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?

I’m an absolute fresher who recently graduated with a B.E. in Artificial Intelligence and Data Science. About 10 months ago, I landed my first job as a Data Analyst at a small digital marketing agency.

To be completely honest, I messed up early on. There was zero mentorship, the team was always swamped, and instead of taking initiative, I coasted. I didn't put in the effort to understand the business context or learn the marketing side of things.

Now, I’m paying for it. Because I didn't make myself indispensable with actual data work, the owner has me spending 7+ hours a day prompting AI to generate 1,500+ fake images for a completely AI-generated 925 silver jewelry brand they want to launch. It’s soul-crushing, and my technical skills are rusting.

Here is the saving grace, and why I’m asking for your help: I still have full access to our agency's data pool. We handle four seasonal D2C brands, one FMCG brand, and one lead-generation brand. There is a massive amount of real-world data sitting right in front of me, and I still have a window to build something impressive to either win back my actual job title or build my portfolio to escape.

I’m incredibly torn on what direction to take this, and I need advice from experienced data professionals:

Should I pivot toward Performance Marketing? Since I have all this D2C and lead-gen data, should I just lean into the agency environment, learn everything about ad spend, ROAS, and Conversion Rate Optimization, and try to transition into a Performance Marketing role?

Or should I stick to Data Analytics? Should I use this current role purely as a sandbox? I could build out automated dashboards, do customer segmentation, or run predictive churn models on these 6 brands just to build a killer portfolio, learn the marketing domain, and then jump ship to a real DA role at a better company?

Given the terrible job market and my background in AI/DS, what is the smartest way to leverage this specific dataset to get myself out of the AI prompt-engineering trenches? Any specific project ideas using D2C/FMCG data would be hugely appreciated

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.

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 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.

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.

When a dashboard looks clean, it is easy to forget that the inputs may still be messy. For marketing data, I try to check the boring parts before interpreting the chart:

- are the key events firing once, not twice

- are internal or test users excluded

- are paid, organic, referral, and direct sources being separated consistently

- are forms, calls, chats, or checkout steps tied to the same definition of a lead

- did any tracking change during the reporting window

If those checks fail, the dashboard can still be useful, but I would treat it as a debugging surface rather than evidence.

What pre-checks do you run before you trust a marketing or web analytics dashboard enough to make a decision from it?

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?

So two years ago I was a data analyst and then we reorganised and I became insights partner, which is the kind of title a consultant invents on a whiteboard.

Recruiter messages in the 12 months before: 14.

In the 18 months since: 1, and that one was for a job in a different country.

I only noticed this last week as my work got better, my output got more meaningful, and I became invisible at the same time. I'm now fairly sure nobody is searching insights partner because nobody outside my company knows it exists.

The obvious fix is to put "data analyst" back on, but that's not my title and guess it might get flagged in my team.

For people with invented internal titles - do you stick with it, or go for something more well known?

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.

As data engineering and AI/ML capabilities mature across industries, I’ve noticed a shift in how companies approach external vendor selection. Many enterprise teams seem to be balancing traditional consultancies with specialized data & AI partners that offer targeted domain knowledge.

Firms like SG Analytics, Fractal Analytics, LatentView, and Tiger Analytics have built distinct footprints here, offering modern data stack architectures, predictive analytics, and tailored AI visibility solutions.

For analytics leaders and senior practitioners in this sub:

• Is your organization leaning more toward specialized analytics vendors or staying with broad IT consulting firms for data & AI initiatives?
• What critical factors (e.g., domain expertise, GenAI readiness, cost-to-value ratio) carry the most weight when you evaluate external analytics partners?

Curious to hear how different teams are structuring their vendor strategy!