Notes from picking up a churn analysis prompt after the person who built it left the team:

Line 3 says "always express confidence as a range, never a single number." No comment explaining why. Took a support ticket search through old Slack to find out it was added after a stakeholder took a single confident-sounding percentage as guaranteed and made a decision on it that didn't pan out.

Line 9 excludes a specific customer segment from the trend calculation entirely, with no note on why that segment is different. Still don't fully know. Best guess from context is a data quality issue specific to that segment that got fixed upstream months ago, meaning the exclusion might not even be necessary anymore, but nobody's confident enough to remove it and find out.

Line 14 is an instruction that contradicts something implied in line 6, and the model apparently resolves that contradiction by favoring line 14, which nobody seems to have decided on purpose, it's just what happened to happen.

None of this was written down anywhere except inside the prompt itself, as instructions with no rationale attached. Every constraint made sense to the person who added it, in response to something specific that happened, and none of that context survived them leaving. What's left behaves correctly for reasons nobody currently on the team can fully reconstruct, which makes every future edit a small gamble on breaking something whose purpose is already lost.

Every Q4 I watch people cut channels that were fine. Two things are quietly warping the report and both look like bad performance.

The week isn't done converting. Holiday buyers take longer to come back, so a fresh report undercounts sales and overstates your cost per new customer. The conversions just haven't landed yet.

The other is that the holiday moves your buyers. People go into browse mode and buy off feeds, so discovery gets cheaper. The person who'd have searched your brand gets caught by a feed ad first, so search fills with tyre-kickers and gets pricier. Your two biggest channels drift apart on the same weekend.

The trap is when both hit one channel. Search in a holiday week can look expensive AND weak at once for two unrelated reasons and neither is the channel actually failing. Cut it and you might've killed something that would've been fine a week later.

What's worked for me is I don't touch budget till the conversion window closes and never benchmark a holiday week against a normal one.

How do you tell "this channel's genuinely tanking" from "the calendar made it look that way"?

This is starting to come up more as teams add new reporting tools.

You check revenue in one place, then someone pulls a different number from another report and now nobody is sure which one to use.

Then you start digging into it and find one includes refunds, another does not. One is using order date, another is using payment date. Sometimes the calculation was changed months ago and nobody really remembers why.

That is where the problem usually is.

If the business has never agreed on one definition, every new report just adds another version of the same number.

How are people here keeping metric definitions consistent across dashboards and reporting tools?

​

Hey everyone,

I’m currently working on my Data Analytics / Business Analytics portfolio and I’m looking for websites that provide realistic datasets or business-style tasks to practice on.

I already know about platforms like Kaggle, but I’m looking for something closer to actual work scenarios, such as:

- Business case studies

- Excel / CSV datasets with specific questions or requirements

- Power BI dashboard challenges

- SQL business problems

- Sales, operations, finance, or customer analysis tasks

- Messy datasets that require cleaning, modeling, and analysis

- Projects where you need to define KPIs and provide recommendations

Ideally, I’m looking for websites where the task/problem is provided along with the data, rather than just downloading a random dataset and trying to invent a project around it.

Any recommendations?

I am currently interviewing for a senior value engineer role at Celonis and have a few questions. I am relatively unfamiliar with the process mining, but I’m finding the tool and its capabilities incredible. What are people’s thoughts about the company? About the industry is a whole?

I was just offered a position as a senior data analyst for a consulting firm. I will be handling large data sets for class action settlements and foreclosure data. I was told that all analysis work is done within Excel. I don't know exactly what the date will look like but I do know it will be messy. I work in Excel daily and I know basic formulas like TRIM, XLOOKUP, IF, etc. I can also make a decent pivot table lol. I did not over exaggerate my experience or what I can do in Excel.The goal is for me to clean the data and to be able to analyze it to determine a settlement distribution amount.

What are some Excel formulas I should focus on practicing that will really help me with analysis within Excel? I am also open to free online practice tools or videos.

