r/analytics

Feels like we've reached a point where analytics alone aren't enough anymore.

Ten years ago the problem was not having enough data. Today the problem is having dashboards full of data and still not knowing what change to make on Monday morning.

User churn isn't getting better because founders can see the problem. Most of the time they're staring right at it.

The hard part is figuring out what to do next.

If anything, AI has accelerated this. More products are getting built faster than ever before, especially by smaller teams and non-technical founders. Shipping is becoming easier.

AI has made building cheaper and faster. It hasn't made product decisions easier.

If anything, the gap between shipping features and knowing which features to ship next feels bigger than ever.

Looking for guidance from experienced folks who have navigated the transition from traditional ML to the current GenAI/Agentic AI landscape.

I have around 9 years of experience in Data Science. Most of my career has been in traditional ML—classification, regression, recommendation systems, propensity models, etc. Over the last couple of years, I've been involved in a few GenAI initiatives, but mostly at the POC stage.

My current work is largely around calling LLM APIs for tasks like summarization, content generation, and similar use cases. While it's GenAI-related, I don't feel I'm building the kind of production-grade systems that many companies seem to be looking for.

I'm now planning a job switch and have been reviewing a lot of job descriptions. Almost every role seems to mention some combination of:

LLMs

RAG

Agents

MCP

AI System Design

LLMOps / MLOps

LangGraph

Evaluation & Monitoring

To be honest, I'm feeling a bit overwhelmed.

When I started my career, stepwise regression was still a thing. Then the industry moved toward ensembles, gradient boosting, deep learning, and now it feels like the expectation is that every Data Scientist should be able to design and deploy agentic AI systems.

For someone with my background:

What would you focus on first?

MLOps or Agentic AI?

System Design or hands-on frameworks?

Which resources actually helped you (courses, YouTube channels, books, projects)?

If you had to create a 3-6 month roadmap today, what would it look like?

I'm specifically looking for advice from people who were experienced Data Scientists and successfully made this transition, rather than generic beginner roadmaps.

Would appreciate hearing what worked for you and what you would do differently if starting again today.

Background: I’m a service designer facilitating a change management redesign for a healthcare analytics department (mix of Tableau, Databricks, Business Objects). Our current process averages about 11 days from the time an analyst submits a change request to when it’s live in production. Leadership wants that number down significantly.

I’m trying to benchmark against other organizations to understand what’s realistic. A few questions:

• How long does your process take?

• Who promotes to production? Is it a separate ops team, the analyst themselves, or automated via CI/CD pipeline? If ops, how many people are on that team relative to the number of analysts they support?

• Tooling: Are you using ServiceNow, Jira, Azure DevOps, a homegrown tool, or something else to manage the process?

• How much of it is automated vs. manual?

• Do you distinguish between low-risk changes (cosmetic dashboard updates) and high-risk ones (financial reporting, regulatory)?

• How many approvals does a change need before it goes to prod?

Especially curious what other analytics orgs look like — especially in healthcare, finance, or other regulated industries where you can’t just yolo to prod.

Feels like everyone's using ChatGPT/Copilot for something now, but I can't tell whether it's mostly "write me a first draft I'll review" or something people are comfortable using for production reporting after proper validation.

Anyone run into cases where AI-generated code looked completely reasonable but was subtly wrong?

How much manual verification do you guys still do vs. just running with it?

I’m curious about real workflows rather than tools.
Can you remember the last time a business metric
unexpectedly changed and made you investigate?

What was it, how did you notice it, and what did you do next?

Did you get alerted, notice it on a dashboard, see it in a spreadsheet, or hear about it from a customer?

I’m interested in understanding how founders actually discover problems in their day-to-day work.

I've been freelancing as a data analyst for the past 3 years and have worked with 100+ clients on dashboards, SQL, Excel, Power BI, Looker Studio, GA4, and other analytics projects.

Now I'm trying to transition into a full-time remote data analyst role, but LinkedIn applications seem to go nowhere. Very few responses, almost no interviews.

For those of you who landed remote data analyst jobs recently:

• Where did you actually find the job?
• Did referrals make the biggest difference?
• Is LinkedIn enough, or are there better platforms?
• What changed that finally got you interviews?

I'd really appreciate hearing what's working in today's market because it feels very different from freelancing.

Am I wrong to think expectations for roles in analytics are getting ridiculous?

I just looked at a role for analytics engineer.

They are expected to own reporting from end to end, do the API work to ingest the data, model the data and build out reporting via conversations with stakeholders.

I feel like it is easier for an engineer to learn the basics of metrics than for an analyst to build all the skills needed to get these type of roles.

For the other analysts, what are you doing in this new world to keep up with these expectations?

