Hey again. Posted a few months ago about ditching traditional dashboards for direct AI-to-warehouse queries.

After playing around more, I realized many are completely skipping the biggest bottleneck: data confidentiality and real enterprise security.

It's easy to hook an LLM up to personal sheets or low-risk data when you have nothing to hide. But try telling a regional enterprise or a strict cooperative, especially back in my country, Argentina, where a lot of traditional business owners are absolute dinosaurs clueless about tech and AI, to pipe their entire SQL database, filled with proprietary financials, vendor contracts, and sensitive operations into an external LLM. Companies lock down their systems for a reason. Microsoft stacks (like Power BI + Azure) give you enterprise-grade encryption and strict data governance where you actually own your perimeter.

Plus, there's the cost factor. As databases scale into millions of records, heavy ad-hoc natural language querying gets insanely expensive token-wise compared to cached, optimized semantic models.

So, how are you guys handling this? Are companies actually trusting LLMs with raw production databases yet, or is the "AI does everything" approach hitting a massive privacy brick wall in the real world? Would love to hear your thoughts.

you can skip to 2:00 to see the results, But this was made using single prompt. Things like this used to be made in hours, now in minutes

Edit- i am using this method from last 5 months, working on PBIP template (the initital github link provided in prompt), yes it is ready for production business data. First, you use Python to do EDA of the DB and feed the business/data context; next, focus on building a Power BI dashboard. I am making a hour long tutorial on this using production sales data replica, Coming soon

So it feels like the subreddit is literally on fire this week.

I've written about this many times before, but the way I see it the core purpose of my job is to help businesses solve problems with data.

No doubt, the tools are changing and that is going to scare people. [u/SQLGene](u/SQLGene) wrote about how we should be mourning role of the Power BI Developer.

I think everyone needs to chill and ride this thing out. With new tools there are new opportunities. The future may seem uncertain but I'm sure about one thing: Businesses will always need people who can help them make better decisions.

Stay strong, folks. Keep learning, stay curious, but most of all RELAX. Everything's gonna be fiiiine.

We know there's a lot of concern about Claude Dashboards as a potential competitor, so we did a quick review.

Here are the key takeaways:

• You can create Dashboards (in beta) in Claude Desktop or claude.ai: These are live artifacts that can query data sources via Claude connectors with MCP servers. They can use extracts or live queries that can stay up-to-date, and you can share the artifact dashboards with other people.
• Solid data foundations are more essential now: Claude Dashboards are a new front-end option available to you. The quality of your semantic models and how they are governed decide how fast you can adopt new front-ends.
• A remote MCP server is required for a live connection: Currently, Microsoft's Fabric IQ MCP server doesn't work for this use-case, but its Power BI MCP server for querying models (preview) does, as a custom connector.
• Claude Dashboards are different from Fabric Apps: Claude Dashboards provide a simpler, more constrained experience which focuses more on analyzing and visualizing data. While Claude Dashboards might be a simple, lean option for individuals or small teams (i.e. personal BI scenarios) there are clear and present challenges for data governance and compliance that likely make this a difficult option to consider for enterprise scenarios.

Job insecurity, accepting the abilities of AI outperforming them, insecurity, denial, Deliberate misinformation to cope, FOMO, and more

Edit- Don't want to sound mean, but I can understand many of you are scared, some of you are in delulu, and some still have not understood the true capabilities of AI in Power BI, but the hard reality is many Power BI jobs will be diminished in the future. It will be very hard for average devs to survive this market; previously, even non-tech people used to make fortunes in this field, but in the near future the scenario will be a lot different,

You need to learn and gain expertise in multiple stacks; for example, a full-stack dev is a combo of frontend + backend. Even in BI, the next thing would be a Full data stack that includes Power BI + Data engineering + CI/CD

Dragging and dropping visuals and creating SUM and SUMX measures. no-code/low-code days are gone. Upgrade yourself or get behind the rat race

https://www.reddit.com/r/PowerBI/comments/1x369hu/summary_of_recent_events/

As someone who is not up to date with recent events, what is happening? I'm a bit confused how this AI slop is being upvoted. Is it in reference to Claude Code one-shotting a dashboard?

As someone who hasn't really tried out Claude Code, how good is it actually? Also is the conversation mainly about Claude Code dashboards, or is it PowerBI dashboards built with Claude Code?

I find AI super helpful when it comes to creating DAX formulas, learning something, or giving a guide on how to do xyz, whether that is clicking in an interface, or writing DAX/M formulas.

In my experience: The business analyst isn't going anywhere. One-shotting a dashboard on open clean data is one thing, but it's a completely different thing to do that inside a messy company where the true business logic lives in people's heads and unwritten rules.

Not to mention there's often times multiple ways to arrive to an answer and by that I don't mean multiple ways to arrive at the same conclusion/number, but different numbers, depending on how you define the pull. ie you could ask a basic question, and unless its highly specified it will return one out of 5 possible numbers. My experience is that most management don't understand this.

