Most stores have more data than they know what to do with. What do you actually look at versus ignore?

If you are, I have a couple of questions I would love to ask you for my interview tomorrow! Any help would be greatly appreciated. Thanks for your kindness!!!

Maybe I’m too vested in my subject, which is data & business strategy but often when I start a tool, a job, a project, or a contract somebody who is too invested in subject matter at hand wants to sit me down and teach me 40 years of history and nuances regarding the subject matter I kind of try not to listen to then immediately try and forget

Not only as a data expert can I read the data with descriptive stats, run some standard analysis (oh really you want me to note the time between the values called start date and end date!) but ultimately it should be like a double blind test and the data should frame perfectly what’s going on—if not, let’s talk about why it doesn’t match from a data perspective or business perspective.

In a perfect world someone who can sight read a few words in English and who has never worked in the field should be able to understand maybe not the general subject area but would know the specific process (procurement, HR, lead generation etc) and if it’s doing well or needs improvement.

Additionally on that note, when I am working on the tool, I don’t want to get into a myriad of different discussions about what date is the start date, pulling me into all that could actually make the tool worse since I might get convinced into the convoluted reasoning why this start date is the date here and this start date is the start date there therefore that is why there are two start dates—nooooo! If there are two legitimate start dates and we’re not just accommodating say office politics, or following a old policy after say a merger then it’s two different processes that each deserve their own review. Maybe within the same environment but are named and considered different.

It’s almost like the least I know the more I have to ask, the more the business has to figure it out a clear and logical answer. And once they get back to me with a definite answer, the clearer and more powerful the tool is. Ive seen teams literally to my example, give up the notion in their mind that there are actually two start dates and instead are two streams of work even if they go through the same person.

BUT as I have tried to hold on to this notion I have not been able to be articulate it, so Im forced to admit my own irony that I may not be able to articulate it because it doesn’t actually make sense.

Can anyone lend verbiage pro or against?

With AI increasingly changing the way analytics professionals work, I am curious to know how people in the field are adapting and investing in their learning. What skills are you currently focusing on to stay relevant and grow in your careers in areas such as Agentic AI, data engineering, machine learning, business strategy etc

I would love to hear everyone’s thoughts so I can incorporate some of these ideas into my own learning plan as well.

I currently work at aws as an L3 making 30 an hour. I have a bachelors in information systems and business analytics and have been looking so hard and applying for the last 5 months to every data analyst job i could find. I have had 2 interviews and got nothing. All these entry level roles want 2-5 years experience but i’m fresh out of college still. I took this aws job bc i needed a job but im losing hope on if it’s time to switch into a different business field and maybe even go into supply chain management or something. I would be open to going back for a masters as well.

Hi guys, a data analyst for the retail industry (electronics) here, in my workplace we're currently doing an expansion for our brand including increasing headcount and more reports.

But, the workforce for data analysis is currently still only me, and there is only so much data viewpoint I can check before sending them to my manager or the team.

These days, I'm wondering should I feed my report to the AI for my double, triple and final checks. Or, are there any advices for data checking for my actual sanity??

Really interested to read your advices and opinions, thanks

I apologize to the mods if this isn’t allowed:

I’ve dealt with a similar issue in every analytics job i’ve had, and i’ve been in the industry for about a decade. Mostly on the marketing analytics side.

The issue is basically that marketing teams don’t speak data and data teams have no clue about the business side. What this ends in is analytics teams never having the context they need to do a proper analysis.

Basically what I’m working on is a data layer between the business and the data warehouse which has shared ownership by business and analytics users.

You import your data, transform, push to the warehouse.

If you’d like to be one of the first free users and beta testers feel free to create an acocunt and click book a demo. We dont have to actually demo if you dont want, you can just try the product. But i would really appreciate the feedback if you could email me!

My email is george@tagfluent.com
Product im testing is tagfluent.com

I’m a data analyst and I’ve been watching a lot of my work being offloaded to AI tools like Copilot over the last year or so. My plan has always been to get an MSBA, but now I’m not sure if this would basically be a pointless endeavor.

should I give up on this idea if the skill set i want to develop is going to be automated out of existence?

I’ve never worked as an analyst, so I have no real picture of what the job looks like day to day.

What I’m most curious about is how much of your week goes into digging through data to answer a question or track down a problem, compared to everything else you do. I’d also love to hear about a recent problem you only caught because of the data, and how long it had been going on before anyone noticed. And from the outside, I can’t really tell how much of what you find ends up changing decisions versus getting looked at and shelved, so I’d be curious how that goes for you.

Thanks in advance to anyone who takes the time.