I work for a US bank. I am in the Data Science team and for the last year I haven't built any ML model. Most of my work requires me to analyse unstructured data using LLM, automate using LLM API, and a little bit of simple LangGraph based workflows.

Curious to know the experience of other people working in the industry.

This keeps coming up in my work and I'm curious how others navigate it. A stakeholder requests a specific metric or dashboard, you dig into the data, and you realize either the data quality isn't there to support it reliably, or the metric they want doesn't actually answer the business question they have.

The easy path is to just build what they asked for and move on. But that often leads to decisions being made on shaky ground, and eventually someone traces a bad outcome back to a misleading report.

The harder path is pushing back, explaining data limitations, and trying to reframe the ask. But that takes political capital and can come across as obstructionist if you don't frame it well.

I've been experimenting with showing stakeholders two versions side by side: what they asked for versus a more defensible alternative, with a plain explanation of the tradeoff. It creates a conversation instead of a confrontation.

Curious what approaches others use. Do you document data quality issues formally before delivering something you have reservations about? Do you have a standard way of communicating uncertainty to nontechnical audiences? Would love to hear what has actually worked in practice versus what sounds good in theory.

My_Qualifications: B.Tech Mechanical Engineering (May 2026)

I knew before graduating that I wanted out of mech. It wasn't just the low pay, I genuinely had no interest in manufacturing/core jobs anymore. I also put my master's on hold cause I wanted work ex first. I don't wanna spend another year just learning something and end up with a huge gap on my resume.

At first I thought about support/sys admin/QA kind of IT roles since I'm not into hardcore coding or SDE stuff. Later I switched to analytics cause it looked like it'd have more options. I've learned Excel, learning SQL rn and planning to do Power BI next, but no portfolio or analytics internship yet.

The more I research, the more confused I get. First people say learn tools, then Python, then AI, then AI agents, then domain knowledge. As a fresher, idk how I'm supposed to get domain knowledge without getting my first job.

I'm applying on and off campus but barely getting any calls. So should I keep going with analytics or switch to something else? Should I target IT support/testing roles instead? Or just prepare for CAT/GRE and move on?

I'm not looking for "the market is bad" replies. Ik that already. I just wanna hear from people who were in this phase and actually made it out. What would u focus on if u were starting from scratch today?

Hey everyone,

I wanted to share a recent marketing audit I conducted for a travel agency here in Algeria. The agency was doing high gross numbers—over 187M DZD (around $1.4M USD) in a single season across roughly 100 trips. On paper, they were crushing it. But behind the scenes, they were suffering from what I call "operational blindness"—spending heavily on Meta ads without a clear picture of which segments or seasons were actually driving true profitability.

I extracted their raw data, cleaned it up, and built a dynamic dashboard to isolate the variables (segmenting by quarters, age groups, geography, and family vs. individual targets).

Here are the 3 major insights that completely flipped their marketing strategy:

The Seasonality Flip: "Individuals" (youth) peak sharply during off-season months (January & October) to catch low-cost travel deals. Meanwhile, "Families" strictly travel during official school holiday windows (March, July/August, and December).

The June Black Hole: Family revenue drops to near zero in June. In Algeria, this is high-stakes national exam season (Baccalaureate & BEM), meaning families freeze all non-essential plans. Advertising to families here is a complete waste of budget.

Families = Higher ROI, Less Hassle: Even though the agency ran fewer family trips (46 vs. 54 individual trips), families generated higher total revenue (99M DZD vs. 88M DZD). The average cart value and profit margin per seat are significantly higher because families buy premium, all-inclusive packages.

📊 Full Case Study PDF & Visuals

I’ve put together the entire breakdown, including the data methodology, the exact dashboard visuals (Q1-Q4 filters), and the strategic recommendations into a clean PDF Case Study.

If you want to see exactly how to turn raw agency data into actionable media buying decisions, you can download the full PDF guide send me massage

💬 Let's Discuss:

For those managing service-based clients or agencies: How often do you deep-dive into client CRM data before setting up your ad sets? Are you seeing similar strict seasonality traps in your local markets?

Drop your thoughts or questions below—happy to talk shop and share analytics insights!

TL;DR: Agency was grossing $1.4M but burning cash on generic ad targeting. Audited the data, found that families spend more on fewer trips and that June is a dead month due to school exams. Rewrote their media buying playbook based on seasonal data. PDF guide attached.

the non technical CEO got tired of waiting 3 days for numbers that werent on existing dashboards. tested a few things with our actual data.

meta*base - open source, self hostable, question builder is decent for non technical people. natural language queries work for simple stuff and fall apart on anything with joins. solid free option.

