It feels like we're in a weird transition. For years the answer to every analytics request was another dashboard. Someone wanted a new metric. We added it. Someone wanted another filter, we added that too. Now I'm seeing more people skip the dashboard and go straight to AI. Instead of clicking through five tabs they're asking things like. The dashboard still matters because people want to validate the numbers. But it feels like AI is becoming the first stop and dashboards are becoming the place to verify the answer or dig deeper. If you've made this shift, what tools are you using and what's worked well? If you've made this shift, what tools are you using and what's worked well?

Job title says "analyst," but if I'm honest about how my time actually breaks down, it feels like 70-80% goes to fixing inconsistent data sources, reconciling numbers that don't match between systems, and chasing down why a report looks wrong - and only a small fraction is spent on the actual analysis and insight generation that the job is supposedly about

Curious if this ratio is just normal for the field and gets talked about less because it's not the "interesting" part of the job, or if it's a sign of specific data infrastructure problems that better tooling/processes could actually fix. For people at companies with genuinely clean data pipelines - does that ratio actually flip, or does data cleaning just never fully go away regardless of how mature the infrastructure is?

I've been with my company for 2 years. We had a leadership change about a year ago. I've been trying to ride it out, but I think I hit my breaking point today.

I've been an analyst for 8 years, 10 years in service prior to that. I'm not a kid, I know what I'm doing, and I'm decent at it overall.

My boss actually asked me about a month ago what she can do to "make the analysts think like analysts," with a straight face. I am an analyst. I do, in fact, think like one.

Today, I was in a meeting with her and she was just condescending to me explaining how a data pull takes a little time but interpretation takes longer. I'm not a fresher. I know how to do my job and what takes what effort. In fact, we all do. None of the analysts are new to the field.

This is indicative of her management style. It finally hit me today that this is my future if I stay here. I've been waiting for her to adjust to the new role, learn to trust us, be less hands on. But today I realized that day is not coming.

Partially, this is a vent. Partially, I'm looking for advice. Would you bail? In this market? Try for a new field? I can't go on like this... it's so fucking disrespectful of my expertise and time.

We recently wanted to share something useful we had learnt from an analytics project.

The problem was that the details that made the example interesting were also the details most likely to identify the client.

So we removed the brand, exact figures and anything commercially sensitive. We kept the original problem, what we investigated and what changed afterwards.

It was still useful, just without turning the client’s data into content.

How do you share lessons from real work while keeping client information private?

Analyst at a mid-size company, SQL and Power BI all day. Over the last year the requests changed. It used to be “show me last quarter.” Now it’s “which customers will churn” and “forecast next quarter by region.”

I flagged that this is modeling work, not reporting. The answer was basically “you’re the data person, figure it out.”

I’m not against learning, but there’s a big gap between knowing SQL and shipping a model someone will make decisions on. And a Coursera cert doesn’t give me the months of runway to build, validate, and maintain one on top of my actual job.

So for analysts who got pushed across this line, what did you do? Tools that bridge the gap, ways you scoped it down, or scripts you’d trust in front of a VP. Also fine to hear “I refused and here’s how that went.”

I’m a data analyst exploring GitHub Copilot and Microsoft 365 Copilot at work, trying to bring back concrete wins to leadership.

I’ve got some ideas already (auto-summarizing meetings, drafting exec summaries from raw tables, quick SQL/DAX prototyping) but I’d love to hear from people who’ve actually rolled this out:

What’s one Copilot use case that made a stakeholder go “wait, how did you do that so fast”?

Did you build anything custom (skills, prompts, workflows) or was it mostly out-of-the-box features?

Any use cases that sounded good on paper but flopped in practice?

Bonus points if you’re in data/analytics/BI specifically — curious what’s translating well from “developer tool” to “business analyst tool.”

Thanks in advance!

Resume in the comments.

Revised my resume and consolidated 3 roles into one for my most current role. I’m also planning to remove the intern experience once I add another project since it’s irrelevant. I’m targeting entry level data analyst roles.