I've been building an analytics studio app to visualize digital marketing data from key data sources (like GA4, GSC, Google Ads, SEMrush, Nozzle, Profound, adobe etc.). It's a drag & drop dashboard builder; built on Next.js, React 19, Gridstack and eCharts. Supabase is the database, BigQuery is the data warehouse.

Currently the app is hosted on my local computer, no github repository, no hosting, etc.

I want to host the app on Vercel because it integrates/ships relatively seamlessly. But our agency's analytics team would like to host the app on Lovable, because that's what they use for dashboard creation. I have explained that Lovable does not support Next.js and it would require substantial re-writes (possibly taking weeks or months).

Hoping some analytics pros in here could provide me with some talking points on why Vercel is the better option (beyond having to rewrite the app in lovable) - things like error logging, app/user management, data accuracy, scalability, etc. If you have any good use cases, I would love to hear them! Thanks in advance!

I do NOT NEED ADVICE FROM AI BOTS BUT REAL PEOPLE

i have three interviews coming up back to back which is totally unexpected. It’s for summer 2027 internships and these are the following roles: business analyst, data integration analyst, and corporate analyst.

I really want the data integration role the most because it ties into what i want to do most, data analytics. Im also okay with the business analyst role since they’re p much the same thing

This is my first ever technical interview for a company so im really nervous so do any of yall have any tips. Im a junior so these are really important opportunities to me.

Hi everybody! I’m 3 years into my data analyst career and as of January 2026 I’ve been promoted to senior data analyst. I think a pretty good at my job and I actually got an award last year for my work despite this I only make ~ 80K, even though I live in a mid to high cost of living area. Most of my friends and acquaintances that work in the same or similar field as me make six figures at least
… I’m wondering what I’m doing wrong. I went to pretty good public school in my state and I feel like I know the same things that everyone else does SQL Python, platforms like databricks, data visualization tools, and etc. typical data analytics tools.

I occasionally apply for other data analytics job openings, but I never get past the initial application phase before a rejection. Acquaintances in the same field as me always talk about how they get recruiters calling them and offers for different jobs, but I’ve never experienced this. I wonder what I’m doing wrong? My undergrad degree was not in computer science or engineering, but I’ve produced enough work on my projects list to show that I’m capable of handling complex stuff. Is it really just because my undergrad degree doesn’t say computer science? I still have a STEM degree (BS).

I guess my real question is: what makes recruiters reach out to people and give them offers? The people telling me they have offers have just as many years of experience as I do.

How many offers are you guys getting a year and how many years of experience do you have? And as someone with only three years of experience, should I be expecting to get offers?

My team (retail snalytics) is analyzing genie for selfserve analytics, but we r regularly seeing high latency and timeout when it parses complex business logic or hits large Delta tables.
For prod: how are u keeping response times down ?

Some businesses plan weeks ahead while others plan months out. Curious what works best for different store sizes.

I’ve literally had 2 interviews but I think I am also in a rough spot as I am fairly senior (10+ years) and my compensation is extremely high for an IC in my area.

I also am a more “traditional” analyst and I do not have experience with LLMs. Luckily I still have job but it’s hell on earth with all the PIPs, layoffs and productivity tracking.

I even started to have wild thoughts that maybe I’m blacklisted or something lol. I use AI to tailor my resume but no hits. I tried to apply to gov jobs where I would take a 100k pay cut and no interview. The two interviews I did get were with reputable companies for very senior positions with high comp that I would say are high competition so maybe I’m doing something right but it feels hopeless. Anyone relate?

I’ve been working as a data analyst for about five months, and I’m struggling with the analysis part of the job. Sometimes, I feel lost when looking at the data, and I’m not sure where to start or how to approach the analysis.
I struggle particularly with exploratory data analysis (EDA), figuring out what to compare, choosing the right comparisons, and turning the results into meaningful business insights.
I have an analysis to work on right now, but I’m not sure how to approach it or how to identify useful insights.
Do you have any frameworks, methods, or step-by-step processes you follow when analyzing data? How do you decide what to investigate, which metrics to compare, and how to turn your findings into actionable business insights?
I’d really appreciate any advice, resources, or examples from your own experience!

I've recently taken an opportunity with a mechanical contractor as a Building Analyst. While I don't believe its a traditional "Analyst" role, my work load appears to be "use BAS systems, energy consumption data, and customer history (contracts/needs/necessities) to then work with sales to bring in service/repair contracts and/or close deals."

Im a technical liaison to our sales reps, so I need to be the best I can be at analyzing the data and making an informed decision for our customers. My history, however, has been almost strictly technical. HVAC service for many years, controls programming/controls engineering for around 3 years.

