business strategy at Gear, views AI as a multiplier that lets analysts work 2-4x faster, but insists on human-in-the-loop because of 'bad data in, bad data out.'
- **Hiring Focus: Upskilling** [06:47] — Ronaldo, a global supply chain director at Agewell, says companies are not increasing analyst headcount but are upskilling existing staff, a trend that could shift as AI matures.
- **Mid-Senior Hiring Demand** [07:32] — We use, head of internal audit analytics at Dr. Reddy's, says they are hiring mid-senior analysts who can extract insights from complex datasets, while freshers face a tougher market.
- **What Hiring Managers Want** [09:21] — Managers prioritize problem-solving, communication, curiosity, and a growth mindset over technical skills. 'I much rather hire someone fresher with the right mindset than an experienced person with the wrong attitude.'
- **Data Analyst 1.0 vs 2.0** [11:24] — Tool-only analysts (1.0) have no future because AI can handle routine dashboarding. To thrive, analysts must add business value and become strategic partners (2.0).
- **Six Core Skills for 2.0** [12:39] — The six skills are stakeholder management, communication, domain knowledge, critical thinking, curiosity, and AI generalist skills (ChatGPT, Claude, Gemini, etc.).
- **Showcase Your Skills** [14:49] — Building skills alone isn't enough; you must showcase them through online credibility—e.g., building projects, sharing on LinkedIn, and engaging meaningfully with others.
- **30-Company Networking Technique** [16:25] — Filter 30 dream companies, engage with their employees via comments, connection requests, and messages for 3-6 months, and you'll become familiar—leading to referrals when openings arise.
- **Unified Analytics Trend** [17:38] — The roles of data analyst, data engineer, and data scientist are merging. Microsoft Fabric and Databricks are betting on unified platforms, making cross-skilling essential.
- **Gateway to Data & AI** [18:51] — The data analyst role is a strategic entry point to data engineering and AI engineering jobs; following Gartner and Forrester research helps stay ahead.
- **Salary Considerations** [19:05] — Salaries vary by location, skills, and company; the video shows general ranges for India and the US, noting that salaries have not increased much in the last five years.
- **Final Verdict** [22:30] — Data analyst is a good career in 2026 if you prepare as a 2.0, embrace intense competition, and continuously learn—otherwise, it can be a challenging path.

### Conclusion

The data analyst role is not dying, but it is transforming. A 2.0 analyst who combines technical skills with business acumen, communication, and AI proficiency will find strong opportunities, while tool-only 1.0 analysts will struggle. The video emphasizes practical steps like showcasing skills and networking to stand out in a competitive market.

## Transcript

I'm sure you clicked on this video because there is a big confusion. One side of internet is saying that AI is going to take away all the data analyst jobs. The other side is saying that between 2025 and 2030 data analyst
that between 2025 and 2030 data analyst jobs are going to skyrocket. Well, I can promise that this is the only video you need to watch to understand if data analyst is a good career to pursue in 2026 or [music] not. Watch until the end
2026 or [music] not. Watch until the end to get the absolute clarity. I can say this with confidence because we are not some people who will do some internet research and create a video. Both myself and Haman Vial who is a
co-creator of this video. [music] We have been active in data industry for more than 10 plus years and we have created this video using three factors. First one is general market study from major reports and job portals. Second
understanding the ground reality by talking with data analyst VPs hiring managers who are actively working in the industry right now. Let me first show industry right now. Let me first show you what the reports are saying.
