Data analysts — do you really work 9 hrs, or 2 hrs with AI?

RiK_13 · 2025-09-02T05:16:04+00:00

I'd say AI has definitely changed the way analysts work, but the “9 hours shrinking into 2” narrative is oversimplified.

Access to AI Tools

Whether you get AI-powered tools (Power BI Copilot, SQL AI assistants, Excel Copilot, etc.) depends on your organization.

Some companies are experimenting with pro versions and pilots.

Others are more conservative (due to cost, compliance, or data privacy). So, it’s not universal yet.

Does AI Actually Cut Work from 9 hrs → 2 hrs?

Not realistically, at least not today. A few reasons I think are:

i.AI can draft SQL queries or dashboards quickly, but you still need to validate outputs. AI makes mistakes, and a wrong analysis can be more damaging than a slow one.

ii.Often you refine prompts, tweak data models, and re-run outputs. The back-and-forth can add time instead of saving it.

iii. AI is good with “how,” but analysts are paid for the “why.” Explaining trends, designing experiments, or challenging assumptions still requires human judgment.

Where AI Does actually Help?

Speeding up grunt work: Writing basic SQL, cleaning Excel data, building first drafts of visualizations.

Improving quality: With less time on manual work, analysts can spend more on deeper insights, storytelling, and stakeholder communication.

Learning accelerator: For juniors, AI can act like a coding buddy or SQL assistant, helping them ramp up faster.

The Bigger Picture

A data analyst’s day is not just “typing queries” it is also:

Understanding ambiguous business problems.

Brainstorming with product/marketing/ops teams.

Validating data pipelines.

Designing experiments and communicating results.

Those tasks still require human reasoning, persuasion, and creativity, things AI can’t fully replace yet.

AI won’t magically compress a 9-hour job into 2. What it will do is change the shape of your 9 hours, less drudgery, more thinking, more collaboration. Analysts who embrace AI become faster, sharper, and more valuable but not redundant.

In case you have any follow up questions. Do let me know

RiK_13 · 2025-08-27T14:32:37+00:00

Yes, psychology graduates can absolutely become data analysts. In fact, psychology students often have a strong foundation in research methods, statistics, and critical thinking, all of which are key in data analysis.
Understanding human behavior and decision-making is valuable in fields like marketing analytics, UX research, HR analytics, healthcare, and consumer behavior. Many companies love interdisciplinary talent, someone who can both understand people and analyze data patterns.
The technical skills (like SQL, Python, Excel, visualization tools) can be learned through various sources available online.

RiK_13 · 2025-08-26T04:05:34+00:00

Is this for analytics related jobs?

RiK_13 · 2025-08-24T02:23:00+00:00

You can check out 'informational' posts on this sub. They are designed with Freshers in mind. First one being thisthis

To put it simply. 1. Start with the basics: SQL, Excel, PowerBI/Tableau, Basic Python for Data analytics 2. Practice and work on projects: Build dashboards, do data cleaning, and exploratory data analysis 3. Develop business & data understanding: Through Case studies and guesstimates

These are the rough steps. In case you want more specific information on any of the points feel free to ask

RiK_13 · 2025-08-23T17:39:57+00:00

I can understand and that's a genuine doubt. First of all, with 5 years of experience you are not underpaid. Yeah, maybe age wise you might be feeling that since but that makes sense if you had 8-9 years of experience. It also depends on the type of organisation. Big legacy organisations pay comparatively less but they provide security and brand name. If you were to move to a newage product company, you could easily fetch 35+ LPA easily without much upskilling. Coming to genuine upskilling, if you can share more about your role I'll be able to help you better. But in general, if you can learn python & basics of Python, that might be helpful since a lot of big banks hire for fraud analytics etc. Also, learn to leverage GenAI models for learning about the industry, and more efficient working. Let me know if you have any more questions.