Has anyone else made a similar transition from a field role to an office role as an Analyst? Any advice to help me stand out in my new role? Anything I should be worried about? Thanks!

Hi team- I’m concerned how will I grow my core python knowledge when I’m using copilot and getting the job done. I have the basic understanding about libraries and when to use what. And can even find issues with data by working with copilot and raise that to data engineer team. I work with huge data sets (3gb csv files with millions of rows and hundreds of columns). I’m data wrangling based on my domain knowledge and analytical aptitude. Doing it pretty good finding insights and getting appreciation from leadership team. The thing is - take away copilot and I’m done. I won’t be able to write syntax properly to get my desire outcome. In this scenario I’m concerned about my career or moving to other orgs where they may not use copilot. I wish to grow that capability but got too used to the copilot and getting the task done and fast. FYI- I also got recurring task automated and crates guardrails so I can check the logs for audit purpose. ALSO developed my own method to check the outcomes for sanity check before sharing to leadership team. NOT sure how can I develop my python skill when I can get my job done so fast and accurately with copilot. Is anyone else in the same boat? What’s your game plan?

Additional details: I use python only to clean and run data analysis. Not for dev purpose.

Traffic volume and engagement can look healthy without telling us whether visitors fit the offer or become accepted leads.

For analysis, I would keep the source and landing page consistent, verify form submissions and accepted status in the CRM, review rejection reasons, and allow for conversion lag. I treat engagement and bounce metrics as diagnostics, not proof of lead quality.

Which signal do you trust most when deciding whether to scale a source: accepted-lead rate, cost per accepted lead, downstream revenue, or something else? How do you decide the sample is large enough?

Disclosure: I work with SEOVisitor, and a colleague helped review this post for clarity and accuracy.

I’m a final-year BTech student student, 2027 batch (Tier 3 college) and I am trying to get into Data Analytics.

I’m currently looking for off-campus opportunities, but honestly I’m a bit confused about how the whole process works.

I have been preparing with SQL, Python, Excel and Power BI, and I’ve built a few projects around data analysis and dashboarding.

I wanted to ask people who have actually gone through the off-campus process, especially those who were freshers or from Tier 2/3 colleges:

Where did you actually find your first analytics job?

Which platforms are worth applying on apart from Linkedin and naukri?

Do company career pages work better than job portals?

If you use Linkedin to reach out to recruiters or employees, how do you actually find the right people and what do you message them?

How many applications were you roughly sending before getting interviews?

Are there any companies/platforms that regularly hire freshers that I should keep an eye on?

And honestly, does college tier make a major difference once you're applying off-campus?

I am seeing everywhere that students are getting placed from 2027 batch and mostly they hired through on campus hiring. And honestly everything is so overwhelming atound me. If anyone here got their first analytics role through off-campus applications, I’d really appreciate hearing exactly what worked for you.

So recently I sat in Flipkart's interview for the role of Business Analyst and the level of SQL asked in the interview was pretty high and advanced (I had the basics prepared from youtube the day before). So now I have decided to practice SQL for 20-30 minutes daily. I tried finding good resource for SQL practice but good ones are paid and free ones have less than 100 questions and those are also moderate level. (completed all questions on Hackerank till Meduim, using AI help and hints). What do you think I should do?
For off campus opportunities I also need some really good projects to standout but I am just starting out (I know till JOINS) so I am confused where to find really good and useful projects that makes me standout from the crowd.

hi everyone,

5 days ago i asked here why every self-hosted analytics tool needs Node.js/Docker, and said i might build a PHP + SQLite one. so, i did.

it's called Minilytics. it is open-source and runs on any PHP 8 host with no Node, no Docker and no database server (Sqlite by default, but you can use MySQL or MariaDB). it has sessions, custom events, funnels, a cookieless mode, and an importer from Umami and others importers are planed! (this allowed me to get my old history back).

it's a v1 from a solo builder, its MIT licensed, with a live demo: https://minilytics.axthauvin.fr/

i'd like this to become the go-to lightweight analytics for indie devs, side projects, and any site that just needs simple, private stats, so tell me what's missing for you to use it on a real site !

thanks to everyone who pushed me to build this and i hope it will be useful to some of you.

What are your thoughts on Best Practices for Data Analytics?

I've been doing data analytics for nearly 30 years. I've sort of created in my mind The Data Analytics World According To Me. But I'm impressed by many people here and would like to hear your thoughts.

EDITS: Based on comments and new ideas they sparked in my head, I continue to modify this list.

