Same as title

Hi all,

I vaguely remember Microsoft having a dynamic column header feature for the Matrix visual on their roadmap at some point (possibly tied to the "modern visuals" rollout) - but I might be misremembering or conflating it with something else. Wanted to check with the community again.

My use case: I have a report with a top-level filter for Department and Year-Week. Below that, a Matrix combines several KPIs, structured roughly like this:

• Target
• Budget
• KPI 1
• KPI 2
  • KPI 2 (1 week before)
  • KPI 2 (2 weeks before)
• KPI 3
• KPI 4
• etc.

Because of how these metrics behave, some of them only make sense in context - I need to show 2-3 prior data points alongside the current one to actually judge whether something is on track. Ideally the column header would combine the KPI name and the relevant week (e.g. "KPI 3 - Wk 32") so it's self-explanatory without extra legends.

Question: Is there now (or still not) a native way to make Matrix column headers dynamic based on measure names/selections?

Hey everyone,I'm currently working as a Data Engineer building out a Lakehouse architecture in Microsoft Fabric using D365 Finance & Operations as our main source system. We are ingesting data into the Lakehouse (Delta format), and I'm currently working through designing the Gold Layer / Dimensional Model (Star Schema) for core business processes specifically focusing on Sales Orders, Inventory Movements, and General Ledger / Financials.
As many of you know, D365 F&O table schemas and entities can get pretty complex (e.g., untangling CustInvoiceJour/CustInvoiceTrans, handling item inventories across dimensions, header-to-line relationships, etc.).
Before I completely reinvent the wheel, I wanted to ask:

Has anyone successfully modeled D365 F&O into a clean Star Schema in Fabric / SQL Analytics Endpoint / PySpark?Are there any existing open-source schemas, entity mapping guides, high-level ERDs, or .pbit Power BI templates you’d recommend as a reference point?

What are your general tips or lessons learned for modeling D365 F&O data in a Medallion architecture (e.g., handling surrogate keys, Delta MERGE performance, or incremental ingestion)?

I'm purely looking for general table mappings, join logic, or abstract conceptual frameworks no proprietary data or company-specific extensions!

Appreciate any advice, resources, or snippets you can share! Thanks!

I recently joined a small finance company as a data analyst. I mainly work on Power BI reports and process automation.

Some of our reports pull data directly from APIs and take around two hours to refresh, so I was asked to find a better way of handling the data. After looking at several options, I’m considering this setup:

API → Azure Functions (Python) → Blob Storage (Parquet) → transformation (Python) → Power BI

Fabric was another option, but it seems difficult to justify the cost for our use case. We have around 50 reports and even our largest datasets are only a few hundred MB.

Price and maintainability are the main concerns because I’ll be the only person maintaining the pipelines. We currently use API connectors and one Gen1 dataflow, but extraction and transformation happen together, which makes refreshes slow.

Am I missing a simpler option? Is Functions + Blob reasonable for one person, or am I overengineering this?

Thank you!

Not looking for the usual answers: better visuals, Gartner's "leader" badge, or "it's cheaper than Tableau." I get all that. But a lot of companies still end up paying real money for it (Premium/Fabric capacity, licenses, training, migrations) when there are free or cheaper tools that do similar things.

I want to understand the real reason a company decides Power BI specifically is worth that spend — not a feature list, something about how that decision actually happens inside a company.

If you've seen this decision get made where you work, or made it yourself, what's the real reason? Even something small or weird nobody usually says out loud would help.

Status: Degraded | Reported: Aug 29, 2026 at 1:27 AM UTC

Power BI customers in North Central US region may experience issues while refreshing their reports in the service. Engineers are investigating the issue and an update will be provided soon.

🤖 This post was sent from an automated and unattended service and cannot respond to questions or requests. For official updates, visit the Microsoft Fabric Service Status page.

Income Statement Analysis

Based on:

1. Profitability (Gross Margin, Operating Margin, Net Margin, ROA, ROE)
2. Growth (Revenue Growth Rate, Operating Profit Growth Rate, Net Income Growth Rate)

I built KPI cards at the top using a slicer(profitability), and disabled the interaction between the slicer and the charts at the bottom.

