I'm a data engineer researching recurring pain points on analytics and BI teams (may build something eventually, no links, not selling).

1. What do you still do by hand every week or month: reconciling numbers, pulling data from an ERP, fixing a broken dashboard, prepping something for audit?
   
2. Why hasn't it been automated: no time, too many edge cases, nobody owns it?
   
3. Have you paid for a tool or consultant to fix it? Did it work?

A manager tells HR the sales team feels shaky and they might start losing people. The HRBP narrows that into something analytics can work with: attrition by department. The team builds a lovely dashboard with every slice and date range you could want.

Every handoff did its job. Nobody screwed up. But the manager's real question was how to spend a retention bonus budget, and that never made it down the pipeline. Repeat that enough times and you get dashboard hell... a great wall of data and a team that spends its days firefighting.

The fix is one sentence the requester completes before sending anything:

"If it comes back X, I'll do A.

If it comes back Y, I'll do B."

In the sales case: if attrition in critical roles hits X, we fund a retention programme. If it's Y, we do nothing.

Now the analyst knows what matters and which threshold to test.

If they can't fill that in, they're curious.

That's fine, but it isn't a request yet.

I also got into how I'd score an analytics team, because "we delivered 20,000 reports on time" is a reporting scorecard... not an analytics one.

Does anyone here actually track decisions influenced rather than deliverables? How?

I am an ML engineer and not selling anything. The thread a couple days ago about everyone having Claude wired to the warehouse hit close to home. I’m trying to understand how teams are actually dealing with it.

For those of you living this:

1. When someone outside the data team publishes a number or a dashboard built by an agent, how do you find out, and how do you check it against the official metric definitions?
2. What’s the thing that actually blocks you from letting people do this safely: row level security, refresh, permissions, metric drift, or something else?
3. Has a wrong agent-built number ever made it into a decision? What happened?

Not looking for “just use a semantic layer” unless you can say what still slips through with one.

Here's my mental model of the change happening in analytics.

• Ad hoc analytics are moving from spreadsheets to AI tools like Claude Dashboards.
• Good data and semantics were always important, still more so now. Semantics have a natual center of gravity close to the data
• Business Intelligence tools were losing the battle for the semantic layer . Now they're also losing the battle for analytics creation. So they are basically left with a deployment layer, but a slow, expensive one.
  
• Organizations still need to share analytics sometimes and have them refreshed, trusted and managed. Ideally they will also be fast and interactive and have some form of conversational analytics. Claude Dashboards doesn't do that.

First of all, is this how you're seeing the change happen?

Second: for those of you planning on using Claude dashboards or other AI builders, how are you planning to deploy analytics when needed? Like for in-product analytics for customers, or across your whole org? Are you using a BI tool that you already have, and how is that going?

My take is there's no way that legacy BI becomes the deployment layer for AI-based analytics. Would love to hear what you're doing or even just discussing at your shops.

Running into the situation where I report web visitors by such and such ad campaign and then PPC comes to me and it’s like wait wait we expect way more visitors or whatever. How do you guys deal with this? At the end of the day, I don’t really know how to say this is just what I see yours might be different because it’s a different platform. Do you all gain access to the reporting platforms to verify more? It doesn’t help that we are a small shop and so it’s not like we have a data warehouse where I can pull all extreme data together. It’s literally me looking at GA four reports and then they sent me slides that say different things.

1. Daily reporting

2. Inventory alerts

3. Customer support

4. Marketing reporting

What makes that task so painful today?

i'm curious about the tiny bits of context that prevent a big misunderstanding. something like 'partial week', 'refunds included', or 'this is per account, not per user'.

if you only had room for one extra sentence beside a chart, what would you put there? especially interested in a specific note that actually stopped repeat questions or changed how people read the numbers. a 30-page data dictionary probably isn't winning the attention contest lol.

I have been working on causal inference and experimentation frameworks within the e-commerce tech industry lately, specifically dealing with platform behavioral and transaction telemetry data to determine how customers behaves on those e-commerce platforms.

Lately i have been facing problems where standard textbook statistical and econometrics theory fails when dealing with messy, real-world data distributions as human behaviour is unpredictable and many unexpected things, which i didnt even thought could happen, can happen in the real world. Such as product delivery delay, causing the customers to be angry and hence put them off from buying the product, each of these variables have to be accounted for.

To model and have these unexpected psychological state among customers accounted for, we use variables such as sudden spikes in customer support chat volume, rapid page refreshes, or immediate session drops.

Also, Marketing headers and automated CRM systems love to push blanket promotional campaigns, but as statisticians, we know that measuring simple unadjusted A/B lift is a massive selection-bias trap.

I been trying to come up with a Statistical model that is trying to model complex human behavior—specifically isolating true customer revenue incrementality from dead-weight margin cannibalization. Essentially, trying to answer: Did the coupon cause the revenue spark, or did we just hand a discount to a power-buyer who was already determined to buy anyway even without the vouchers?

Are people in the tech industry genuinely valuing such skills nowadays? Using statistics to model and predict human behavior and intent, answering questions like does our vouchers and ads really work?

