"Can we get visibility into this?" - I will not act on this information. I want it to exist near me, like a plant.

"The data doesn't feel right." - The data contradicts what I already told my boss.

"Interesting." - I stopped listening at slide two.

"Can you make it more actionable?" - I don't know what I want. Iterate until I recognize it.

"We're a data-driven organization." - We have dashboards.

"We need to be more data-driven." - We also don't open them.

"Quick question." - Project.

"Super quick question." - Two projects and a definition fight.

"Can we add AI to this?" - I attended a webinar.

"What's our AI strategy here?" - I attended two webinars.

"Directionally accurate." - Wrong, but in a direction I like.

"Single source of truth." - My source. Of my truth.

"Let's align on definitions first." - This meeting will not survive. Nor will the next one. See you in Q3.

"Can you pull this real quick? Shouldn't take long." - I have already promised it to someone.

"Great analysis! Let's discuss next steps." - This will never be mentioned again.

"Great analysis! Really great work." - This will never be mentioned again, but warmly.

Look, in 2004 — a few years after I was born — I’m sure Tableau was quite groundbreaking. But it’s an absolutely unacceptable piece of software at this point. Keep in mind that Tableau is charging about $700-$500 per creator license according to some sources. At this price point, you could use something open source like Superset, Metabase or Redash which will accomplish most of what organizations need for nearly $50 per license.

Tableau at this point seems to be an industry standard primarily because of its affiliation with its parent company — Salesforce. There’s no end to how many dashboards I see that are inconsistent in terms of quality, spacing, and design even from the same company.

Tableau is hyper-focused on customization when most dashboards and BI layers require standardization. It feels like a product made for boutique dashboard design. And yeah, there are cool things you can do with it like make a flower graph or some other esoteric visualization. But those visualizations are unnecessary for the modern business. Sure, you can merge 8 datasets from disparate sources if you want - but seriously - why would you ever want to merge someone's Excel document on OneDrive with your production SQL query?

If you're an organization large enough to afford Tableau, you can afford better upstream data engineering. Simple.

The most important issue with Tableau is that data analysts are no longer dashboard designers. I’m a data engineer, a BI user, and an ad-hoc analysis deliverer. Not a dashboard designer. Sure, I want sensible views for my stakeholders, but those should take no more than 5 minutes to create and populate. Tableau is fast, but I promise that I've created dashboards in less than 1 minute using some of these other tools at a cheaper cost. Tableau cannot do that. You will spend hours on dash boarding, creating several sheets, trying to mash them up into a dashboard, setting up the Tableau Cloud, or whatever else.

The goal of any tech organization is to automate away most of the unnecessary work. You cannot automate Tableau. You can't access Tableau dashboards as code in a way that allows you to mass update every Tableau dashboard to change the name of a few metrics all at once.

I could go on and on about specifics about Tableau, but the price point, the difficulty of use, the impossible navigation of their Server and Cloud products, the lack of open source modification...

Coming up on 5 years at my current company, 6 YOE.

Just made director, and got a huge pay raise! Salary at 118k, and a bonus that brings TC to 160k/year.

Track thus far:

Year 1: Data Analyst - 51k

Company change..

Year 2: Data Analyst - 64k

Year 3: Senior Data Analyst - 80k - Performance based promotion

Year 4: Senior Data Analyst - 88k - 10% raise for performance

Year 5: Senior Manager - 103k - New team

Year 6: Director - 118k + 40k Bonus - Performance based

Just hype. No team, no direct reports, just me and the grind.

Edit: love all the haters :)

For context, I quit my consulting job with nothing lined up about 5 years ago. The only skills I had from that role were SQL, Tableau, and some company-specific applications. I met a guy out in New York who was the CEO of a fast-growing startup and asked if he needed a data guy. I flew in for 5 in-person interviews and got the job. I used my SQL and Tableau skills, added Python and Excel, and was promoted to Lead Data Analyst after 1 year and more recently to Head of Data after making some large contributions to the company’s culture and top line.

We were acquired by our top investor group and now I mostly do data analyst mentoring on the side. I’ve seen countless mistakes that people make both in the application process and after being hired. I’d love to answer some questions for you all!

EDIT: Lots of great questions here, so I'll share some of my high-level answers. Hope they help!