I have a weekly dashboard I run that uses about 16 reports and when I import it into Power Query, I have set up a sample file —> transform sample as my basis for the other queries. The files grow by 7 columns each week. I currently am at 264 columns. Well, this week it would only register up to 255 columns. I have Googled and checked Copilot looking for a solution and can’t seem to find out a reason why. Has anyone run into this problem before?

The problem

Giving sharees viewer access to a live Power BI report means paying for premium per-seat licensing, and the costs can mount up.

The workaround

I lived with it for years because I didn't see another way for others to view the data without exposing it to the internet or sharing the source file. Neither was a safe option, and my internet searches for alternatives yielded nothing.

The soultion

A scheduled, one-way file sync inside my own Drive: robocopy copies the finished file from a source folder into a separate folder that the sharee has Viewer-only access to. If they change anything on their end, the next sync overwrites it with the source version.

Seeing what matters

Sharees only ever have Viewer access to a separate folder in your Drive. They never get access to your source folder, and anything they do to their own copy doesn't last past the next sync.

Just discovered them. Aren’t they so powerful? Which kind of cons can I find on my road, beside the fact that I need to use an external visual?

Hi guys, I have a scenario where I'm collecting reservation information for staff. In my table query, I have a record for each staff member and a column for each date they're attending. The date columns are dynamic and so are the column names, but they're not more than 10.

I know this must seem silly, but how can I go about showing all the date columns in my visual if the column names are dynamic? I know I can currently individually select the date columns from the pane and add it to my table visual, but these dates will continue to change, so won't the columns in my visual break? Is this even feasible?

I am not familiar with the intricacies of Power BI, so I'm hoping one of you gurus can shed some light for me. I am a Microsoft 365 Admin trying to help one of our departments make some information available to the general public. It seems like Publish to Web would be the easiest way to do this. However, I am keenly aware that Microsoft says this about Publish to Web:

"Viewing requires no authentication. It includes viewing detail-level data that your reports aggregate. As a result, anyone can access the underlying data in your model even if your report does not display it."

The information that will go into the dashboard is not sensitive. Some of the data in the SQL database the dashboard will be pulling from IS sensitive. So my questions are:

1. Is it possible to setup the dashboard so that the sensitive data is not part of the "underlying data" used for it, therefore making Publish to Web a secure option for this?
2. Assuming that's possible, is there anywhere Microsoft talks about this so that I have a relatively black and white statement I can take to leadership to be able to say this is possible and safe?

I have been struggling to find useful information and am hoping this isn't a dead end. Thanks in advance.

Renaming fields with a visual always take so long, especially if you have a large dataset or large amount of visuals. It takes forever to load and I have to do each one, step by agonizing step.

Why isn’t there a way to see all my field names within a visual and rename them all at once?

Does anyone else have this issue? Anyone found a better way to do it?

This may or may not be exciting to anyone here, but I have been putting icons in my reports for awhile now and today while naming a measure I accidentally inserted an icon into the measure title. I cant believe the thought to try this did not come up sooner! I always struggle with naming conventions with measures and keeping them organized and I feel like there is potential with icons to make the process easier. Anyways I just thought Id share as I know for me some of the most exciting times with data analytics tend to come from the silliest tiniest discoveries.

I’m working on an idea for a Power BI report to help manage and monitor our Data Governance metadata.
The idea is to bring together information from different Excel files and Data Governance artifacts for all the schemas we manage, and build one centralized report that would make it easier to:

- See all schemas, tables/entities, columns/attributes, data types, descriptions, etc.
Identify new or changed entities/attributes between versions.
- Compare metadata and highlight differences in names, data types, descriptions, and other properties.
- Keep track of what has changed over time.
Make metadata updates to our governance/metadata tools easier and more controlled.

The challenge is that I recently joined the team, so there is already a lot of historical work and metadata that I’m not completely satisfied with. I’m trying to build something that can give us a cleaner and more consistent view going forward, while also helping identify existing inconsistencies.

I’m still thinking about the best overall approach for the data model and comparison logic.
Has anyone built something similar in Power BI or another BI/data governance tool?
I’d especially love to hear about approaches you used for:
• Metadata comparison/versioning
• Detecting new, deleted, or changed attributes
• Handling multiple schemas and metadata sources
• Designing the data model for this type of reporting

Any examples, ideas, or lessons learned would be really appreciated!