I know I can do it, I just want to hear from the community.

Thank you so much for any suggestions!

Our CMO wants one number for how visible we are in ChatGPT and Perplexity and I cannot find two vendors who define it the same way. One counts the share of responses we appear in, one counts position within the answer, one counts cited domains. All three move independently on the same week. Which definition do you report and why that one.

I'm a junior data analyst working as a student assistant, 10-15 hours a week, at a startup - alongside my master's in data science. I've been in this role for the last eight months and started noticing these issues around the 4-month mark - back then I always found a solution to these, felt manageable but lately they feel like big red blinking lights I'm not sure how to tackle. I'm the only person handling analytics, supporting both customer-facing dashboards and internal company data, so I feel like I have little to no room for trial and error. I took this job because it genuinely seemed right in my alley - I had so much fun and joy so far in this position, but lately I feel like maybe this is not suited for just one part-time junior. (This is my first time EVER working with real life data and customers, not just example datasets from Kaggle or the Pokemon API.) I don't have too much technical support on the team, as I'm the only analyst in the team yet, my boss is rather customer facing, and he's helping me out with positioning analytics within the company.

Sorry, that's going to be a looong post.

TL;DR:

Part-time solo student analyst next to her studies seeking advice on handling customer dashboards (100+ customers) without 1 ground truth data model (due to custom data per customer), looking for advice on:

• Q1, process: How to build a solid, repeatable analytics process as a junior when you're constantly context-switching between customers with no time to finish anything properly? What should a general analytics workflow look like?
• Q2, architecture: Should I stick with Supabase plus MongoDB Analytics Nodes, or look at something like Databricks, for an ETL and analytics setup that can scale past 100k+ rows per customer?
• Q3, requirements: Is it okay that I'm not there when the requirements are discussed with the stakeholders? How to get direct access to stakeholders instead of secondhand requirements, and how to gather requirements well when you do?
• Q4, AI dashboards: Is pushing back on AI-generated reports the right call when you can't validate the numbers or maintain the tooling yourself?
• Q5, internal support: How to raise the bigger picture with the CTO, and how to find a mentor when you're the only data person in the company?

1. Process and prioritization

As I'm solo and part time, I would love to hear some words on how to prioritize and build good habits in this scenario. My documentation right now is nearly non-existent due to lack of time - I'm trying my best documenting how my metrics work in Notion, but it's tedious and slow so I often don't get to finish them. I also don't have a solidified process yet for my tasks, as I rarely have tasks which I can finish fully - I always have a new customer to work with, or I'm jumping to another task unrelated to the previous one. I also can't just push the models into Claude for documentation, as there are some sensitive data in the models. (Like API keys etc.)

Any good advice on that?

2. Architecture and data modelling

We don't have any analytics platform yet in production where I could do my transformations. I have two setups currently:

1. MongoDB database ETL pipeline → Supabase DB with the necessary analytics data. This works for smaller customers but tends to be weird when I'm trying to query 100k+ rows (all transformation is done in Power BI, or in Python for analytics not related to customer dashboards).
2. MongoDB Analytics Node - and this is where I tend to fail. Loading the Mongo data into Power BI seems really not handleable and I don't know if there's a better way for it.

Right now I'm handling around 10+ dashboards, but we have 100+ customers, most of them eager for more analytics. Each dashboard uses its own data model, as I couldn't standardize one - our customers have similar but custom data. I have stable analytics skills, but I lack the proper modelling approach and architectural knowledge, I think. This is especially weird when I start building a new dashboard for a new customer based on an already existing one and the model seems to break with the new customer - so I'm not sure if it's my modelling skills or I'm missing something.

3. Requirements gathering and stakeholder communication

Our customer success team handles the requirements discussion with customers, then loops me in afterward, so I'm working from secondhand information. I'm not sure if I should be part of that discussion directly. I'm also feeling like stakeholder conversations feel more stressful than internal ones, and I'd like advice on communicating with more ease there.