This is a report from World Economic Forum and this chart displays fastest growing and fastest declining jobs between 2025 and 2030 and you can see
that data analyst and scientists which is one category their jobs are going to is one category their jobs are going to grow at the rate of 40% as per World Economic Forum. Now they just don't create this report based on random
research right they have a specific methodology they check lot of facts and they carefully design these kind of reports the second one is from Bureau of Labor Statistics from US government and
here they are saying the growth rate between 2024 and 34 for the job of operation research analyst which is one category of data analyst is going to be 21%. And for data scientist is going to be 34%. Now if you look at what data
scientists do if you read all this description half of it is like data analysis. So all these entities which are creating these reports they combine data scientist and data analyst in kind of one bucket only. Third one is from
of one bucket only. Third one is from MIT where they are saying 95% of JNAI pilots at companies are failing. The point I want to make here is uh when you hear all this hype on internet that AI is going to do a complete automation and
at the same time when you read this report from MIT uh you get the reality check right it's not that AI is so cool that it can automate pretty much everything and by the way MIT again it's a very credible institute here in US
they have this another report where they are saying that 23% of wage wages paid for task involving computer vision. See they are specifically talking about computer vision. Let's say you are doing some kind of crowdsourcing to uh
some kind of crowdsourcing to uh categorize images etc. 23% of those jobs are viable in terms of AI automation. Now when you talk about data analysis job you can understand it is quite different than the job which involves
computer vision. All these reports fairly indicate that there are going to be data analyst jobs in the future and even major job portals such as no.com, LinkedIn etc supports this claim. So based on this I can say that data
analyst jobs are booming. So go take this XYZ boot camp or maybe buy my own boot camp. This is what most of the YouTubers are doing. They are just giving you generic advice and you should not listen to them. In order to make a
decision, we need to go over few more things to understand the ground reality. We spoke with some of the data analyst and their managers who are working in the industry right now. We asked them various questions and found interesting
answers which I'm going to show you now. The first thing we wanted to understand is how AI is impacting this data analyst job. So we spoke with Raguan who is a senior analyst at Ford. >> Yes. uh AI is uh definitely shifting
what analysts do but not uh replacing the data analyst. So here are some ways that I can um tell about why that shift or how that shift is happening. First thing is uh with LLM and AI assistance
you can easily get solutions to your problems. For example, let's say you are working on an Excel formula or PowerBI dashboard where you're writing DAX. So you don't need to memorize or uh keep in mind all the syntax uh details. Earlier
you used Google now you can just give your particular problem and uh the DAX seconds. The only thing that you need to do is you need to verify whether it's right or not. And in another aspect I would say let's say if you're going to
research uh some KPIs or to try to understand a business process using AI you would be able to kind of get the summary and uh do the research in a very summary and uh do the research in a very fast uh uh time. So what AI is enabling
fast uh uh time. So what AI is enabling us is to uh help us uh dedicate more us is to uh help us uh dedicate more time to uh kind of understand and come up with solutions rather than taking too much time on implementing them.
>> The answer provided by Raguan is the common theme we found across many companies and Nate who is the VP of business strategy at geared clarified how his team of data analyst is approaching AI. I don't see it
necessarily as how do we automate existing work, but rather it's seen more as a multiplier uh for the work that we're doing as opposed to an offset in our work. So I would actually clarify that this is allowing our engineers or
our data analysts to kind of work two to 4x faster than they did before in many cases. Um which allows us to kind of move more rapidly and solve more problems. uh but very much in the state of being human in the loop because
of being human in the loop because uh the classic kind of uh statement of bad data in bad data out uh very much holds when it comes to AI and so we are pertains to what pipes we're building from a data perspective uh and to the
accomplish and so there's a lot of training and there's a lot of handholding as it pertains to the level of AI consumption that we have >> in essence AI is not replacing data analyst. It is making them do more. Do
reports say the same thing? Yes. Do experts say the same thing? Yes. And we at Code Basics believe the same. Let's move on to the next important question. We asked this question to Ronaldo who is a global supply chain director at
Agewell, a leading FMCG company. >> We're not expecting on increasing the number of analysts uh in the in the next couple of years. Um I think the focus now is more on upskilling the people we already have uh rather than adding more
headcount. That being said that could change after 2 years as AI matures. We use who is a head of internal audit analytics at Dr. Rady's shared a similar >> Always on the lookout for talented data analysts. Currently we are hiring uh mid
and senior level folks who can draw insight from complex data sets and add two answers we can understand that product companies are not hiring fresher
data analyst. Instead they want their existing workforce to be trained in AI and data analysis. And this resonates with the market sentiment where freshers are not getting hired at product companies. But at the same time the
reality is different when it comes to startup and the consulting companies there freshers are still getting hired for data analytics role. So in essence we can say that hiring for data analyst has not stopped but it is safe to say
>> gear is going to continue to hire more analysts in coming years as our business you know continues to scale we'll need to support you know new areas uh as we go deeper on pricing um and logistics
and uh customer service and various aspects of how to manage e-commerce. Uh so as we continue to scale up, we'll we'll definitely see uh growth in the analytic space uh because at the end of the day uh more of our processes are
going to become more automated. You're going to need data analysts that can interpret those processes and be able to be the ones to kind of feed AI and automation. Uh so so it's being used intentionally, purposefully, and
intentionally, purposefully, and carefully. uh of course uh as we kind of uh as we approach this new era of AI uh and data and analytics >> as a fresher just know this the doors
for product companies are not completely closed but they are little less open than what they used to be and it is more open for startups and services companies. Okay. So if people are hiring data analysts in 2026 what are the skill
sets and competencies that they are looking for? It's not the technical skills that are going to matter more. It's your problem solving ability and the communication skills. >> I like people that are curious, that are
always asking why and what if. Um, I need people that are hungry to learn, that are constantly uh trying to not be comfortable, trying to get out of their comfort zone to to where you actually grow and develop
yourself with an open mindset. So I much rather hire people that are probably maybe fresher on the market but they have the right mindset rather than bring someone with a lot of experience but uh that doesn't have the right attitude.