Application Process: 1. Email the hiring manager directly after submitting your application. You can find their email on RocketReach.

1. Think about how you can contribute to the company before you join. What does their data team likely do all day? Come up with ways you can help and share them with the hiring manager.

2. Have numbers on your resume. Even if they're estimates, include them. Hiring managers want to see A) you made an impact and B) that you understand true impact is quantifiable.

3. Practice your interview answers but do NOT memorize them. Allow yourself to be genuine in the moment. Use AI apps like Vocal Image to improve your communication skills if needed. The goal is to speak at a steady pace while enunciating, making eye contact, smiling, projecting, and breathing. When in doubt: SLOW. DOWN.

4. See if you can connect with a recruiter, tell them your story and skillset, and have them share your resume with their clients. They’re great advocates if they’re on your side.

I have worked at companies like LinkedIn, Pinterest, and Meta. Over the course of my career (15+ years) I've hired many dozens of candidates and reviewed or interviewed thousands more. I recently started a podcast with couple industry veterans to help people break in and thrive in the data profession. I'm happy to answer any questions you may have about the field or the industry.

PS: Since many people are interested, the name of the podcast is Data Neighbor Podcast on YouTube

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've run a small analytics agency since 2017. Primarily in the database layer (organizing, cleaning prepping data) and then shipping it to PBI and Tableau for dashboards.

Met with one of my favorite clients today for our weekly and he said he doesn't want to talk about PowerBI - he wanted to show me everything he's built himself in Claude.

What followed was an hour demo of - more or less - how he was planning on replacing us with this Claude Cowork pipeline.

Luckily they are good people, and they like us, the conversation was along the lines of

"How can you support us transitioning in this direction".

It just have easily could have been "bye felicia".

But man - what a wakeup call. I spent the next hour on the treadmill, crafting my advice.

Their plan was to have Claude sit directly on top of an ETL tool (won't name names, there are many options for this). They could ask it any question they wanted, AI would go to the tool, pull in the right data and answer the question. They'd even set it up to write to specific google sheets too. It was impressive.

But risky. Here were my bullets back.

1. Traceability - when (not if) something goes wrong, how can you find it, and how easy is it to fix. It's a black box you don't have access to. Troubleshooting it is near impossible.

2. Consistency - factoring just human nature aside, asking the exact same question on different days could lead to different results. Based on algorithm changes (infrequent but they happen) or based on existing/new context in a chat. It's really hard to guarantee consistency with AI. Try it yourself ask a question today, interact with the chat and ask the same question tomorrow, is the output identical?

3. KPI definitions - you ask it for conversions from google ads. Does it know what a conversion is? Does it know how to calculate net sales? And tying to above, will it be the same twice?

A few other things too like privacy and token usage. My suggestion was to do the ETL into BigQuery, then create a curated dbt layer with all the logic, proper naming, agreed kpi definitions, and condensed data in there. And then have Claude sit on top of that instead.

Idk, we'll see where it goes. Eye opening day where, basically what I knew as always coming, came.

I don’t want to discourage people trying to make a career switch— but, right now is a terrible time for entry level data analytics jobs and tech in general. You’ll be competing with those who were laid off from prestigious big tech companies amongst many others in smaller firms.

I was recently laid off in January and have 6-8 years of relevant experience in Analytics, Python, SQL, and R. It’s been a struggle… most of the jobs I’m applying for require 2-3 years of experience and I have received 2 callbacks for jobs out of 100+ applications so far. If your degree is outside of statistics, data science, computer science, or some quantitative / analytics program, (🙋‍♂️) it’s going to be difficult.

I’m posting this so you can set your expectations if you struggle to find a job at first. If you’re in it for the long haul, awesome— keep your current role and apply like no other. But, I wouldn’t expect it to come easily or in the short term.

A quick tip: focus on one industry and have case studies you can use relevant to that industry (insurance, healthcare, SaaS, finance, etc.). Most of these companies are looking for experience in their industry. A major part of analytics is understanding the environment you’re operating in— far beyond code and charts.

There are some other factors, too. On-site jobs are easier to get. Remote work is very competitive, and depending on what state you live in they might not consider you off of that alone.

I hope this helps shed some light on the current market, I’m free to answer any questions you may have.

