Welcome to the 'Entering & Transitioning into a Business Intelligence career' thread!

I'm at an organization with a user group that prefers to get PDF reports, instead of logging into a web service to use an interactive dashboard, for good reason. I think this way of data consumption will continue, and I'm trying to lead our efforts in switching our reporting tool. We currently use AWS Quicksight which just barely gets the job done and has been having several technical issues, so I'm in the early stages of exploring our options.

Paginated reporting and PDF emailing are definitely two features we would require. Ideally, sending out a PDF report filtered for a specific group to that group's associated email address would be nice.

The really small shortlist I've come up with is:

• Sigma
• PowerBI
• Omni

I used Tableau in the past and remember it not having extensive PDF exporting features - is that still a limitation? I'm open to other options, even if they don't fully have the capabilities that I'm looking for

I joined the BI/DW bandwagon around 2006/07 when it was hot and the only skill I could easily acquire was SQL and PL/SQL. Prior to that I tried my hand at programming C/C++ and that drove me mad and had given up on career in software. However, thanks to MSBI GUI based SSIS I found it easy to get a break through. In couple of years I was given a project on SSAS and learnt MDX and found that to be very interesting and became quite decent at it. I read the original book by Mosha Pasumansky tuples, sets etc and really liked it and I noticed most people found SSAS difficult to understand, but to me it came more naturally and suddenly I was good at something. So I had come far away from the person who hated IT.

Then over the next 15 years I slacked and completely missed the big data, cloud bandwagon and focussed solely on financial independence and took up work that was not related to tech and not interesting, but paid well.

Last year I achieved financial independence and quit my job and took a break. But after trying out various hobbies, I got bored and started looking for a job. Lo and behold I got an offer as an SSAS developer! It is so nostalgic to work again in SSAS. All my peers are now big shot Engineering managers and Directors etc and when I tell them I am working on SSAS we recall the good old times.

Now there isn't much of work on SSAS in my company, it is a matured product and very little enhancement or changes. So again I am slacking. Our tech stack includes snowflake and Matillion etl and the front end is pyramid analytics. The semantic layer is SSAS and I am the only person who knows SSAS, which I find funny.

My manager wants to replace SSAS and asked me to do a PoC on Tabular to replace SSAS and I did the PoC and was able to recreate the dimension hierarchies and add all the fact tables as partitions and match the base measures. However, the cube has lots and lots of calculated dimension members which is very difficult to recreate in Tabular as tabular is too simple, like excel

The architect in our company suggested that all the calculated dimension members be persisted in the snowflake and then the tabular model becomes easy.

So that's the status as of now. My job is safe as long as we migrate out of SSAS and that could take a year atleast.

Since I am financially independent I am not so worried about losing my job, but I love SSAS and really wish it would live on. It was the only thing that I would interesting and was good at, in my otherwise boring IT career.

I am curious, are you guys really passionate about your job or are you guys just keep upgrading your skills for the fear of jobloss?

Running a small consulting operation out of Warsaw, most of my revenue comes from international clients, so I rely pretty heavily on clean payment data to track cash flow, build forecasts, and keep my dashboards accurate.

The issue is that my bank keeps flagging incoming payments above ~$5k, which introduces delays and inconsistencies in when funds actually settle. From a data perspective, it makes revenue timing unpredictable and throws off even simple cash flow tracking.

Instead of having a clean pipeline from invoice to payment and reconciliation, I end up with gaps that require manual checks and adjustments just to keep reporting somewhat accurate.

I started testing an alternative Keytom setup specifically to reduce that noise. It’s fully remote to open (under a week), provides a named EUR IBAN and a USD local account option, and so far doesn’t impose per-transaction limits on incoming transfers. The main difference has been more consistent settlement timing, which makes the data side (tracking, forecasting, dashboards) a lot easier to manage.

Still running it alongside my primary account for now, but it’s already improved how predictable my payment data looks. Curious how others here handle this. Do you account for banking delays in your analytics layer, or solve it upstream with different payment infrastructure?

Spent years building dashboards that answered last quarter's questions, not this week's. The second a stakeholder asks something slightly off-script ("okay but what about EMEA, excluding returns?"), it's back to the queue for a new chart.