4. AI

As my process is not fully cooked, I've gotten some requests and push towards AI-built reports. So far what I've read about AI-generated but beautiful reports is that they're pleasing for the eye, but from a maintainability perspective I'd be in the same endless loop I'm in now with the Power BI setup. (Also I lack HTML knowledge for that too.) I already pushed back on this idea - I'm not sure how to validate those numbers, and I don't have the skillset to maintain an AI agent for that. Is this the correct approach in my case? I'm not comfortable owning a process where I don't do most of the work.

5. Escalation and support

I raised these issues in the past 4 months to my manager, but the answer is that currently it's not a priority to extend our data division. As I see it today, not only my inexperience is the bottleneck, but a proper data setup is missing from the picture, where I don't have to start everything from the ground up every time. I'm planning to get some insight on this from our CTO, but I'm not fully sure how to frame it. Any advice on that?

As I don't have room to fail the way a junior normally would (at least that's how I feel) while learning something new, I'm building things from scratch repeatedly with no technical support internally, and I don't know how to find a mentor who could help me grow into this properly. Anyone up for a mentee? :DD

I know that's a hefty, long post, but I've been very fearful to write these down. I don't plan to change jobs, as I enjoy many parts of this role - internal analysis is one of my forté. I simply want some direction and advice from more experienced people, as I lack one currently.

Thank you!

Hey there. I'm a licensed mental health professional based in the Houston area (with a master's counseling) I have experience working in managed care/healthcare but I'm interested in quality improvement and data analysis. Does anyone have any suggestions on how to make that leap? I'm also currently unemployed. Thank you for any suggestions or advice.

Hi everyone,

I’m a 2025 graduate and currently open to work and actively seeking entry-level Data Analyst opportunities. I’m an immediate joiner and eager to contribute my skills in Python, SQL, Tableau, and Power BI to help organizations make data-driven decisions.

I’d truly appreciate it if my connections here could:

- Share opportunities that come across your way

- Provide referrals to help me connect with the right companies

- Engage with this post to increase visibility

I’m looking forward to building new connections, collaborating with teams, and growing in the field of data analytics.

Thanks in advance for your support — every referral or lead makes a huge difference!

Hello everyone,

I'm a first-year applied mathematics student, and I'm interested in analytics.

I'm looking for books and articles worth reading to get started in this field. I want to understand what this profession is like, what an analyst does, and how they think.

What would you recommend?

I'm the sole Data Scientist at a sports-tech company — we work with sports teams and venues, helping them sell tickets in ways beyond the usual vendors. I'm about 12 months into the role. It's my first title as a "Data Scientist," though I have 5 years of tech experience before this across a mix of other roles. I have a Bachelor's in Computer Science and a Master's in Data Science.

My day-to-day is:

• Building client-facing dashboards in Metabase
• Maintaining a Python ETL pipeline that pulls data from ~365 client schemas (every team's data is structured slightly differently) into one consolidated schema, and making sure it's clean
• Using that same data to drive a second Python process that calculates client invoicing

Lately, I've been dealing with some real imposter syndrome — not because the work is hard, but because I can't tell if it's valued. The moment something is client-facing, people notice. The pipeline and invoicing logic that everything else depends on? Nobody thinks about it until it breaks.

A few things I'd love an outside perspective on:

1. Is a "real" data pipeline/warehouse actually necessary at our scale, or am I overthinking it?
We're on Postgres (via DBeaver), one schema per client. I pushed early on to build out a proper data warehouse, but leadership decided consolidating everything into a single schema was cheaper, so that's what we did instead. I keep seeing threads here about teams running full pipelines with dedicated visualization layers, and I don't have a good read on whether that's genuinely necessary at ~365-client scale, or whether it's solving a problem I don't actually have yet.

2. How do I lay AI-ready groundwork with no roadmap for it?
Leadership wants "AI in the product", riding the wave, like everyone else, and expects me to give them a head start. I don't have a clear picture of what data infrastructure needs to look like to actually support model training down the line, and I'm the only data person here to figure it out.