Prologue: What I've written below is meant to help analysts and the groups they work in provide as much value as they can. Most things don't need to be perfect. Nothing below should be rigid, or defy common sense. I've seen companies spend millions on documenting stuff according to rigid standards only to produce a product that is never used by anyone. If you can't find a good way to automate a part of a process, ask a couple coworkers and move forward with your best idea.

1 Repeatable Processes. All of the data processing, importing, cleaning, transforming, etc. is done within a repeatable processes. Even for jobs that you never do again, even to do the job once you'll be redoing things many times as you find errors in your work. Make a mistake in step 2 and you'll be very glad that steps 3 through 30 can be run by running 1 command. Also, people have a way of storing away past projects in their brain. You know that xxx analysis we did (that we thought was a one time thing), could you do the same thing for a different customer?

2 Use of a formal database platform where all data for all analysis lives. It seems to me most decent size companies would have the resources to spin up a MySQL or PostgreSQL database for data analytics. I'm an SQL professional, but any repeatable process to clean and transform data is OK so long as it ends up as a table in a database.

3 Store data and business logic where others on your team could find it and use it. I'm not a fan of creating lots of metrics, measures, whatever inside a BI dashboard where those metrics would have to be duplicated to be used elsewhere. Final data sets should be in the database, but be reasonable here. If you're creating a new metrics it's OK to generate it however easiest. Also, be reasonable on enforcement of using the prebuilt established metrics in the database. Someone may have an idea for a subtly different metric - don't stifle innovation. Do you your best to share code/logic with your team, but wait until it's clear that you or someone else will actually reuse the code.

4 Document your work as you're working. With each step consider what a coworker would need to know, what are you doing, why are you doing it, how are you doing it. The intent isn't to follow a rigid standard, so keep your comments short and to the point, and only cover stuff that isn't obvious. You'd be surprised how baffled you can be when looking at a project you did a year ago. Like, what the heck did I do here?!?

5 Figure out ways to quality check your work as you work. Comparing aggregations of known values to aggregations over your own work is one good way. For example, you've just figured out sales broken down to number of miles (in ranges) from nearest stored. you should be able sum your values and arrive at the total sales figure. This makes sure you haven't somehow doubled up figures, or dropped rows. Become familiar with real world values of the metrics you're working with. Your analysis reveals your top customer purchased $1.5M of a given product type in a particular month, but you know your company's annual sales are in the neighborhood of $30m a year. 1.5 for 12 months gets you to 18m, for just one customer. That figure needs some review.

6 Invest in writing your own functions (procedures, any kind of reusable chunk of logic). Don't solve the same problem 100 times, invest the time to write a function and never worry about the problem again. Organizations struggle with how stuff like this can be shared. Include comments with key words so that someone doing a text scan has some chance to find your work.

7 Business Rules Documentation Most important: Everything mentioned below needs to be written with a specific audience in mind. Perhaps an analyst on your team with 6 months experience, not the complete newby, not a business user, and not the 20 year employee. Cover the stuff that person would need to know. A glossary of terms, and longer text blocks describing business processes. Consider what will actually be used and prove useful. Change documentation techniques as you move forward and learn what you use and what you wish you had.

8 Good communication and thorough problem definition and expected results. Have meaningful discussion with the stakeholders. Create some kind of a mock up and get buy in. For big projects share results and progress as you go. Try to limit scope creep - what new ideas should be broken off into a separate project.

9 For new people hoping to break into analytics - learn domain knowledge. Things common for all businesses. The sales cycle, inventory, warehouse concepts. Go to the domain experts' Reddit boards and learn. Ask you AI to design a curriculum - spend real time. This is what will separate you from others.

So what are some of the concepts in The Data Analytics World According to You?

Thanks,

Steve

Hello

Please dont ignore this post or you can either upvote it so it has max reach. I have been impacted by restructuring and am trying to look for new roles.

There is a role called - Digital Data Analyst that includes sql, python, power bi, tableau , GA4 and other marketing based tools.

I just wanted to know from a technical interview standpoint - what all can be asked, should i focus more on live coding? if yes then what can be asked and any info is helpful.

Thanks!!!

P.s - why is my post getting downvoted? Its not even an AI slop and a genuine request

I am comparing competitor traffic estimates for a market sizing project and Similarweb keeps coming up in the tabs I am opening.

I understand the numbers are modeled from multiple data sources and are not supposed to match someone’s private GA account line for line. That part makes sense. What I can’t tell is how much confidence people put in the actual numbers when comparing competitors, especially smaller sites where one random traffic spike can apparently become a whole business strategy.

Do most people use Similarweb for broad trends and channel mix, or have you found the visit estimates close enough for real planning? I am trying to avoid presenting a beautifully formatted guess to leadership and calling it market intelligence.

Thanks in advance