I work as a Senior/Staff DS at one of the $1T firms, and clocked 15 years in Data Analytics/Science roles. I have mentored hundreds of students who have passion in analytics the past 5 years: including resume checks, doing mock interviews, career guidance, and referrals for the exceptional students.

However, the past year there has been significant top-down pressure to integrate AI into our workflow. This isn't isolated in my firm, it's impacting nearly every large company. Even the recent layoff from Amazon, Meta, and Google showed a lot of shedding of SWE roles, especially junior roles, given advent of AI.

This is specifically translated as the grunt work of drafting dashboards, coding, researching, etc. is all shifting to AI. These activities used to be the primary point for entry level roles. However, as more activities are shifting to AI, hiring will gradually be tighter and tighter as the work of 3-5 people can be done by a single person. It's becoming evident this is a phenomena will gain tremendous amount of momentum. A dramatic shift in how we approach job hunting is needed - especially those who are investing tremendous amount of capital into university programs.

I'm starting an AMA based on what I've experienced so far and what I've noticed worked for students in the field. So I hope I can tackle as many questions as possible

I'm not taking in any mentees at this time.

My girlfriend's been creating invoices in Excel for her family's business that's overseas. But I realized it was taking her hours to create each invoice (she would have to translate all the factory info from Chinese/English, take basic calculations each time, and re-type a lot of fields. So I mentioned to her a while back that she would save so much time if she just created a template that automated the legwork of the process, but she didn't seem convinced.

Well, yesterday, we finally sat down to look at it, and I made some pretty basic changes: a data-dump sheet in the background linked to the front-facing invoice, the translate() function to take out the manual translation process, and rounding/ceiling functions to clean everything up. Basically, it cut the process down from hours to what I imagine is an hour max for each invoice.

The funny part is that she was absolutely blown away each time I used one of these functions - apparently, she always associated Excel with being not very capable and for old people. I told her that we were barely scratching the surface of what Excel can do (I'm not even good with Excel lol) and that blew her mind even further.

Recently, I've been trying to explain to her the concept of frontloading work to save time in the long-run, and I'm hopeful this illustrated it. Anyways, this was just a little win that made me happy, so wanted to share! :)

I’m currently interviewing for data analyst roles (mostly entry level) and every take home task i get is the same. It’s a bunch of tables with masked data, and they all want actionable insights, except i feel there’s never enough information to actually find an actionable insight. you can rarely use domain knowledge as identifiable data is anonymised and they never provide enough historical data to see if differences really matter.

Do other people feel this way or have i gotten the wrong end of the stick, if so any advice would be appreciated.

EDIT: Thanks you for the advice in the comments, I think being curious, documenting my process for the analysis I can do and explaining what data I needed to delve deeper is the way to go.

I started at a new company recently. My task was to create a Power BI dashboard for the VP to find opportunities. After weeks of back-and-forth, the dashboard went live in February.

My manager said all was going well. And then today I got an email from the VP: “could export some actionable data to Excel?”

Maybe two or three years ago I lamented the fact I had never gone into software development in spite of the fact I probably had the coding mindset for it, regretting the tedious and stressful aspects of Analytics as well as lower overall pay.

Now with AI leading to massive layoffs and / or reduce hiring in software development and other Engineering fields, I'm thinking Analytics was a good field to specialize in since it has that sweet spot of being just close enough to the business and just close enough to the tech side that it is hard to automate away via AI. Furthermore, I think demand for analysts in general to understand data and accommodate reporting changes will also increase if AI is accelerating software changes and changes to data models and systems.

There might be AI front ends replacing some dashboards, but by and large, this profession is safe from disruption I think.

There’s been a lot of talk about how the data analyst role is changing, especially with AI becoming part of the workflow.

But curious to know what that actually looks like in people’s day-to-day work.

If you work in analytics:

What tools do you find yourself opening every single day? Is it still mostly SQL + Excel + Power BI/Tableau, or has your stack changed?

Also, has AI actually become part of your regular workflow? For example, using it to write/debug SQL, analyse data, build reports, etc.

And are you finding yourself working more with things like cloud platforms, dbt, data pipelines, Git, etc.?

Basically, what does a normal day of analytics work look like for you in 2026?

Hey y'all - I started as a freelance analyst in October 2017 after spending five years in the workforce. Periodically I stop in to answer questions in case anyone is thinking of pursuing this type of work/career. Happy to help anyone out in the comments.