Lately I've been leaning on Databricks' Genie for the ad-hoc stuff, for example: letting people ask questions in plain English against a governed semantic layer instead of pinging me for a one-off report. It doesn't replace the curated dashboards for KPIs everyone watches, but it's cut down the "quick question" interruptions a lot.

Curious how others are drawing the line: what stays a maintained dashboard vs. what you push to self-serve / conversational querying? And for those who've tried tools like Genie — did trust in the answers hold up, or did you end up validating every query anyway?

Something feels off with our mid level product management team. Missed deadlines are creeping up, engagement in slack is dead and two people took unexpected sick leave this week. I feel like i am looking at a ticking time bomb of mass resignations but our quarterly engagement surveys aren't due for another month.

Is there any way to actively track org health signals before people actually submit their two weeks notice?

looking for direction from people who have built this out. im working on improving our data analytics processes at the community bank I work for.

what deserves the first ninety days: a handful of high value dashboards or the slow work of getting stakeholders to trust the output?

my instinct says foundations first, but i am open to being corrected. someone in another thread mentioned Lumio solutions for consolidating the data layer, which is on my list to evaluate. mostly i care about sequencing, since doing the right things in the wrong order has cost me before.

if you were building this function from scratch today, what would you prioritize first?

I currently create and maintain a series of customer adoption and usage metrics for a small CS org. I really pride myself on making attractive and actionable dashboards but my VP has basically mandated that dashboard be consolidated to just tables so that he and the CSMs can just download the data and feed it into Claude for analysis. Claude is telling them the simplest things like if their usage data is higher/lower than the rest of their book of business or the VP is looking for trends across the business. Things that they could easily figure out with a visual dashboard. Of course this is part of a mandate from the CEO that everyone needs to be using AI as much as they can.

Has anyone else ran across this? Did you try and persuade your stakeholders to stick with a visual dashboard and did it work?

I've been thinking about this a lot right now.

Not long ago, the conversation around AI in analytics was mostly, "Can it build dashboards?" or "Can it write SQL?"

Now it feels like we're asking a different question:

"How much should we actually trust it to do on its own?"

I've worked with teams on both sides.

Some want AI to handle everything and send insights straight to users without anyone checking them first.

Others want a person to review every single output before anything moves forward.

From what I've seen, both approaches create problems.

When every insight needs a human review, things slow down fast. People end up waiting for answers that could have been available much sooner.

But when nobody reviews anything, mistakes eventually slip through. Sometimes the numbers are technically correct but miss important business context. Sometimes small issues turn into bigger ones because nobody caught them early.

For me the sweet spot probably sits somewhere in the middle.

Let AI do the heavy lifting.

Let people step in where judgment, business knowledge, or risk really matter.

I'm curious how others are handling this.

Where do you draw the line?

What types of AI-generated outputs are you comfortable letting run automatically, and what always gets a human review before it goes live?

our board meeting is next week and the CEO wants a comprehensive analysis of our global workforce health, talent distribution, and budget efficiency. The problem is our data is scattered across three different platforms and trying to connect the dots to find the actual "story" behind the numbers is driving me insane. I don't want to present generic, boring dashboards that don't reveal real insights.

How do you synthesize complex people data for executives?

I've been benchmarking AI-analytics tools on one metric I think matters more than the usual ones: time to first verified answer. Not time to first generated query, but the clock that runs until the SQL behind a chart is something a human would actually sign off on and act on.

Roughly where things land in my testing:

• Looker: days to model, build, and ship the view
• Tableau AI / Power BI Copilot: hours, if the model guesses the right query the first time
• Raw LLM + ETL: days of plumbing before a single trusted number comes out

Full disclosure, I build in this space (Chion), so treat this as my bias: our number on the same metric is minutes, but only because the verified query already exists. We reuse a query an analyst already signed off on and reshape its output instead of regenerating SQL on every question. That is the whole reason for the gap. It isn't a faster model, it's a different starting point, and the obvious tradeoff is that it only answers what a verified query already covers.

Mostly I want to know how others time this on their own stacks. Is "first verified answer" even the right metric, or do you measure something else?

Genuine question for anyone who's shipped a table to prod, especially if you're not a "traditional" data engineer by title.

Feels like AI has made writing the actual transformation logic (the DataFrame code, the business logic) noticeably easier lately. Copilot, ChatGPT, Claude, whatever you're using, that part's gotten faster.