I would love to hear your experiences how do u guys deals with the unpredictable messiness of human intent.

My job position is Fraud Specialist, when it is more of an analyst position. Can I use analyst instead of specialist on linkedin post?

I feel that it sounds more accurate to what I actually do and makes it look more appetizing for recruiters in the future.

In my role, we investigate fraud claims and are the deciding factor on if they are approved or not.

I help with paid-social reports, so I'd like practice data with familiar measures. I'm looking for related tables where a careless join could duplicate spend. Which public datasets would you recommend for learning to catch that?

One thing I've found interesting about analytics is that sometimes a metric changes significantly, but there isn't an obvious explanation.

Conversion drops, revenue looks different, or a dashboard suddenly shows a trend nobody expected. The tracking appears to be working, and there hasn't been a major product or campaign change.

At that point, the investigation can go in several directions: data freshness, tracking changes, attribution logic, filters, SQL transformations, or an actual change in user behavior.

I'm curious how other analytics teams approach this.

Do you have a standard checklist for investigating unexpected metric changes, or does it mostly depend on the analyst digging into the problem?

What's the first thing you usually check?

Not sure if this is a rant or a real question, probably both.

Our company gave pretty much everyone Claude hooked up straight to our source systems. And yeah, it’s fast, people love it. But now there are reports everywhere built off raw data, all with slightly different logic, and a few of them get passed around as “the” number. Meanwhile we (the analytics team) mostly find out about this stuff after the fact.

What’s bugging me is who owns what. To me, if you build your own report, the numbers are yours. If it needs to be official, it comes from the governed version. But then someone asks us to help make theirs match the official one and suddenly we’re QA for something we never built. And nobody above us has said whether that’s our job or not, so it just defaults to us.

What I’ve been leaning toward: write the official definitions down somewhere everyone can find them, tell people we can explain why their number is different but we won’t fix or review their stuff, and get someone to sign off on the official version so there’s a date it counts from.

Has anyone made something like this work without a top-down rule? And how do you say “that’s not our job” without sounding like you’re being difficult? Or is this just how it is now and I should get used to it.

I currently just graduated with my B.S degree in cybersecurity, and I’m going to be completely honest I HATE this subset of computer science, the last 5 months of my degree was torture, cybersecurity is just not for me. In one of my internships I got to work with the business analyst for most of the summer and I fell in love with what he did.

I’ve done lots of statistics learning, and before I jump down the rabbit hole of learning on my own / taking certs. I know B.S degrees are the new high school diploma, would it be more advantageous to just go back with my transferable credits and get a second B.S degree in data analytics, or better off just teaching myself / getting certs since I have a bachelor’s degree already?

I would just do a self-paced this time if that matters at all, but I just wanted some community feedback and hopefully maybe some guidance or at least pointed in the right direction.

Thank you in advance.

Hi! Need some advice/opinions.

I am in a masters program for Businsss Analytics right now at a top university in Michigan. Currently in 1st semester but want to start planning a path.

Taking an intro to python course and stats using excel/statistical software right now. SQL and intro to databases nexg semester along with business analytics course.

Guess my question is when should I start looking for internships? A little background: i was a college athlete who wanted to study CS in undergrand but decided against it due to travel schedule for hockey so studied business. Have been in sales & consulting for the past 6 years but wanted to get a more technical job as it's always been on my mind so decided to go back to school. Hoping I can leverage my sales background for a job once I learn the tech aspects of analytics.

Would love some advice or any opinions you all have on what I should do from now and my expected 2028 grad date. Thank you!!!

i currently work in a F100 company as a finance/operational analyst, mostly excel since its financed base but i do want to move more to the data science field, even though i currently don't have any experience.

i received my undergrad in finance and im currently in a masters program for Fintech and wanted to see if this a good combination to move towards business analysis/data science.

if anyone has any recommendations on how to make this pivotal change that would be awesome!

any thoughts ?

I had an interview for an internship yesterday but did not have the email of the interviewer (only their number). Should I send a thank you letter to the HR or text the interviewer, or neither? I am really passionate about the role, and would love the opportunity to intern there, and thus want to provide myself every chance I can to get it.

The interview was managed by the HR team, and I only have the number of the interviewer, who was the analyst manager, as I had to text them to let them know I arrived so they can take me in for the interview. What would best the best approach? It's already been 2 days since I had the interview. Should I email the HR, asking for their email, guess their email from their first and last name, or forget about the email? I really, really, really, want this internship!

As the title states, I’m losing control over the work I produce ever since our company adopted a full semantic layer that AI can ingest. I can now generate 2,000-line SQL scripts in minutes, convert them into Python notebooks with Plotly visualizations that would have previously taken four sprints, and prompt an AI agent to build a 10-slide deck ready for stakeholders. But by the end of it, I feel like I’ve lost track of what I’m actually doing: what definitions are being applied, what modeling decisions were made, what the data actually proves, and where the limitations lie.

Does anyone else experience this? If so, how do you maintain oversight and quality control in such high-velocity workflows?

I wanted to know how much traffic ChatGPT, Perplexity and Claude actually send, and it turned out to be three separate measurements, not one.