>> Many candidates are great at crunching numbers but struggle to communicate the insights effectively. Data analysis in my mind is not just about numbers. It's about telling a story that can uh drive business decisions. domain knowledge is
almost as equally as important as uh technical knowledge in many capacities in the world of analytics. Without domain knowledge, there's so much, you know, I'd say waste or inefficiency uh because you often spend a lot of time
trying to absorb knowledge from business people who don't really have the time to teach the analysts about their job or what they're trying to accomplish. So, I I do think that business uh and domain expertise is critically important in
many cases. um as it enables you to be faster, better and be able to speak and tell stories with the data and that what you're actually looking at and if the domain knowledge is lagging the next best thing I kind of tell people is that
natural curiosity is the next best best trait for an individual in analytics. This is something I look for and something that usually can't be taught. But if you don't have the business context or the domain expertise, I
usually go for someone who is uh naturally curious because the curiosity aspect is what that allows them to dive deeper into that domain uh and be able to speak more intelligently about it. One thing that is very clear is
technical skills and tool knowledge will not help you fetch a job. If I had to be definitive, I can say that there is no future for people who are focused only on technical skills and tools. You know, there is a person let's say who is just
focused on creating dashboards in PowerBI and they don't do anything more than that technical work. Those kind of people do not have a good future because that is something that AI tools can already do it either partially or fully.
If you want a bright future as a data analyst, you need to add a business value on top of the technical work. To summarize, people who are focused only on technology and tools are called data analyst 1.0. And there won't be any jobs
for data analyst 1.0. On the other hand, as you already heard from the experts, people who are curious, who are good in communication, domain knowledge, etc. are going to thrive. These are the core skills which will upgrade you from 1.0
to data analyst 2.0. So what are the skills required uh in order to become skills required uh in order to become 2.0? Well, first of all, don't get me wrong, technical skills are important, but on top of the technical skills, you
need to have these core skills to become 2.0. Number one is stakeholder are talking to business manager, engineers and so many different parties and you need to manage their expectation. I think this uh skill is
common across data analyst, AI engineer, software engineer and many other roles. In order to do good stakeholder management, you need second skill which is communication. And by communication, I don't mean that how fast or how well
you speak English. It is about conveying your ideas in a effective manner to other person. Third one is domain knowledge. First you need to understand functions and for that you can check
these LinkedIn post and don't think that you need a job in order to develop domain knowledge. You can uh use some of the resources which we are going to understanding. The next skill is critical thinking. Let me explain this
by giving you a simple example. Let's say I'm working with several analyst and I have given them a data set and they will generate lot of insights. Okay. So let's say five analysts are generating different kind of insights. Even I can
give that data set to Gemini or Chat GPT and Chad GPT can also give those insights. Okay. But there will be one rare data analyst who will come and tell me that we can apply this particular insight in our business to drive the
impact. These kind of analysts will differentiate themselves from rest of the crowd and these are the one who will grow and thrive. And then comes curiosity. The great data analysts they do not stop at obvious answer. They will
question patterns, investigate anomalies, ask why repeatedly and explore data beyond what was requested. Curiosity drives innovation. And the last one is AI generalist. Learn how to use generalpurpose
uh AI tools such as chat GPT claw gemini gro notebook lm and all of these to make yourself more productive. I'm going to attach all the links and all the material in a document which you will find in the video description below.