About me/the company:

Graduated University with a computer science degree.

Worked five years as a business analyst in shared services for a multi conglomerate. Was fantastic exposure to industry but I was a soft skills BA (requirements and some PM work) and hated it. My mentor there introduced me to Tableau which I worked on for a year before leaving.

Went to government, hated it, start moonlighting as a freelance Tableau dev, got enough traction to quit job, and that's where this started.

Now most of my data is spent in a data engineering role, while other people on my team do Tableau and PowerBI development.

Hired first employee in 2021, second in 2023.

Clients are mostly coming from LinkedIn, referrals and via white labelled partnerships. But it's inconsistent, feast or famine type stuff. Still, it's worked well enough, we're alive and well.

Feel welcomed to ask me anything about the data industry or running the consultancy.

FIRSTLY I want to preface this by saying this is going to be a bit of a rant - a lot of what I'm saying has already been said on this sub but I just want to get some things off my chest.

Don't get me wrong, there are perks to being a DA, especially early career. Depending on how you play it you can have the flexibility to jump into various different career paths : data science, SWE, data engineer etc. I also love how it gives you opportunities to bridge the gap between stakeholders and tech.

I've been working at my current company for a year and a half and while I am grateful I have a job given the current job market there are a few things that are REALLY weighing on me a lot:

1. Be wary!! Data Analyst = Excel/Dashboard Jockey (sometimes)

So much of my workload is QAing spreadsheets**,** adhoc mindless data requests, updating PowerPoints and fixing dashboards. It's reached a point where sometimes I want to scream, leave my job and become a plumber or elevator technician (which apparently aren't bad options according to reddit).

I'm looking for other data analyst positions and I see a lot of other "senior" positions that are exactly the same responsibilities. I've learned to ALWAYS ask about the job requirements in detail and in person, so many data analyst positions are not what they seem.

2. It's easy to fall behind the technology curve

The staple DA tech stack would be SQL, Power BI and maybe some cloud. You may get some opportunities to branch out but I look at friends in DS or SWE who are constantly picking up new skills in ML/Model Building etc. and it feels like I'm falling behind.

A big part of this is that my company outsource a lot of technical work externally and I've found it really hard to get involved in anything more technical than building a Power BI dashboard. Beware of companies that outsource all/most of their technical work because you will have limited progression!!

3. People don't want data analysis, even when they need it

From my experience a lot of companies are not interested in seeing the bigger picture. They have their KPIs which they want to meet and they are only really interested in those metrics. You could build a state of the art model that gives a holistic view into your company's performance and models future strategies that will save millions but senior leadership will not be interested because it is not a dashboard visual of 5 to 10 KPIs that they need to meet by the end of the year. It's this tunnel vision that drives me crazy sometimes.

OK rant over. I realise a lot of these points are related to company culture and will vary from job to job, honestly I hope a lot of people have had a different experience working as a data analyst! If so please do share.

I work for an organisation that is spending so many hours thinking about how it can give all 4000 employees Power BI access to do what they want. As an analyst I'm getting worn down as everywhere I go people are asking me if they can just do the data themselves, someone even asked me if they could copy my data model today. That's with me providing really helpful reports, some with export functionality and I'm generally willing to help but my customer base is hundreds of people so I can't give everyone everything they need all the time but that's not unusual. In theory I love self serve but what I don't love is that idea that my job is so easy that any random employee can replicate it, I'm also worried that my job will become making models and dax measures for other people that don't understand it and then have to look as their ugly outputs. Management don't care at all, this is the pet project of a couple of engineers and I don't really know why. I'm wondering about my chances of finding somewhere less dysfunctional or are all analytical jobs going this way?

Sure, AI tools are helpful for data analysts as far as assisting with coding or helping to research code syntax. And I suppose a well-tested AI tool on top of a pristine data catalog can provide a chat-based tool for data research some may find easier than manually searching through documentation, but I think these use cases are where AI's usefulness to data analysis as a profession ends. Note I'm referring here also to Analytics not Data Science, which as a specialty concerns itself more with bread and butter descriptive reporting. Data Science is a different beast all together built on the foundation of Data Analytics.

