r/analytics

Hello! I am a relatively experieced a analytics engineer and I kind of have an idea of the price range of the architecture i am suggesting, but i want to know your take!

The exercise here is to suggest a business setting and try to come up with thecheapest possible production ready set of tool to run it.

Imagine a traditional wholesale company, in the fashion good industry. 2 warehouses (physical, not data warehouses), around 3000 incoming orders per month, 30000 outgoing. Data sources are mainly ERP, provider offers, ticketing system for client complaints, CRM, some supply chain data like delivery times, wayslips...

So the goal here is to have a star schema with all the data needed to understand the business. Nothing fancy, no ML, no AI. Just a good data warehouse, reporting built on top.

The condition is to centralise all data, have full analytics visibility, and use only Cloud resources (all company systems are in the cloud)

So my question is, with the existing available Data tools (ETL, Visualisation...) and without ever running stuff locally (so a notebook with hardcoded API keys does not count), what is the cheapest you could run the analytics stack on this company (excluding headcount)?

PS: i now see this question could seem like i am looking to buy tooling. i am not and this is purely hypothetical.

I have worked as Senior DA at Meta (Facebook) and was laid off last year. I am moving back to India and would like to give back to community. Along with working at Meta, i have also given interviews at Google, Uber, Microsoft, Snapchat, Revolut etc for different locations across world.

Now, I am thinking to mentor a cohort to help them crack Product or Data Analytics jobs.

Why i am doing this -

1. I like teaching. In college years, i used to teach PCM to school students. Now i would like to teach Data to others.
2. After working for over decade in different companies, i am taking time off and would like to share my experience.
3. Not doing this solely for money but i would be charging a fees to keep myself motivated to teach daily and arrange necessary logistics for class.
4. I have free time so i would like to keep myself occupied. Over last few years, i have made decent corpus and i am exploring other things as well.

If any college students/freshers/professionals would like to get mentored for cracking analytics, feel free to reach out.

How are you actually using MMM outputs to justify brand investment when the model keeps pointing you toward performance?

Working on a paid media strategy where I’m trying to balance Reach and Frequency for long-term brand building against short-term ROI. The MMM keeps optimising toward what converts, which means brand-heavy investment looks terrible on paper.

Curious how others are handling this in practice. Are you presenting MMM outputs alongside something separate, like brand tracking data or a Binet/Field split, to make the case for upper funnel spend? Or are you finding ways to frame the MMM results so the brand investment doesn’t just look like wasted budget to whoever is approving spend?

Not asking whether MMM is the right tool for this job. I know it has limits with adstock and long-term equity. I’m asking how you’re actually navigating the conversation with stakeholders when the model is telling one story and your instincts are telling another.

I have changed my career path and thus I'm no longer doing data analysis in my daily job now, so I'm genuinely curious nowadays, in real work settings, which part of the work do you use AI the most or do you think should be handled by AI?

If I were to speak about it, I feel like data cleaning, data standardization, data profiling, data visualization, SQL writing and these labor-intensive work can all be done by AI. Do we just need to split the work, assign the task and review the results with our judgement?

For AI visibility tracking, what metric do you trust most?

Ngl guys I've been looking for data jobs for over 2 years. Been able to interview with over 20 companies and many HM rounds and final rounds. I really have no idea what's wrong. There was 2 times I almost landed a job tho but the companies have their own reasons to not move forward.

Anyway, just wondering how's your story? Any tips?

I'm super exhausted, especially seeing ppl younger than me graduate every year and gain more experience and moving up in their career ladder and I am here with nothing.

Btw I'm currently trying Wonsulting’s ultimate plan. Would be happy to share the experience if anyone interested in

I hate heavy math. Despise it.

I understand many liberal arts majors are becoming a bit of a waste of time ROI-wise and very difficult to get a job in. So, Ive decided I can push through the statistics-like stuff like for the data science minor.