Using this you can build all these six skills which I mentioned. Now building these skills alone will not guarantee you a job because there is a huge competition especially among freshers. There are two things required in order
to become successful in a career. First one is building a skill. Second one is showcasing those skills. Most of the people focus on the first part. They study one thing after another and they keep on building those skills. But what
is even more important is how you show your skills. One of the important ways to show your skills is to build online credibility. You can practice by working on different projects and then share the work of that project online. Let's say
work of that project online. Let's say on LinkedIn at code basics we conduct free data challenges every month where you get a problem statement, data set and everything. And what you can do is you can build a project and then you can
share it on LinkedIn. Now when you share the project on LinkedIn, you are not showcasing just the technical skills but you are showcasing your verbal communication, your presentation skills and so many other core skills which
hiring managers value. Even when you start commenting on other people's post in a meaningful way, you started building that online credibility in a subtle way. When you reply to someone's comment or post in a meaningful way,
you're building that micro relationship. Let me tell you a simple technique. Filter 30 companies which are your dream companies where you want to apply and companies. Okay, they can be data analysts, managers, HR etc. And then
start engaging with them. You can engage with them in couple of ways. You can comment on those post, you can send them connection requests, you can build that connection and have onetoone message exchange as well. And it will take some
sudden but it will take let's say 3 to 6 months and by that time your profile will become familiar to them and by all this online activity you are creating some impression in their brain. So now
if they have a requirement in their company they may either reach out to you or let's say if you ask them for a referral they will happily give you that referral. Right? See if I find a job and I directly go to a stranger and ask for
they don't know me. But if you have built this relation over a period of 3 to 6 months then they will definitely give you this referral. I personally know several people who have done this and it has worked for them and you can
do the same thing offline as well. You can attend conferences, meetups etc. and then you can build those relationship. One more thing you can do to stay ahead of the competition is following trends closely and I want to mention one
specific trend that trend is unified analytics. In the data world there are three major roles data analyst, data engineer and data scientist. And what we are seeing is these three roles are kind of merging. Why? Because AI is
automating things at all these three levels which is giving these people time to do more. And therefore now you're seeing a person who will be doing a little bit of everything and that is called unified analytics. And I'm not
saying this randomly. You can see big companies like Microsoft betting on unified analytics products such as fabric. Datab bricks is also doing the same thing. They are creating this unified platform one-stop shop for doing
your analysis engineering and AI work. This means two things. Number one, if you are pursuing a career as a data analyst and if you learn data engineering a little bit, then that will give you an age. Number two, once you
become data analyst, new career opportunities will open up where you can pursue data engineering or AI engineering. So in a way, data analyst will become your gateway to enter AI and data field. So I would suggest you
follow the reports from Gartner and Foresters. These are the top research companies and many big companies respect their research and you should know what they got to say. All right. Now, how about salary range? Well, salary depends
on the location, your skill set and the company that you are working for. On the screen, I'm going to show you a general range in India and US. Now, you need to ask this question. Is this career right for you or not? Because even if we
believe that there are many jobs and this data analyst role is going to grow etc. If it is not suiting our natural ability, skills and interest, then there is no point, right? So to decide this you can take this free test that we have
you can take this free test that we have designed and based on the answers that you give in this test it will tell you uh how good a data analyst career is for you. If you are still confused try free resources. Do not buy any paid course.
We have a free playlist on our YouTube channel. It's called sales insights. And using this particular playlist you can build a small project. And when you build that project through that experience, you will understand if
or not. Later on, if you decide to go for a paid course or a boot camp, please do your thorough research. Talk to people who have taken that exact same course or a boot camp because nowadays people use marketing gimmicks. They talk
nicely and once you get trapped into a course or a boot camp, you will not only waste money, but you will waste your time and effort as well. So let's summarize our discussion in 12 points. Okay. So I have those 12 points in front
of me and I'm going to go over them one by one. The first one is all major reports say that data analyst is a good career until 2030. Second one job
portals show significant job openings. Third, several reports say that AI cannot replace data analyst roles easily and experts in industry believe the same. Four, but the market sentiment is people aren't getting jobs easily,
especially freshers are not getting hired in product companies as they are looking to train their existing people first. However, startup and uh service companies show more interest in hiring freshers as they could hire them at
lower salaries. Number five, professionals and hiring managers are not looking for data analyst 1.0 who are just building charts and visuals. Number six, they are looking for data analyst 2.0 who are like their strategic
partners who can manage stakeholders, carry domain knowledge and know all generalpurpose AI tools. Number seven, even with all these skills, the competition is very high. Number eight, you need to stand out with online
credibility and networking otherwise all your efforts will go for a toss. Number nine, the current trend is the role of data analyst and data engineer is merging. So learn unified analytic tools such as fabric and data bricks. Number
10, data analyst role is a gateway to data and AI field because later on you can become data engineer or AI engineer as well. Number 11, the salary for data analyst is decent but it has not increased much in last 5 years. Number
12, it is important to check your self-interest and your natural inclination towards this role by uh going through this free survey and also by practicing a free project on YouTube. Considering all these 12 points, our
conclusion from code basics is that data analyst is a good career to pursue in 2026 provided you understand these three things clearly. Number one, you need to prepare for data analyst 2.0 0 role not 1.0. Number two, you need to embrace the
heavy competition and do extraordinary things to stand out. Number three, you need to stay ahead of the trends and keep learning and optimizing your career path consistently. One more thing, we have not released the data analyst road
map video for 2026 yet, but whenever we do that, we will add it in cards and also add a link in a video description. All right, I hope this video helped you clarify this confusion. If you have any questions, please post in the comment
box below. Thank you very much for watching.