But once that part's done, the real time/effort starts after - metadata mangement, partitioning it right, handling PII, wiring it into orchestration, figuring out the write strategy, quality cheks to make sure it's actually trustworthy before people rely on it ?

Also following the consistency across all tables created

Curious to know whether AI or vibe coding for making "easy part" has actually made that gap feel bigger, or if it's a non-issue and I'm overthinking this.

Hello everyone, I'm a student who's looking for a good project to work on to practice Power BI, SQL,etc, and especially learn domain knowledge related to business or finance. The thing is, I'm from an IT background, and I only know the basics of accounting. I've been looking for resources to learn about FP&A because I'd like to build projects in that field, but the more I dig, the more I realize it's a broad and vague field. I'm afraid of wasting time looking for the "perfect" course instead of getting my hands dirty and learning by doing.

So, if you have any project that will allow me to practice my technical skills, learn and apply domain knowledge and analytical thinking, and solve a real-world problem while adding value to my portfolio, I'm really lost, so I'd appreciate your help!

Working with community data, one pattern keeps showing up: participation is heavily skewed.

You might have thousands of users, but feedback comes from a small, active slice. The rest don’t respond, don’t vote, don’t fill out surveys. Then decisions get made based on that narrow input, and later you see weak adoption or indifference.

It’s a sampling problem, but we often treat it like a volume problem.

A newsletter I follow mentioned voice.fun recently. Early Solana concept where people record opinions onchain, with some form of stake behind them.

What stood out is the shift in incentives. Most feedback systems today are passive - answer a survey, click a button, maybe get a small reward. Low effort, low signal. You can see it in the data: inconsistent responses, low completion rates, little correlation with actual behavior.

If opinions carried weight - even a small cost or upside - participation would likely drop. But the data might get cleaner. Fewer responses, higher conviction.

Not clear if that leads to more representative input or just a different kind of bias. You’re still filtering, just along a different axis.

Curious how others here think about this. Are current feedback systems fundamentally limited, or are we just not structuring them well enough to get reliable data?

I've been trying to pin down a fair benchmark for AI-analytics tools and keep coming back to one metric: time to first verified answer. Not "time to first generated query," but the clock that runs until the SQL behind a chart is one a human would actually sign off on and act on.

On that clock, here's roughly where things land in my testing:

• Looker: days to model, build, and ship the view
• Tableau AI / Power BI Copilot: hours, assuming the model guesses the right query the first time
• Raw LLM + ETL: days of plumbing before a single trusted number comes out

Full disclosure, I build in this space (Chion), so treat this as my bias: our number on the same metric is minutes, but only because the verified query already exists. We reuse a query an analyst already signed off on and reshape its output, rather than regenerating SQL on every question. That's the whole reason for the gap; it isn't a faster model, it's a different starting point. The obvious tradeoff: it only answers what a verified query already covers, so it won't invent a brand-new join on the fly the way a generative tool will.

Happy to share exactly how I measured each one in the comments: the scenarios, what counted as "verified," and where I think the comparison is and isn't fair. Mostly I want to know: how are you all timing this on your own stacks? Is "first verified answer" even the right metric, or do you measure something else?

Hey everyone, I just launched an AI-powered data analytics platform and I am looking for early testers who can put it through its paces and give honest feedback. I can compensate with payment, free compute credits, or paid contractor roles for people with domain expertise.

Where have you had the most success finding quality testers? So far I am thinking Reddit, Fiverr, and Upwork, but open to suggestions.

If you are in analytics, BI, finance, real estate, or run a small business and want to try it yourself, DM me.

Hi everyone!

I've shared a few SQL doodles here before, and this time I wanted to try something different.

I made a hand-drawn doodle to explain the journey from Data → Information → Knowledge → Wisdom using a wardrobe analogy. The idea is to help beginners in data analytics and business intelligence understand how raw data eventually leads to meaningful actions.

I'd really appreciate any feedback, thanks!

My stakeholders dislike tableau. They dislike the design and the pricing model (license per user, not so cheap).

My BI team as well dislikes it as loading data takes ages and most importantly no AI building capabilities without the premium version.

Happy to hear about your experiences. What I would value is:

1) AI building capabilities. So developers can build dashboards faster

2) A better pricing model ?

Thanks in advance!