As for the majors that I'm seeing people usually major in to work in the analytics field:

The data science major at my school is too much heavy math (Calc 1-3, Linear Algebra, etc). Oh, and business major at my school is too many remaining credit hours which would require me to not graduate on time (more money). My school does not have economics major or anything like that.

I Already have internships and projects in the area of data analytics. I am currently building my technical skills (SQL, Excel, Visualization through Tableau, Python later)

As long as I secure a strong portfolio of projects, internships, and network well -- will this major + minor combo allow me into the field of business / data / sports analytics or a similar field.

I'm hearing the major doesn't matter as much as the internships/experience, portfolio, and connections , which is what actually opens the job opportunities.

Help me out!

Hi guys,
I am an MBA 1st year student, I am pursuing my MBA in Business Analytics, Can someone please point me in right direction or best help me land an internship,
I am Completely lost, I tried everything, I have goodanalytics knowledge, but I am unable to get an interview, i don't want to pursue an internship in any dumb company as it will be my first work experiece, PLEASE HELP IN ANY WAY POSSIBLE, I am running late already,

I am moving to the US and was looking into becoming a healthcare data analyst. I have a bachelors in criminology and I am sufficient in excel, power BI, and python. How do I go about applying/becoming a healthcare analyst. Is that all I need for qualifications or is there anything else I should do to apply? I don’t have any background in healthcare

https://preview.redd.it/xh8np2c3zd2h1.png?width=493&format=png&auto=webp&s=5e62826d1c528d529d0e88beb0e24d16ecc6ede3

https://preview.redd.it/rv487c75zd2h1.png?width=472&format=png&auto=webp&s=d1debc8e338e928f8f20761164011fa5ccfde379

Im a graduate of data engineering from the cariben, don't have any data experience besides some basic one in my current job, which i highlited, i have a very polished github but my projects are kind of basic and im working on a very good one as of right now.

I am currently in the process of completing the Data Analysis Google Course on Couresa. I was wondering if there was any feedback anyone who has completed it can give.

I am wanting to get into data analysis and change my career.

Any tips?

Please respond if someone is willing to help.

MBA student here specializing in Business Analytics. Looking for a Data Analytics internship — remote or anywhere in India. Unpaid is fine if the work is genuine and there is something to learn.

Happy to contribute to real projects and give my best effort.

I’m an International grad student deciding between two full-time offers and feeling really grateful but torn.

Offer A is a BA Equivalent role at an MBB firm. It feels like a huge learning opportunity, strong brand name, and potentially a great path into corporate strategy, or data/analytics strategy later. I’m excited by the growth and exposure, but I’m not sure I want to stay in consulting long term, and also about exit paths.

Offer B is a business intelligence/analytics role at a major financial services firm. The work is more familiar to me because I’ve done something very similar before, and I genuinely like the domain. It’s also in a city where my family recently moved, which matters a lot to me personally. Although I like the role, My concern is whether I’d be choosing comfort over growth.

Long term, I’m interested in analytics/data strategy or general corporate strategy. I’m trying to figure out whether it’s better to take the consulting opportunity for broader exposure, or choose the role that is more directly aligned with my prior experience, lifestyle, and location preferences.

For people who have made a similar choice: how did you think about brand, growth, location, and long-term exits and any tips on navigating this decision?

As someone starting out in Data Analytics, this is something I genuinely struggle with.

Most of us in college focus on learning tools like Python,SQL, Excel, or Power BI, but people often say companies care more about business understanding and problem-solving.

The thing is — without real industry exposure, how do students actually build that mindset?

What helped you start thinking beyond just dashboards and technical skills?

Hi, I have almost 2 years of experience in SAP BW

And I want to switch from SAP BW to Data Engineering, I want to put some project into my portfolio/resume and then apply to companies,

I have considered the fact of me searching for Data Engineering projects in my own company but they don't allow this kind of cross platform change.