#julius_ai - upload a csv, ask questions, get charts. my CEO could use it without help which is the real test. limitation is it works on uploaded files not live databases so someone has to export data every singllee time.

d_ench - data analysis agent connects to our warehouse directly. CEO texts it from his phone through imessage and gets answers without bugging anyone. 85 to 90% accurate on straightforward questions, flags when its unsure. not a replacement for real data science but solid for daily quick lookups.

chatgpt code interpreter - best for deep one off analysis. actual python execution behind the scenes. not connected to live data though and no persistence between sessions.

CEO now texts _DenCh for quick stuff and comes to me for the hard stuff. his dream is 75% real which is further than i expected.

Hi!

So I am working on cleaning up our customer data quality to arrive at a customer masterdata. I tried to check for duplicates, nulls, invalid email formats and phone numbers, etc. I also tried to review with business some logic, like an inactive customer cannot have an active subscription etc.

However, my problem is when just skimming the data, I still see some weird data quality issues-- like a full name and last name combined (i.e., last name is made redundant and entered in both full name and last name), some company names have zzzz or are named customer, some first names have Mr and Mrs, etc. Is this the part where AI will be useful? Or is there a more deterministic and appropriate approach for this?

What are your thoughts?

Before starting my career, I thought certain skills would dominate my day-to-day work.

However, after gaining some real-world experience, I’ve realized that some skills seem to be emphasized much more than they’re actually used.
For those already working in the field, what skills do you think are overrated?
For example:
Advanced programming?
Knowing every machine learning algorithm?
Advanced mathematics?
Memorizing statistical methods?
Something else?
On the other hand, what skills turned out to be much more important than you expected?

For me, AI tools have made many programming tasks much easier, and I find myself using a relatively small set of statistical methods repeatedly. I’m curious whether others have had similar experiences or completely different ones.

Which platform is more widely used for data analyst roles: Snowflake or Databricks?

If you could learn only one first, which would you choose and why? I'm particularly interested in which one is more commonly used in day-to-day analytics work across companies.

Hello there, I’m about to start college in a month and plan to get my degree in business marketing and administration with a minor in data analytics. My plan is to be somewhere in the business analytic field. The question i have today is really where do I start? What are the key points I should focus on ? I’m starting college with no prior experience with this field!

Anyone using cowork to read dashboards and send briefs?

Hi, I have about 2and half years of experience as a supply chain analyst at a big e commerce firm and I have been thinking of getting into BA side, currently i use excel, sql, python(mostly claude), tableau, AWS and BI on a day to day basis. What would you recommend for me to improve my skillset on? I know it takes a lot of communication and understanding customers and stakeholders, I’m usually more of an introvert but I’ve been working on my communication skills. Any kind of advice would be much appreciated, I’m hoping to move into next job as a BA by January 27’ hopefully.

A bit about me. Over the past 4 years, I've written large internal audits and risk assessments for Fortune 10 companies using R Studio, but they cannot be shared to the public which limits my ability to showcase my data analysis skills on my portfolio. Since they're trapped behind NDAs, I only can share vague overview descriptions. I wanted to build up my portfolio by drafting working papers and articles for the public and government agencies to consume.

Long story short, I have a desire to become comfortable drafting R code and publish an analysis article alongside a parallel working paper that eventually will be submitted to a journal, and then repeat the process for a new project. Kinda like a LinkedIn article, substack article, and then submit the article to other publications like the Financial Times. All while drafting a working paper I have on github, R Studio CRAN, and my public portfolio.

However, I'm not sure if this process is the most industry standard method or a safe approach for repeating future papers. Idk if this article process would cause my working paper to be rejected. I've seen journals mention that the author cannot share the article to other publications (figures and tables) before submitting for peer review for copyright purposes, but they say sharing a working paper for feedback is acceptable if I source my articles within my working paper and final draft submission.

TLDR: I'm curious what process people in the data community go through when they create a custom graph and finish a working paper draft.

1. Do you draft articles for social media and online publications alongside a working paper?

2. Do you generally ignore research journals in your reporting due to length of peer review?

3. Any other tips or advice you may have before I begin converting my notes into an article and working paper?

Hi all! I am starting a Masters of Science - Business Analyst program at a university in Michigan this coming September. It has been quite some time since I’ve been in school, as I graduated my undergrad in 2019. I wanted to do undergrad in computer science, but since I played college hockey, the program director at the time and myself both agreed it would be extremely difficult to get through due to the hockey schedule from August till April during the year.

I’ve been in sales the past 6 years now, and the desire to do a more technical job never went away so here we are and brings me to my question.

Is there any topic I can start researching and diving into over the next couple of months to get a little of familiarity with it before starting classes? I will have to take two pre req classes, 1. Enterprise systems 2. An undergrad stats class.

Thank you!!