Why do I say that AI cannot do the job of a Data Analyst? I say this because the actual front-end creation of data outputs and visualizations or analyses has always been the easy part of the profession that we figured out how to basically automate, simplify, and make self-service many years ago with myriad tools and frameworks (including chat frameworks). If you have a pristine, well-validated dataset that has dealt with the edge cases and business nuance, it's often trivial to "analyze" it or slice and dice it to answer business questions (or better yet, find the right questions to ask 😎).

The hard work of being a data analyst is exactly the part AI doesn't do well and maybe can never do well, the validation side and business context side. If we had an organization with truly pristine data and truly stable processes such that you could just hook the data warehouse into AI and replace the jobs of data analysts, you could have already done that before the invention of AI with self-service BI tools from the 2000s.

Now I won't deny AI's usefulness in advanced data-science-y contexts like tagging text and scenarios like that nor will I deny that AI can probably provide useful rough sketches or high-level explorations of data I suppose, but these are just tools added to the toolset of professionals. These hardly generate enough impact to replace data analytics jobs in the way that some are claiming AI will replace other technical jobs.

What do folks think? Agree or disagree or any other thoughts or experiences?

A lot of dashboards feel like the analytics version of sitting through a bad meeting. Lots of slides, charts, and numbers. Usually the the conversation turns off the main topic and the purpose becomes less clear. Everyone reviews the information, and someone says “we should dig into this more.”

The problem isn’t that companies don’t have enough data, dashboards, or insights. The hard part is knowing what actually matters, where to focus, and what action will have the biggest impact.

A dashboard that says sales are down or churn increased is useful but it leaves the hardest questions unanswered: Why did it happen? What changed? What should we do next?

How do you handle this. Are dashboards driving decisions at your company, or are they mostly used to show trends and answer questions that have no real decision behind them?

Hey, my job has literally turned into prompting all day. Wanted to know what you all are doing (if anything) to ensure your analytics skills don't atrophy?

It’s no secret that AI is coming for the white collar job market and fast. At my company, people are increasingly using ChatGPT to do what was once core job duties. It’s only a matter of time before the powers at be realise we can do more with fewer people with the assistance of technology. And I suspect this will result in a workforce reductions to improve profitability. This is just the way progress goes.

I have been thinking a lot about how this will affect my own role. I work in HR analytics. I use tools like Excel, SQL, R, and PowerBI to help leadership unlock insights into employee behavior and trends that drive decision making for the company. Nowadays I rarely write code or build dashboards without using ChatGPT to some extent. I frequently use it to get ideas on how to fix errors and display visuals in interesting way. I use it to clean up my talking points and organise my thoughts when talking to stakeholders.

But how long can people in my role do this before this technology makes us useless?

For now, I will focus less on upskilling on tools and more on understanding my customers and their needs and delivering on that. But what happens when EVERYONE can be a data analyst? What happens when they use something like CoPilot to identify trends and spot anomalies and craft compelling stories? 5 years ago, I was focused on leaning new tools and staying up with the latest technology. Now I question if that’s a good use of time. Why learn a new tool that will be obsolete in a few years?

Between offshoring and AI I am worried I will become obsolete and no longer have a career. I’m not sure how to keep up.

Appreciate your thoughts. Proud to say this post was not written using any AI. :)

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?

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

I've observed that in some orgs a lot of people see a status hierarchy where “data scientist” sounds more technical or advanced than “analyst,” so analysts sometimes get underestimated. But I believe, the roles often overlap, and in most cases an analyst generates better ROI than "scientists" because lots of time business problems are just analytics problems. The line between the two also are quite blurry now with AI tools being generally available... I'm not sure where this attitude usually comes from? Is it company culture? Or is it more of the Linkedin-driven job-title snobbery than from the work itself? How common is it in general? How do you deal with situations where you, as an analytics professional, are looked/spoken down on just because of a job title?

Looking back at some older discussions about data, it’s interesting how much the field has changed, not necessarily in the fundamentals, but in the kind of work and tools people are dealing with day-to-day.

Things like working with unstructured data, AI-assisted coding, data pipelines, automation, and production systems are now part of the conversation in a way they weren't for a lot of people when they started.

So for people who’ve actually been working in data for a while:

What feels most different to you now compared with when you started?

• What skill has become much more important than you expected?
• What used to take you hours that now takes minutes?
• What part of the work has barely changed at all?
• And is there anything you thought would be important when you started that turned out not to matter as much?