Not to mention privacy hell. Sure; once Microsoft gets some of these capabilities in PowerBI we'll be more in trouble, but in my experience most people are hyper critical to Claude/ChatGPT. Claude sends your data to the US, so if you're in EU that's a no-go immediately. You've probably often heard that you shouldn't write sensitive information into Claude/ChatGPT. Why is that? Do you frequently do that yourself? Most of these CEO's don't trust these companies, they only trust Microsoft. If you head over to r/ClaudeAI literally noone exposes Claude Code directly with customer/sensitive data, it's mostly SWE's without access to the customer data, or running Claude Code in a docker container.

Created this project to understand those question stakeholders want you to answer.. Feedback would be appreciative.

Github here if you want to learn more about the project.

https://github.com/WatchTheory/us-defense-investment

I work in management accounting within the leasing industry, and I'm looking for ways to improve my workflow. A former colleague of mine showed me that Power BI allows users to automate report generation and provides a user interface to select views, filters, and thresholds. He demonstrated an example that could be printed out and interacted with in a browser.

Currently, I create data-heavy Excel sheets that pull information from the IBM Slice server. My monthly workflows are relatively straightforward; I change the month, refresh the data, and adjust some table styling. I also write comments directly in the Excel sheets and present my findings by sharing my screen. I've started using modern formulas like FILTER and XLOOKUP, but there's still some work involved each month. When I need to create custom or detailed views, I often have to build them from scratch, copying data into tables or pivot tables.

For automation, I've created some macros and templates to streamline data processing. Overall, I feel quite content with my setup.

I tried Power Query twice and found it unhelpful; it was less effective than coding in VBA or Python. Power Query seems just like another version of Excel with a clunky UI. And it's without useful data connections, so there is no use case for me. Unfortunately, Python is not an option for me due to maintenance concerns: I'm the only one who can resolve issues, and it's quite labour-intensive to develop.

Can Power BI help me, and in what areas? I have a feeling that the need for such a tool may not even be necessary for my work. I love beautiful tables, would love a chart, but do not want to surgically make them every month. Would love to have a PDF report to send out every month. My Excel print looks very average.

Any opinion/example is welcome. Thanks,

Based on the announcements from FabCon, we have updated our Fabric Apps explainer. Below is our key takeaways.

• Fabric natively supports visualization libraries: You can create and share visuals built with Vega, Vega-Lite, and D3.js using Fabric Apps.
• Fabric Apps are a new workspace item: Fabric Apps let you build web apps, programs that live in the browser, such as dashboards or apps to manage data and services.
• Data apps query Fabric data sources: A data app connects to and queries a semantic model with DAX, like a Power BI report, so you can build interactive visualizations from code.
• A data app is code: A data app consists of HTML, TypeScript, and DAX files. You can write them yourself, but the expected path is that AI does most of the work, either a coding agent or the Modern Report Builder that Microsoft announced for Power BI Desktop.
• Agentic development makes them easy: Data apps give full flexibility over how data is presented and are quick to build with coding agents, but introduce new governance, consistency, and AI-dependence challenges. They also lack the baseline that every report gets for free, like the same interactions between visuals and the same accessibility features in every report.
• They raise questions for Power BI's future: Microsoft presents them as the next generation of report development, and is bringing apps to Power BI Pro and Premium Per User customers. Power BI reports are still more mature and consistent, but data apps are more flexible and faster to build, so they'll play a growing role as AI advances.

Hi all,

I’m an independent BI consultant working with Microsoft Fabric and Power BI. For several clients I’ve built the full setup: data pipelines, notebooks, a lakehouse and Power BI semantic models on top.

The next step I’d like to take is letting clients chat with their own data. They would ask questions in plain language and get answers and simple analyses back, without me building a new report every time.

I’m trying to get a clear picture of what actually works in practice:

1. Which options are out there? Fabric Data Agents, Copilot in Power BI, Copilot Studio, Azure AI Foundry, or something else? Which one are you using with clients, and why that one over the others?
2. How is it set up technically? What does the structure look like: does the agent sit on top of the semantic model, the lakehouse, or both? How do you prepare the data for it (dedicated views or tables, descriptions, instructions)? And how do clients access it: through Teams, a Power BI report, or a separate app?
3. Data sensitivity: this is clients’ business data. How do you handle security, where the data is processed, and what do you tell clients about it?
4. Adoption and costs: do users keep using it after the first few weeks? And what does it roughly cost in capacity and licenses per client?

Experiences that didn’t work out are just as welcome as success stories.

Look forward to your answer guys!

Flaky-ice

In RLS-enabled reports, we can use “Test as role” to check data access. Is there a similar way to test a specific user's access in a non-RLS report?