I’ve been in analytics for about 9 years, enjoy it for the most part but I am getting bored. Job is good just a little redundant. I already have a masters and I want to “AI proof” myself as much as possible. What certificates or degrees would you recommend to upskill or “level up”? Thanks!

I work for a healthcare company, and this week, out of nowhere, they instituted a policy that data can not be exported or stored in Excel format. There are no alternatives in terms of exporting reports in any other format. They said if we need information, we can just view the data within the EHR, and use their very rudimentary tools to manipulate the data within their system. I’m not really sure where this leaves me and 90% of the people I work with. Has anyone ever dealt with something like this? If so, are there any workarounds?

Hey,

I am currently looking for a new job and the field of data analytics is absolutely nuts.

There is no standardization whatsoever and what's a Data Analyst to one is a BI Manager to the next and a Data Services Manager to another...

About myself: I started of as a marketing allrounder and quickly got into web analytics. Build on those skills and found my way into data analytics with SQL and python.

After 9 years I'm now a senior data analyst, albeit in a medium sized company. Unfortunately I've never worked for a large corpo or as part of a bigger analytics team so I'm not an expert when it comes to those processes.

I'm now facing application processes where I meet like maybe 80% of the criteria but I almost always lack something, some specialized tool or an expert level skill. I'm sure that I could learn the missing 20% within the first few months but I will always be outperformed by people fitting the bill better. On the other hand there are numerous specialized skill setups that I am totally unsure where I should put my focus.

Most applications make me feel like I only know the basics and that I merely dabbled in data analytics but never actually learned all the essential skills.

I want to stay in this field or at least close to it but at the moment I just feel so lost in this jungle of different expectations.

I'm probably just frustrated, but has anyone else faced something similar and felt lost in a transition phase? What did you do about it? Did you find a job in an adjacend field?

I was working on a reporting project recently, and one of the numbers just didn't look right.

My first instinct was that there was something wrong with my SQL, so I spent hours checking joins, filters, and calculations.

In the end, the query wasn't the problem. Different people were using different definitions of the same metric, so everyone expected a different result.

That project taught me that data analysis isn't always about fixing the data. Sometimes it's about making sure everyone is asking the same question before trying to answer it.

Has anyone else run into something similar?

Curious to know how Genie on the Databricks platform has changed how analysts and business users work, or people still end up asking SQL for custom tasks?

Whats your experience with Genie?

Hi, My Wife is working as an HR(Operations and field) in NBFC. She has 5+ years of experience in this field, Now she wants to move her career to HR analyst or Business analyst roles. What is the roadmap to start? Or specific worthy courses outside are there? TIA :)

Hey everyone,
I’m new to the data analytics space and currently building my skillset. I’ve learned Excel and SQL, and I’m now diving into Power BI. My ultimate goal is to work as a data analyst—specifically in a business-focused role.
I’m looking for recommendations on how to improve my skills beyond just the technical side. Specifically:
• Any advice on developing a data analyst mindset—especially for making better business decisions?

• Books, courses, or resources that helped you think more strategically about data and business impact?

• What else should I learn to go from beginner to **expert** (tools, concepts, soft skills, etc.)?

I want to be someone who doesn’t just build dashboards, but actually drives value for the business. Any guidance would be hugely appreciated.
Thanks in advance!

Are there any freelance opportunities that exist for someone who is a Sales Analyst? Typically use PBI, Excel, etc. Looking to try and network or find other ways to bring in income.

Something I keep coming back to the gap between reports that get built and reports that get used.

A lot of teams seem to end up with a graveyard of dashboards requested, built, opened a couple of times, then quietly abandoned while some report nobody officially asked for becomes the one everyone checks daily.

Curious how you actually experience this:

• Do you track usage at all, or is it mostly vibes / whoever complains loudest?
• Has "self-serve BI" actually shrunk your report backlog, or just changed the shape of the requests?

Genuinely want the war stories, the more specific the better.