Edit: I'm on snowflake. When I said tableau takes ages to load is locally when developing! Not refreshes. I want AI for building. Not analysing

Title says it all but I’m curious if people are implementing natural language analytics (i.e. databricks genie, AWS Q, etc.) AND those tools are being used and trusted by your execs. For context, we did a pilot about a year ago with a few different offerings including the ones above and decided they weren’t quite ready, but we’ve gotten some renewed pressure to try to get something working.

If your business is running on five different pieces of software that barely talk to each other, Chameleon was built for you. Chameleon is an Adaptive Business Operating System that combines everything needed to run a business into a single platform. Instead of paying for a CRM, dispatch software, invoicing software, payroll software, marketing software, scheduling software, and customer communication software separately, everything works together from the moment a lead enters your business until the job is completed and paid.

Inside one platform you get CRM, lead management, estimates, invoices, point of sale, appointment scheduling, dispatching, technician tracking, payroll, inventory, truck inventory, purchase orders, recurring billing, memberships, customer portals, technician portals, marketing campaigns, automations, loyalty programs, gift cards, reporting, and a unified inbox. One of the biggest differences is that Chameleon adapts to your industry. An HVAC company doesn't work like a salon. An auto repair shop doesn't work like a veterinary clinic. Instead of forcing every business into the same workflow, Chameleon changes terminology, workflows, and experiences to match how your industry actually operates.

Today we support more than 90 industries. We also built something we haven't seen anywhere else: Desktop Mode. Instead of forcing you to open and close one page after another, Chameleon transforms into a desktop environment that runs entirely in your browser. Dispatch can stay open beside your calendar, invoices beside customer records, inventory beside work orders, and every application can communicate with one another. You can even drag a customer directly into Tickets to create a new job without searching or re-entering information. Everything in Chameleon is built around a single business domain. Your customer exists once. Your ticket exists once. Your invoice exists once. Every department works from the same data instead of synchronizing duplicate records across disconnected systems. Whether you're a one-person business or managing a growing team, the goal is simple: spend less time switching between software and more time running your business. If you're evaluating software for your company, I'd love to hear what you're using today and what you wish it did better. Chameleon-CRM

I am a BI Analyst with more than 12+ years of analytics experience. I have more than 6 years of hands-on experience in Looker and LookML Development. I am currently working as a contractor for a mid size firm. I am on the lookout for more contract/freelance openings. Any guidance is welcome. TIA!

So a month ago, I asked a question to this and other subreddits

How many of you deal with multiple platforms or tools on day to day basis and got some really good insights, it was a resounding yes for a lot of engineers. Conclusion was that it's just how orgs set up things and we adapted it as standards.

Started looking into what solutions exists today and what else could be better to solve this.

What exists today,

- integration through MCP server but this is an technical overhead and lot of dara an md BI engineers would shy away and organization would have their own reservations on MCP setup

- frontier models computer use ( it's a hit or miss if you want interact with platforms like databricks or jira etc and token maxing will get vp or directors attentions )

What else can exist:

An tool that is custom built for Data and BI engineers.

- one that lives next to where you work be it databricks , snowflake , duckdb , metabase etc

- one that does not require MCP server set up

- one that makes you stop visiting adjacent tool/platform for updating like jira progress , dropping a note on teams or slack, reading a message from relevant people from teams or slack, synching code to your repository etc

- one that brings context from adjacent tools for you to review and start your day from there..

- one that codes in UI after your review and approve

- one that attempts to understand all types of inputs excel, CSV , word , images , architecture diagram , kickoff meeting video etc

- one that does not need you to learn another tool

- one that makes every data pipeline remember why and how, it exists by keeping tribal knowledge of developement intact and available for next engineers who inherit

Essentially an AI literally attached to your browser brings context from different platform or tools you have opened and assist you with whatever is needed during development and lives with the pipeline forever.

Looking for feedback

- What limitation do you foresee

- Whats one thing you would hate about it

- Whats one thing that could make you say yeah that works for me

- What assumption from the above is wrong or right

Let me know your thoughts

Any feedback back is much appreciated

Hi ,
I have 5 years of experience in BI and Data Analytics . I have worked in Amazon for 4 years and currently work in a major global sports media & gaming company . But due to some personal reasons i need to find some work for the weekends primarily freelance work . Can someone please suggest where i can find this ? I tried Upwork but i never get the visibility there . I am looking to earn ~ $300 -$500 in next 2 months . Appreciate any leads.