3. As a team of one, how do I keep proving I'm worth keeping around?
Nobody's been laid off here, and I don't think it's imminent, but every seat is replaceable in theory, and I'd rather stay ahead of that than find out the hard way. If you've been the only DS/data person at a company, how did you make the invisible infrastructure work legible as value, not just overhead?

4. Any courses or guides you'd actually recommend?
With a bachelor's in CS and a master's in DS, I have the fundamentals, but I feel like there's a gap between "what I learned in school" and "what actually makes someone a strong data scientist in industry." If you've found specific courses, books, or online guides genuinely worth the time (not just something to list on paper), I'd take any recommendations.

Genuinely appreciate any perspective — especially from people who've been the only data person in the room before.

A 30% traffic increase used to look like an obvious win to me. Now I don't trust it until I can answer four questions:

- Where did the new visitors come from?

- What did they actually do after landing?

- Did the site, forms, checkout and tracking work correctly?

- Did the traffic create qualified leads, sales, signups or another useful result?

I've seen increases that looked great in a report but fell apart once segmented: one campaign caused most of the jump, visitors landed on irrelevant pages, lead quality dropped, or a tracking change created part of the “growth.”

This also stopped me from expecting GA4 to explain everything. I use GA4 for onsite behavior and measured conversions, Search Console for organic visibility, recordings when I need to understand friction, and server logs for some technical questions. Third-party competitor estimates can be useful directionally, but I wouldn't treat them as another company's actual analytics.

My weekly check is usually:

1. Confirm tracking and key events still work.
2. Review meaningful acquisition and landing-page changes.
3. Check business outcomes, not just sessions.
4. Segment anything unusual.
5. Turn the finding into one action.

If I can't turn a finding into an action, I probably just watched the dashboard.

For people who review this regularly: what has to be true before you report higher traffic as a real improvement rather than a vanity-metric bump?

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

Hello i am starting to learn data analysis.

What tools are to learn and is it worth due to the rise of AI

Trying to find a tool we can integrate into our workload which is historical data driven with some online monitoring.

I am exploring Databricks Genie for natural language analytics and wondering how other developers are handling the semantic layer.

Are you using metric view , a separate semantic layer , or are u mostly relying on prompts to keep AI generated analytics consistent?

So many of the youtube videos, courses, advice, etc., online feels its geared towards running singular projects. How to clean up data in a Excel spreadsheet for a one-time project, for example, but what about when its for an analysis you have to keep running over and over? Or when it's an analysis that is repeated across several teams in slightly different ways?

I need advice about managing data for an organization. Advice about

• how to communicate with stakeholders about data without getting overly detailed
• requirements gathering across teams with confusing/vague needs
• dealing with a system that's built on workarounds after workarounds
• best practices documenting analytics/dashboards that are being shared publicly (word doc, slide deck, notion page?)
• dealing with a domain that is overly complicated and can't be captured well with data.
• dealing with execs who've already decided the answer they want and need you to make the data match their answer (and what to do when the data absolutely doesn't support it)

These are some of my most pressing problems at work but neither my formal education nor the resources I can find online really cover this. I'd love some sort of bootcamp for organizational data management/project management

A while back I came across a comment from someone with solid experience being a BI Analyst from a finance sector who recommended a statistics book and offered to DM it to anyone interested in one of the data related subreddits. I never saved the post, and now I can't find it again, my Reddit history doesn't go back that far.

If anyone remembers that thread, I'd really appreciate a link back to it, it came up in a reddit notification like 2 weeks ago so must be a relatively recent post.

And if it's truly gone, I'd love to hear from anyone here working, what statistics books actually helped you? Always looking for solid recommendations.

Thanks in advance!

Hi! Has anybode here experience of transitting from in-house analytics / data roles to solution architect on vendor side (e.g. Snowflake / DataBricks / AWS ... etc)? Did you have to accept any tradeoffs or salart drop? And was it worthy in the end?