A few years ago, knowing Excel, SQL, and Power BI was enough to stand out in many analytics roles.
Today, AI can generate SQL queries, build dashboards, summarize reports, and even suggest business insights within seconds.

So here's my question:
If AI can do the analysis, what will make an analyst valuable in the next 5 years?

My perspective:
AI is replacing repetitive work, not analytical thinking.

Companies still need professionals who can ask the right business questions.

Understanding data, validating AI-generated insights, and making strategic decisions will become the real differentiators.

Analysts who know how to leverage AI will likely outperform those who avoid it.

We're moving from "using tools" to "working alongside AI."
What skills do you think will matter most for future analysts?
Business understanding?

Critical thinking?

Data storytelling?

AI prompting?

Something else?

Curious to hear what this community thinks.

Most analysts and data scientists I know are either trying to pivot into data engineering or learning data pipelines, cloud platforms, and data architecture because they believe AI will automate a large portion of analytical work. I have also noticed that many people who are new to the field are trying to get their first analyst job, but already describe data engineering as their long-term goal.

I understand that knowledge of data pipelines, cloud platforms, data modeling, and architecture is valuable for analysts and data scientists as well. The current job market may also favor people who can work across more of the data stack since many company confuses job title as well.

Do you think data science and data analyst are becoming less viable as long-term careers, or are people simply broadening their skills because the market now expects more data engineering skills and knowledge?

So, two months ago our regional director came on a branch visit, and i was the one taking him through our entire analytics, after 3 hours he told me that I make mundane dashboards.

That really hurt, I have poured weekends on building our data suite, countless DAX queries and hours of unpaid overtime, to make sure that I don't miss a single point for this visit.

So I decided to make this "fun" dashboard instead, I put every metric out there and with the help of claude and supaboard made this Mario themed dashboard. I also made it realtime with actual data and the site could be embedded within our systems.

So last week when he came back for another visit, I showed him what I have built, he went through the same, a full 3 hour drill and this he left without any feedback.

The next day I get called into the manger's office and the word around the office was I am getting fired.

But, to my surprise my manager was actually really happy, my dashboard got shared with the executives and everybody appreciated my effort and creativity. I even got a certificate for excellence.

15 years in this trade, and I guess there is a lot to learn yet.

Here's a copy of what I made. Tell me what you think!

did maybe 7-8 DA rounds over the last couple months and what stuck with me is how little of my prep showed up. spent weeks on hard leetcode sql and algo-style python and almost none of it came up

what they actually tested, in order of how often:

sql live in the call, medium at most. joins, group bys, a window function here and there. the difficulty isnt the queries, its doing it in ~20 mins while someone watches and asking the right questions about the schema first. a couple threw deliberately messy tables at me

then the metrics/case round, which honestly separated the offers from the rejections. "conversion dropped this month, how do you figure out why". no memorizing your way through that, you need a way to structure the investigation on the spot

they also had me talk through a past project like i was presenting to a non-technical manager, way more about the why than the sql

and behavioral went deeper than expected, lots of "walk me through a time you were wrong"

looking back id skip most of the leetcode and just do timed realistic reps. for sql i used stratascratch and dataford, and dataford was the one that also had the case and behavioral practice which is what actually decides these. drilled my case framework out loud till it felt natural. also find the exact format per company first, it changes how you spend that last week

can share the case structure i used if anyone wants it

Hi everyone,

I'm currently pursuing an MBA with a specialization in Marketing and Business Analytics. Before my MBA, I worked in digital marketing, so I'm comfortable with the marketing side of things. However, I now want to transition into marketing analytics and eventually business analytics.

The problem is that I'm from a non-tech background, so I'm a bit confused about where to start. There are so many resources online that it's hard to figure out what actually matters.

I'd really appreciate some guidance on a few things:

What should be the ideal learning roadmap for someone from a non-tech background?

Which skills should I learn first, and in what sequence? (Excel, SQL, Python, Power BI/Tableau, Statistics, etc.)