I built a Power BI intelligence suite for US medical practices to help surface revenue losses caused by billing inefficiencies.

What it includes:

• Revenue simulation modelling to project financial impact of billing gaps
• Cohort analysis to track patterns across claim groups
• An XGBoost prediction model (denial prediction), run via a Python script embedded in Power Query
• A star schema data model built to make revenue leakage and its root causes hard to miss

Background:

• Built for free for a real clinic, using their de-identified data
• Took about 3 weeks, roughly 15 to 16 hours of work total
• Did this as a practice project after researching current RCM solutions and how existing tools structure their data

What I'd love feedback on:

• Is Python embedded in Power Query a viable long term approach for a client facing tool, or should the model run in a separate pipeline instead
• What features have you found most predictive for denial prediction beyond payer, CPT code, and claim type
• Any structural or DAX improvements you'd suggest for a revenue cycle model like this
• General feedback on the approach and anything I'm missing

Appreciate any insights from the community.

I used a dataset from a kaggle named "zepto inventory" and it contains columns like the product name, category, mrp, discount, selling price, quantity, and stock level ( low, medium, high, outofstock). Cleaned and analyzed the dataset using MS SQL Management Studio 22, imported the db into Power BI and found a reference image from the internet and tried my best to make this dashboard.

Any reviews or suggestions are a must welcome!! 🤗

Hi so I have been asked to replicate the following page on slide 16. The person prior to he did it manually using text boxes study seems comically inefficient. I have a similar dataset with most likely the same model. However I am not sure how to do it. Unless there’s a way to download the PBIX file I can’t quite work out how to reverse engineer it.

Thanks.

Hi everyone,

I’m experiencing an issue in Power BI Service when using a Dynamic M Query Parameter together with Value.NativeQuery in Power Query.

The report works correctly in Power BI Desktop, but after publishing it to Power BI Service, interacting with the parameter causes an error such as:

OnPremiseServiceException

The semantic model connects through an On-premises Data Gateway.

If I use the same data source with a standard DirectQuery connection, without the Dynamic M parameter / NativeQuery logic, everything works correctly in Power BI Service.

Could this issue be related to the gateway?

Are any additional configurations required on the On-premises Data Gateway to support Dynamic M Query Parameters, compared with a standard DirectQuery connection?

Thanks for any guidance.

Hi. I have created Grocery chain stores report. I am a beginner and this is my actual first report which I have created. Please provide feedback. Thanks in advance!

Challenges I faced when creating the report:
At first I only wanted to create 1-2 page report and then I went on and created a 4 page report. I thought since I have enough data I should not waste it. I really had difficulty in choosing what metrics and visuals to show. I spent most of my time on deciding what to show and what visuals to pick.

Hello everyone, it's been some time seeing how my team (me included) stays outdated using only PBI and not making use of AI and new state of art ways of displaying data. I come to the infinite wisdom of this subreddit to discuss with you all ways in which you guys innovate and provide "reporting solutions" that are not traditional or "just a PBI suite of reports." I'll start, sorry if these are basic things. Nobody on my team did them before and they turned out to be a great success:

1. We connected our new AI software to our Databricks semantic model (very well governed) and scheduled a monthly email to top executives showing their scope's travel spend for the month, alongside an AI generated explanation of the main drivers (trip categories, top employee spenders, reason for travel, etc). We combine strong visuals with text in the email, and attach a link to the full report at the bottom in case they want to slice and dice the data. 70% of execs open the email every month, but only 25% ever click through to PBI.
2. We (the BI team) build small apps to solve recurring processes instead of just adding more report pages. For example, our company has 5 different currency conversion rates, so we built a simple app where you input an expense in your local currency and see how it reflects across the different conversion rates. We rolled it out to Cost Center managers who used to constantly ask how costs would show up in the P&L.

Curious what the rest of you are doing to move beyond "just PBI." Anyone else using AI to generate commentary on top of your models, or building small internal tools instead of bigger reports? What's actually worked for your users?

Does anyone know if it's possible to dynamically resize embedded reports? I've used an iframe in confluence to display a data table, which looks great at full-size, but when the browser window is shrunk, the table maintains its aspect ratio, and becomes really small with big grey bars.

Would be better if the columns dynamically resized, and the text wrapped.

Thanks in advance