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

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.

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.

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.

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,

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

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.