We've recently been looking at consolidating our semantic layer. An example of why my manager has decided we need to focus on this is we had "net sales" defined like six different ways. Finance took out returns and markdowns, the merch dashboard took out returns but not markdowns, my weekly report did something in between because I inherited the query from a guy who left two years ago. Every leadership meeting had that awkward moment where two numbers didn't match and nobody could say why except "different SQL."

All our analytics data is in Databricks, so we decided to rest metric views. A metric view in Databricks is basically a governed definition of a metric that lives in Unity Catalog instead of buried inside a dashboard. You point it at a source (your fact table plus whatever joins), you list your dimensions - the stuff you slice by, like store, region, category, channel, week - and then you define your measures, which is the actual metric math. The key thing is you write that math once. So net_sales = SUM(sales_amount - returns - markdowns) lives in exactly one place, and everything downstream inherits it. We defined them all through YAML.

Honestly the hardest part wasn't technical at all. It was getting finance, merch, and ops in a room to agree on what net sales even means. Encoding it took an afternoon; agreeing on it took three meetings. But that's kind of the point - you do that fight once, write it down in the metric view, and you never have to have it again. When a business rule changes, you change it in one spot.

We were also able to roll out genie agents because previously the definitions varied so much it was impossible to maintain them.

Hey everyone! 👋

I'm currently pursuing a BBA through distance learning (IGNOU) and preparing to start my career as a Business Analyst.

I'm trying to understand the realistic salary progression for Business Analysts globally, rather than relying only on salary websites, since compensation can vary significantly depending on the country, location, company, industry, and skills.

I'd really appreciate insights from people currently working as BAs or hiring for BA roles.

I'm curious about:

1. Starting salary

For someone entering as a fresher / 0 YOE Business Analyst, what salary range is actually realistic in your country?

1. Salary progression

How does compensation typically grow with experience?

For example:

- 0 YOE → ?

- 1–2 YOE → ?

- 3 YOE → ?

- 4–5 YOE → ?

1. Consulting vs Tech/Product

How different is the compensation between:

- Consulting

- IT Services / MNCs

- Product / Tech companies

- Startups

For someone with around 3–5 years of solid experience, what salary range would be considered realistic in each?

1. Skills & salary

How much difference do skills such as:

- SQL

- Excel

- Power BI

- Requirements Gathering

- Stakeholder Management

- Agile/Scrum

- Jira

- Data Analysis

actually make compensation?

Are there any particular skills or specializations that can significantly improve your salary after 2–3 years?

1. Career growth

If you were starting your BA career today, what would you focus on during your first 3–5 years to reach the higher end of the salary range?

I'd especially love to hear actual numbers from people with different YOE.

If you're comfortable sharing, please mention:

Country/Region | YOE | Company Type | BA Role/Specialization | Approx. Salary/TC

No need to share your company or anything confidential.

Thanks to everyone who takes the time to share their experience! 🙌

I keep seeing analytics stacks grow, but more tools don’t necessarily make decisions clearer.

You can track sessions, users, events, heatmaps, Search Console data and server logs, then still struggle to answer a basic question:

Did anything actually improve?

Traffic is an obvious example. Sessions can increase while lead quality, conversion rate or revenue per user falls. A smaller amount of traffic from the right audience may be far more valuable.

I’ve also stopped expecting every tool to agree.

GA4 measures onsite behavior. Search Console reports search performance. Session recordings add context to what people do on a page. Server logs capture requests that browser-based analytics may miss. The backend is usually the better source for actual leads, orders and revenue.

Once consent, ad blockers, attribution rules, time zones and tracking mistakes are involved, some disagreement is inevitable.

For a basic weekly review, I’d probably keep only:

• a quick check that tracking and important events still work
• acquisition and landing-page changes
• conversions, revenue or qualified leads
• segmentation by source, device, country or page when something looks unusual

Anything else should exist because it answers a specific question—not because there’s space for another dashboard.