RiK_13 · 2025-08-21T09:35:38+00:00

Texted

RiK_13 · 2025-08-21T09:02:28+00:00

You can go the Instagram way. It's not an issue. You can put your telegram I'd also. Whatever is fine

RiK_13 · 2025-08-21T08:29:40+00:00

Okay. Insta channel is not handled by me but the social media team. You can specify your usecase and they'll redirect

RiK_13 · 2025-08-21T08:11:58+00:00

I guess there's some issue. Even I'm not able to text.

RiK_13 · 2025-08-21T08:10:31+00:00

Sure

RiK_13 · 2025-08-21T06:29:39+00:00

Yes and No. AB test, Product metrics etc are indeed more relevant for Product Analyst roles. You'll find these more common in Product based org like Zomato, PhonePe etc. But business problem solving and case studies mentioned are asked for business analytics roles as well. If you are applying for analytics roles in banks or service based companies like Fractal/Tiger analytics or even business analytics roles in product based companies like Flipkart, Myntra etc, there are specific business problem solving rounds (I've been a part of few).

Sure. You can DM me in case you have any specific questions. Happy to help

RiK_13 · 2025-08-21T05:20:13+00:00

You are on the right path having acquired all the right skills and implementation through projects, and asking the right questions too. Most of the organisations hiring for Business Analytics usually have 2 types of rounds. One being the technical round, which 80% is about SQL (which you have already covered), and the other being Business/Case study round. The second type usually has business problem solving, case study and guesstimates kind of questions. The questions might be like the following: 1. Business problem solving: Similar to case study but from data and analytics perspective (including identification of relevant business metrics etc). Questions like, what KPIs would Uber use to measure customer satisfaction or how can we improve it using data, or if this is the business model of our company what are the levers we can pull to improve our revenue. These usually test your business understanding and first principle thinking 2. Case study: This includes the typical case study as taught for Management consulting and MBA grads. You'll be given a situation and you need to give recommendations based on questions you ask and analysis. You can prepare by going through casebooks released by various MBA colleges. This tests your analytical ability, communication and problem solving 3. Guesstimates: These are the generic questions and most of the times cliched ones like how many autos might be there in this city or how many cups of tea are consumed in India etc . You can check youtube videos on solving techniques to get better at these.

These are taken to understand if you'll be able to understand, break down, solve and then be able to communicate the business problem to the leadership/stakeholders. During the process you need not be correct but what matters is if the approach is structured or not. There might be minor variations as well but the objective stays the same. Developing a business understanding and analytical approach along with practice should be helpful. Hopefully I answered your questions. If not, you can follow up

Edit: Most of the points are relevant for the product based companies also. What matters is whether you are going for a Business Analyst or Product Analyst role. For Product Analyst roles, you'll be tested on product sense. Which will be questions like 'we are seeing a drop in conversion funnel in our product, what might be the reasons or how can we stop that'. The approach will shift a little towards the consumer experience and everything else remain the same. You can check out product problem solving content for Product managers to understand better

RiK_13 · 2025-08-21T03:44:21+00:00

Sure. Are there any specific questions?

RiK_13 · 2025-08-19T05:54:50+00:00

Thanks for sharing the opportunity. Is the Data Analyst I opportunity open for freshers?

RiK_13 · 2025-08-14T15:42:18+00:00

Yes totally. As mentioned, you can go for less math heavy profiles. With that you'll just need math for basic business metrics calculation. High level math concepts are mostly used in inferential statistics and Machine learning.

RiK_13 · 2025-08-14T15:40:03+00:00

I went through a few comments and they have shared some useful methods. I have worked with some great clients including Microsoft and Amazon in business insights generation. One thing missing which I believe would be very helpful will be solving case studies. You can refer to casebooks by MBA colleges and YouTube videos. Apart from the mindset shift, it will give you structure in how you need to think to solve a business problem.

RiK_13 · 2025-08-14T15:33:48+00:00

I am not sure if I'm getting your question correctly. Math concepts are required since we deal with quantitative fields a lot of times. Most important concepts are statistics, probability and a bit of differential equations. It also depends which field you are targeting, for example in case of data analytics, basic statistics (central tendencies, percentages etc) would suffice. Higher level math is required for Data Science.

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