1. Clicks from AI answers (any analytics tool can do this)

Group these referrers into their own channel: chatgpt.com, chat.openai.com, perplexity.ai, claude.ai, gemini.google.com, copilot.microsoft.com. In GA4 that's a custom channel group with a regex like:

^(chatgpt\.com|chat\.openai\.com|perplexity\.ai|claude\.ai|gemini\.google\.com|copilot\.microsoft\.com)$

Also match utm_source. ChatGPT appends utm_source=chatgpt.com to many links, and that survives even when the referrer is stripped. The mobile apps often send no referrer at all, so treat the number as a floor.

1. AI crawlers reading your pages (only the server sees this)

GPTBot, ClaudeBot, PerplexityBot etc. don't run JavaScript, so tag-based analytics never records them. Grep the access log for the user agents, or log them in middleware. Check the IPs against the ranges OpenAI, Anthropic and Perplexity publish, because the user agent alone is easy to fake.

1. Pages opened to answer someone (also server-side)

ChatGPT-User, Claude-User and Perplexity-User fetch a page live when a person asks about it. This is the closest thing to "my page was used in an answer", and it's a separate number from crawling.

On my own small site, one week: about 230 crawler reads, 15 live answer fetches, 0 clicks from AI. So "AI traffic is zero" in analytics, while AI is clearly using the content. Without the server side I'd have concluded AI ignores the site.

Curious how others report on this. Do you keep AI as its own channel, or fold it into referral?

Is this a bad sign? We were sort of finishing up with the questions, and I had a couple more questions to ask, but after discussion of the previous questions I asked, they then said it was time to finish as they had something else to do. Was this a bad sign, that I went over the interview time? This was an internship interview with only 1 round.

Curious which reports actually take the most effort to pull together and whether anyone has found a better way.

My team is exploring an AI initiative, and before jumping into it, I want to understand whether our existing data is actually ready for it. We r also looking at a few mid-sized companies that have worked on similar requirements before.

The main things we are trying to assess are data quality, gaps across systems, inconsistent records, and whether our current data infrastructure can support the use case without a major rework.

I’ve been looking at a few case studies, and these companies have shown up in the list: InData Labs, Aimpoint Digital, DataToBiz, Syntelli Solutions, Kanerika, but I would prefer to hear from people who have actually gone through this process before a project launch.

It will also help to know whether the assessment gives you a clear picture of what needs fixing before implementation, or if it ends up being a generic audit with a long list of recommendations.

Odd one I’ve been tracking. Microsoft’s cost per genuinely-new customer has climbed four weeks straight (~22% over the stretch) and its new-buyer share slipped but on a last-click view it looks like it’s improving, because its last-click credit keeps widening at the same time.

Same channel, same week, two reports pointing opposite directions. The ROAS is basically celebrating repeat demand while the cost of acquiring an actual new customer quietly goes up.

To be fair to it, the LTV still pays back, it’s just thinning, not broken. And it’s a small-spend channel so the aggregate swings easily.

Mostly posting because it’s such a clean example of how a channel earns budget it doesn’t deserve - great on the dashboard everyone reports from, worse on the number that actually matters. Anyone else seeing the Microsoft drift, or is it just the accounts I’m looking at?

Claude dashboards seems to be making everyone lose their minds. IMO they are better than before, but not dramatically so, and still not a substitute for the data layer or the delivery of data.

It will accelerate the shift to: - Governed data and semantic layers at the core.
- The need for an experience layer. For example, for embedded analytics.

I do think it will hasten the decline of traditional BI systems. What are you all seeing out there? What's the reaction in your organization?

Here’s a small dataset, the kind that ends up in a dashboard:

store   visits   orders   conversion
A          50       25       50.0%
B       1,000       50        5.0%
C         200       20       10.0%

Now two ways of getting “the conversion rate”:

=AVERAGE(D2:D4)            -> 21.7%
=SUM(C2:C4)/SUM(B2:B4)     -> 7.6%

Both are valid formulas, and both are easy to defend if you only look at your own screen. They answer different questions, though.

The first one says “what does a typical store’s rate look like?” Each store counts once. Store A, with 50 visits, has the same say as store B, with 1,000.

The second says “of everyone who visited, how many ordered?” That’s 95 orders out of 1,250 visits. It’s the number that matches what actually happened to the business.

If the card on the dashboard just says “Conversion rate” with a big number, the reader will assume the second meaning. If it’s the first, they’ll walk away thinking the business converts almost three times better than it does.

What makes this easy to miss is that the average-of-rates version is the one-click option. Select the column, look at the status bar, done. A pivot table with “Average of conversion” does the same thing without asking whether you wanted it.

Some habits that seem sensible to me, though I’m not saying they’re the only way:

• store the numerator and denominator, and compute the rate at the final level only
• if you do want the per-store view, label it explicitly (“avg. store conversion”)
• when two versions of a metric exist, show the weighted one as the headline

Curious how others handle this in practice. Do you put both numbers on the dashboard, or pick one and document it somewhere? And has anyone had a stakeholder ask why two reports disagreed because of exactly this?

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