Most permissions in our environment are assigned through security groups. Instead of manually checking group memberships and permissions, can we simulate or test the report as a specific user?

Is there any built-in feature or alternative approach?

Most agent projects stall for a reason that has nothing to do with the model: there is no shared, governed definition of what “customer,” “order” or “incident” means across systems, and no way to trace an answer back to its source. You probably already have that business logic sitting in your semantic models.

There’s a workshop on Oct 31 that uses those semantic models as the starting point for an ontology and graph in Microsoft Fabric IQ, then puts a chat agent on top of it. Answers come back explainable and traceable, row and column level security is respected through Purview, and business users can see where an answer came from, which is what earns their trust.

It is built for data and analytics professionals as much as for AI engineers, and no advanced machine learning background is needed. Led by Brian Bønk, Data Platform MVP.

Full details and the agenda are here.

I have been thinking about how much time we spend on repetitive tasks in Power bi

It could be validating reports, checking measures, comparing report versions, updating documentation, testing filters, or anything else...

What is one task in your daily workflow that you wish could be automated? And have you tried any tools or scripts to solve it, or are you still doing it manually...

I guess doom posting sells and gets more engagement but it can’t be good for peoples mental health.

I browse this sub to get ideas and see others work/progress. Not to feel uneasy about my career.

Ai will change the data analytics landscape but we will adapt accordingly. The posters who make it seem like it’s the end for everyone are doing us all a disservice.

At FabCon Barcelona I showed this Fabric App. I decided not to focus on “look what the tech can do” but on “where does the work around a Power BI report actually happen?”

So I made up Moll, a wholesaler with a Power BI report that everybody likes. However, to take action, a lot happens outside of the Power BI report. The buyer exports to Excel to order. The warehouse counts stock in a different tool and the budget gap gets explained in yet another Excel file (that nobody can find and that has 4 versions).

In the clip you see a few of those moments in the app: the buyer reorders everything from one supplier at once, the warehouse enters a count on a phone (and the buyer sees it directly), orders get confirmed, a budget gets corrected, and every change ends up in a log (that you can export to Excel, with all kinds of filters..)

As a Power BI developer (I love Power BI), I’m not trying to replace Power BI. The Power BI report is still where I would analyse and want to see the insights. The Fabric App is for the moments people need to do something with those numbers, like ordering, counting, approving or explaining. And it runs on the same semantic model, so the same measures and the same numbers. (I asked the agent to reuse the existing measures and only add new ones if really needed.)

There were some great creative demos at FabCon ( u/PowerBIPark Lego app which he also posted on reddit), so I wanted to add a more everyday business example.

Full walkthrough on YouTube.

I am very happy that I got this opportunity, which also made me explore even more of what’s possible with Fabric Apps.

I am curious what you would use this for!

Hello. any powerbi discord servers still active ? cant find any and the others are behind a paywall. thank you.
im currently *new* in some way. i know how it works etc but i have never had a chance to have my hands on real project and would love to have a community where i can ask stupid questions without being judged.
thank you

Hi everyone,

I’m currently working as a Data Analyst using Power BI. I have strong Excel skills and an intermediate level of Power BI knowledge, but I’ve realised that I don’t enjoy the analytical side of my role, especially interpreting results and presenting insights to stakeholders.

I enjoy working with data and creating reports, but I’d like to move into a more senior, specialised role that focuses on reporting rather than administration or analysis.

What job titles would you recommend I look into? Has anyone made a similar career move, and what roles would you suggest?

Thanks in advance!

Hi team,

With AI tools improving so quickly, I’d like us to look at where they could save time in our reporting work with Power BI and Fabric.

Do you use AI for the following topics and if so how?

• Documentation: measure and table descriptions, data dictionaries, process docs
• Bulletproofing processes: reviewing logic, edge cases, test and reconciliation checks
• First mockups: quick layout and KPI sketches to align with stakeholders
• End-user training: guides, FAQs, “how to read this report” explainers
• Also: DAX/Power Query support, SQL and notebook code in Fabric, requirements from meeting notes, automated commentary on reports

Which tools are you already using?

With new AI software being able to create dashboards etc, are Power BI developer jobs now at risk?

Hi All,

I’m an Analytics Manager building out a technical assessment and development framework for my team. The goal is to give clear, objective feedback to my analysts and map out transparent growth paths and moving away from generic "proficient in Power BI" descriptions and getting specific on what separates a Junior (L1), Mid-Level (L2), and Senior/Expert (L3) developer.
I’d love to get feedback from other managers, leads, or senior devs on how you benchmark skills, assess real technical depth, and deliver feedback without micro-managing.

Thanks

Hi, a junior pbi dev here who wants to pursue a mid to senior role. Just wanted to ask what’s next after pbi? Was planning to follow a career plan but thinking now that I’m must have changed since AI is taking over so much of things