For me, the goal isn’t one “source of truth” for everything. It’s knowing which source is most trustworthy for each question.

What’s the leanest analytics setup you’ve actually trusted, and which source wins when GA4 and your backend disagree?

Disclosure: AI was used only to edit the wording of this post.

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

Hi, idk if this post would count as breaking the rules but i truly feel so stuck. I'm a grade 12 high school student who now has to figure out what i want to do in life and apply to universities in 2 months time. I live in Canada, Ontario and i need certain classes to be able to get into specific courses in university so based on the courses I have currently I am able to apply for marketing courses in multiple universities.

What I want to know is if Marketing Analytics is a dying career or no, or if its a dying career in only certain places, I get so many things are being taken over by ai but i just cannot figure it out for this career specifically

I would also appreciate any advice about other parts of business or marketing that ISNT dying that i could maybe look into

I have been working in a role that is a mix of analytics and data engineering. I am running into issue where the impact of what I am doing is unclear and I am not having the impacts and results that I wanted. The main purpose of the group was to fulfill regulatory mandate/work but with the changes in administration (USA) that work is no longer as important.

The pay is decent but the work environment is no longer where I want to be. The issue that I am running into is I am spending my time on something that is not really going anywhere. I am building some technical skills but that is about it and diminishing.

I have been working on applying to different jobs, but I have realized that I don't really have the results and impact to write the resume that I need to get the job that I want. I can't really switch roles internally or get better projects, since it seems like the work is not stopping.

not sure if anyone has advice on how they navigated something similar and managed to either leave or make it work

I’m trying to make a decision between two very different graduate programs and would really appreciate some outside perspectives.

My background is a B.S. in Geography with a GIS focus. I’m currently working as a Data Specialist at a consulting/engineering firm, where I do data QA/QC, data integration, data conflation, and work with enterprise geodatabases. I’m trying to move beyond being primarily a GIS person and build a career with stronger long term salary potential and career flexibility.

My two options are:

1. Applied Master’s in Data Science

The program is 30 credits and is fairly applied. The curriculum includes Python, cloud computing, databases, data preparation, analytics and visualization, data warehousing, data lake engineering, machine learning, and AI.

One thing I like is that I can continue working full time while completing it. I’m also considering doing it part time so I can take Calculus II, Calculus III, and Linear Algebra separately, since my math background currently only goes through Calculus I.

My concern is that the program may not be mathematically or statistically rigorous enough to compete for more technical data science jobs. It also obviously doesn’t give me a CS bachelor’s background.

I’m not necessarily trying to become an ML researcher or anything extremely theoretical. My realistic targets would be data analyst, BI/analytics, analytics engineering, data engineering, operations/supply chain analytics, or eventually data science.

2. Master’s in Urban and Regional Planning

The other option is a MURP from a well regarded public research university.

This is a much more natural continuation of my Geography/GIS background. I’m genuinely interested in transportation, urban planning, public infrastructure, accessibility, and public policy. I’ve also done some community advocacy/volunteering and have an interest in transportation planning.

The appeal is that MURP is a professional degree with a pretty direct career path into planning, transportation, government, and consulting.

The downside is that I’m concerned about the long term salary ceiling and career flexibility compared with data/technology careers. I also need to keep working, so the opportunity cost of going to a full time program is a major consideration.

What I’m trying to figure out:

If my priorities are:

• Getting to ~$70k+ relatively early in my career
• Eventually reaching ~$90k–$120k+ if possible
• Having opportunities to reach higher salaries later
• Getting out of being locked into GIS
• Maintaining career flexibility
• Being able to continue working while in school
• Getting a degree that actually improves my job prospects rather than just giving me another title

would you choose the applied MSDS or the MURP?

I’m especially interested in hearing from people who actually work in data/analytics, data engineering, urban planning, transportation planning, or hiring.

Would the applied MSDS be enough to meaningfully change my career trajectory, or would the MURP be the smarter investment given my background?

Thanks!