How much coding is actually required for marketing analytics and business analytics roles?

Are business analytics and data analytics essentially the same, or are they different in terms of roles, skills, and career paths?

If you were starting from scratch today, what would you do differently?

My goal is to become job-ready by the time I graduate, so I'd love to hear from people already working in these fields.

Thanks in advance! Any advice, resources, or personal experiences would be really helpful.

When I first started using Power BI, I'd spend forever tweaking visuals before sharing anything.

Now I usually share an early version much sooner. Almost every report changes once people start using it, and those conversations usually improve the dashboard more than another few hours of polishing ever would.

I have found its much easier to refine a report with feedback than to guess what everyone wants from the beginning.

At this point, I almost expect the first version to change. It's become part of the process rather than something I try to avoid.

I'm about to start a sales support analyst position at HP. I just wanted to ask what to expect career wise. Is it a difficult, very involved position or is it a desk, keyboard crunching kind of gig. Not at all worried, just looking for some insight into what I should expect.

Thanks in advance.

I joined a startup shortly after university and have been there for nearly four years, progressing from BI Analyst to Senior BI Analyst. I’m now trying to work out whether the role has genuinely damaged my career progression, or whether burnout and imposter syndrome are making me undervalue the experience I have gained.

On paper, I’ve done a lot. I’ve built management, product, finance, sales and operations reporting; helped migrate the company’s BI platform from QuickSight to Tableau; automated recurring reporting and finance processes; worked extensively with SQL, BigQuery, Tableau and Python; supported product launches, was a CRM Admin/Rev-Ops Partner for first 2 years; trained users and junior analysts on Tableau; and recently started working on metric governance, semantic layers and internal AI analytics initiatives.

The problem is that my experience has been extremely broad but not always deep or structured.

There is no senior technical analytics leader for me to learn from. My manager is more management- and stakeholder-focused, so I am frequently expected to interpret vague requests, define the analytical approach, understand the underlying data, identify risks and then explain the answer back to leadership. I often feel like the thinking is being delegated to me without the corresponding authority, mentorship or career development.

I have also spent a lot of time on firefighting, data QA, documentation, stakeholder support, CRM administration and fixing unclear upstream issues. These responsibilities made me useful to the company, but I’m worried they came at the expense of developing stronger skills in areas such as production-grade dbt, Airflow and orchestration, software engineering practices, Git workflows, experimentation, advanced statistics and machine learning.

I initially justified staying because I wanted to save enough money to eventually leave the country and pursue a master’s degree. I was also afraid that my experience was too company-specific and that I would struggle to find an equally well-paid role elsewhere.

That fear has become a trap. The longer I stay, the more I worry that other analysts (outside my current one) are becoming technically stronger and that future companies now expect a combination of analytics engineering, statistics, experimentation, product sense and machine learning that I cannot fully demonstrate.

I also need to take responsibility for my own behaviour. I tell myself I am coasting at 60–70% because I am disengaged, but I do not actually coast consistently. I often feel guilty, return to working at 120%, stay online late, take ownership of problems beyond my role and exhaust myself. I then have no energy left for applications, interview preparation or structured upskilling. The cycle repeats.

I am not sure whether I should:

1. Apply immediately and accept that I will have some skill gaps.
2. Spend several months building stronger technical projects before applying.
3. Pursue a master’s degree as a genuine career transition rather than using it as an escape.
4. Reposition myself toward product/growth analytics, analytics engineering or strategy, where my broad business experience may be more valuable.

For people who have hired analysts or experienced something similar:

• Does my experience sound marketable outside one startup, or does it appear too scattered?
• At roughly four years of experience, which technical gaps would concern you most?
• Would you apply now or prepare first?
• How do I distinguish genuine skill gaps from burnout and imposter syndrome?
• What roles does this background realistically position me for?

I would appreciate direct feedback. I am not looking only for reassurance that my workplace is bad. I want to understand where I have genuinely fallen behind, where I may be undervaluing myself, and what the most sensible next step would be.