So I reckon my best move is to change the company

I have a little bit experienced from fabric as my current client are using it and I helped them with data ingestion from BW

I believe I should put that too in the resume.

I am really not sure how to approach this.

It will be really helpful if someone has insights on this

Thank you.

We are migrating our CRM, and have been assigned to take the lead in data quality & reporting. I am feeling a bit overwhelmed and excited. For those who have such experience, what can you advise?

We’ve been diving deep into data workflows lately, and keep coming back to one realization: we spend a massive amount of time getting data into the warehouse, but the real magic happens when we actually push it back out.

Most of us have invested heavily in building clean, reliable data models. But let's be honest: if those insights just sit in a dashboard, they aren't actually changing the way our teams operate.

Why we think it’s a game-changer:

• Teams work in tools, not in data warehouses. Whether it’s sales in Salesforce or marketing in HubSpot, the data needs to land where the work actually happens. This process removes the need for manual CSV exports and repetitive data requests because once the pipeline is established, the data syncs automatically.
• The best part is that you aren't building new infrastructure from scratch; you’re simply putting your existing clean data to work and getting more value out of the models you’ve already perfected. This gives business teams the autonomy to work within their own tools using warehouse-validated data, shifting them away from inconsistent spreadsheets and ensuring everyone relies on a single source of truth.

Curious to hear from those of you who have already implemented Reverse ETL. What was the specific catalyst that made you realize it was necessary, and do you consider it an essential part of your stack now or more of a secondary tool?

I keep circling that question and I'd love some real pushback, because from where I'm sitting it looks like the second thing. But I might be missing something obvious.

Quick context. I'm a solo founder running three projects at once. A native AI Mac app, an AI web platform, and a small marketing agency that helps promote the first two. They don't share much technically. Three Supabase projects, three Stripe accounts, a few single digit TB of data spread across them. But the questions I have about them every week are basically the same. Where did MRR move? Which cohorts converted? Which campaigns drove real usage, not just signups?

My current setup, mostly by accident, is pointing Codex at Supabase and Stripe and asking. It works surprisingly well. The thing I keep noticing is that most of the work isn't the SQL. It's me re-explaining the business every time. Which Stripe product maps to which app. What "active user" means this week. Which subscription states actually count as revenue. The agent is great at SQL. The slow part is teaching it what anything actually means.

The embedded side has the same shape. The agency's product ships reporting to clients, and right now that's Supabase queries with a UI on top. It works, but every new report quietly forks the metric definitions a little. Nothing dramatic. Just enough that revenue on the dashboard and revenue in the weekly export don't quite match if you squint.

So the thing I'd love input on, especially from people running internal and embedded analytics on a few TB of OLTP Postgres:

At this scale, is the right move a proper semantic layer (I'm mostly torn between Cube and dbt Semantic Layer) sitting between the raw data and everything downstream, so internal questions, embedded reports, and the LLM all hit the same metric definitions?

Or is that overkill for this shape, and the more honest answer is a typed metrics module in app code, a small analytical replica (DuckDB, ClickHouse, or just a read replica with the right indexes), and letting the LLM rebuild context per session?

Happy to be told I'm overthinking it. That would honestly be the best outcome.

been thinking about this for months while building my own tool

ga4, plausible, fathom, even posthog. all of them open with pageviews and unique visitors. that's a 2010 metric. for any saas, ecommerce, or content site with revenue, pageviews are at best a leading indicator and at worst pure noise.

the real questions are:
- which channel actually generates paying customers
- what does the path from anonymous visit to revenue look like
- which content pieces correlate with conversion not just traffic

i ended up building zenovay specifically because stripe attribution was missing from every privacy first analytics tool i wanted to use. plausible has goals but no revenue context. fathom is similar. posthog has it but the cookie consent stack adds 80kb to your page.

curious what others here use. is anyone happy with their current revenue attribution setup? what did you stitch together to get a real picture?