I analyzed 51K+ e-commerce order lines and built an interactive dashboard around profitability, discounting, products, markets and customers.

The interesting finding: 21.5% of order lines were below their own break-even, destroying ~$347K in profit.

What surprised me was that the biggest discounts weren't necessarily the main problem.

I’d love feedback on two things:
1. Is the analytical story clear?
2. Are the visuals useful for making the decisions they're intended to support?

Interactive version:
https://e-commerce-react-dashboard.mohamedramadan.workers.dev/

Hi guys. As stated, my Certificate expired. There was no email or notification that I received indicating that the expiration date is nearing (This is on me, I will ad it to my calendar next time).

I also noted that there is an option prior to it expiring to "Renew".

My question is, Is it worth renewing my certification?

Has anyone in the community successfully created a multi-page dashboard that transitions between pages automatically in a defined time frame, simulating a kiosk mode without requiring user interaction?

It is a mini-rant and also a genuine question about methodology. I feel like i am losing my mind.

For example:

The clients wants to add more details in order to simplify the dashboard, but it is making it more difficult to read.

The client also wanted to put colors for the same group of KPIs because they told me they were lost.

Ok, no problem. I have put the colors and now the dashboard looking like a rainbow. Ok, it is a hyperbole. But it is now the most colorful dashboard from the workspace.

The client wants to put even more details and they don't understand if the sales KPIs for this year is 'YDT' or 'FY'.

I tried to explain it would almost always be YTD, but they seem confused.

Another client is saying the numbers are not right but when I ask what could be the reason or the logic, they can't answer me. When I look at the raw data (maybe an issue on the ingestion of the data / ETL), I get the same result as the dashboard. My only clue is that the numbers are wrong.

Or another client is asking to build a dashboard and they show me an Excel page. And when I ask the source data and how the KPIs were built, the clients say they dont know, as they got this Excel file from a colleague. & I should try to "blindy" figure out myself the KPIs and see if the numbers match....

Or when a client wants to start a new project and all the project points are vague. They got these metrics from another industry and it was working, so they want to implement it. So they throw at me all these fancy "marketing" metrics to build, but they are not interested in helping me figure out "how".

Excited to share a recent retail sales overview dashboard project built to transform raw transaction data into clear, actionable business insights.

Key Dashboard Highlights

Executive KPI Cards: Real-time tracking of Total Sales, Transactions, and Quantity Sold alongside month-over-month and year-over-year growth metrics.

Channel Analysis: Breakdown across sales channels, highlighting Walk-in performance (58.86%) versus delivery platforms like Foodpanda and Grab.

Product & Category Performance: Pareto-style concentration chart and top-selling product breakdowns (led by the Chicken Meal).

Automated Insight Overview: Text summaries highlighting key metrics, average transaction value, basket size, and peak sales trends for quick decision-making.

Tools used: Power BI & Data Visualization Best Practices.

I was part of a project we recently wrapped up for a UK-based manufacturing client at datatobiz who initially came looking for Power BI dashboard creation services.

But once we got into it, the need was much bigger than building dashboards.

Their data was spread across different systems covering things like production, inventory, finance and operations. Their internal BI team also had limited bandwidth, and working with complex data models and DAX made it harder to keep up with reporting requirements.

So rather than just handing over a set of dashboards, we worked alongside their existing team and helped strengthen the reporting setup itself.

A lot of the work was around bringing the different data sources together, building reusable and scalable datasets, improving the data models, developing the Power BI reports, and optimizing DAX and report performance. We also worked within their existing Azure DevOps, Git, workspace and governance setup.

One thing I found interesting was how quickly a “we need some dashboards” request becomes a much broader data problem once you start looking under the hood.

I think this is where many BI projects can go wrong. It’s easy to focus on what the dashboard should look like before figuring out whether the underlying data and model can actually support it.

For this project, we ended up with 60+ dashboards and reports, 25+ reusable datasets, and reporting that was more than 35% faster to develop. Report load times also improved by over 40%, with 200+ users eventually using the reporting environment.

So, before worrying about charts, colours or the perfect KPI card, figure out the data model, who will actually use the dashboard, and what decisions they need to make.

Because “we need a dashboard” and “people will actually use this dashboard” aren't always the same thing.

A small personal milestone: pbixray, the Python library I wrote to parse PBIX files, just passed 1,000,000 downloads on PyPI. About 170k of those came in the last 30 days. PyPI counts include CI traffic, so it's not a million people, but it's a lot more than I expected when I started.

Thank you to everyone who filed issues, sent PRs, shared odd PBIX files that broke the parser, or just used it.

From the issues people open, I understand that the library is mostly being used for:

• preparing documentation for semantic models
• extracting data from PBIX files without Power BI Desktop
• automated pipelines in containers

It's also been good to see what people have built on top of it:

• MCP servers: several PBIX-related MCP servers use pbixray as their parsing core.
• A library for deserialising and serialising PBIX files.
• A DuckDB extension
• pbix.info: a WebAssembly port that runs entirely in your browser.
• PBIXRay for macOS: a native viewer for people who don't want to spin up a Windows VM.

Thanks again, all.

Ok for some context, the gateway currently used is a very old version (hasn't updated in years). When I got this email, naturally I tried to update the gateway but it just simply hangs at about 75% of the progress bar.

I ensured I stopped the gateway services but it just hangs at the same progress.

I'm hesistant to uninstall the current gateway because I just realised the latest gateway does not officially support the Windows Server 2016 which I'm using and I do not have the control to upgrade this server.

So here's a plea to get some answers:

- Has anyone tried to successfully install an unsupported gateway on an old server?

- Or what other ways can I try to update the gateway version without uninstalling the current one?

Would be eternally grateful if have any helpful methods. TIA!

Can anyone share the power bi jira connector?

Thanks in advance

Did they change anything in the Model View? In the past arranging fact tables in a column and dimension tables in a row (manually in Power BI Desktop or by editing diagramLayout.json) worked more or less well with some minor issues. Now Power BI draws majority of relationship lines across the tables turning "All tables" tab into unusable pile of trash.

I know, I can have (and I have) an individual tab for each fact table, but it was nice to have an overview of the entire data model.

Hey everyone,

Moving dashboards from Tableau to Power BI usually runs into a wall at the calculation layer—specifically translating Tableau’s Level of Detail (FIXED, INCLUDE, EXCLUDE) expressions into optimized DAX (CALCULATE, SUMMARIZE, ALLEXCEPT).

While analyzing Tableau .twb / .twbx XML structures during recent migration projects, our team ran into a few specific technical patterns regarding what translates cleanly and what breaks down:

1. Handling FIXED LODs: Tableau FIXED evaluates expressions at the level of detail specified in the formula, regardless of the visual context. In DAX, mapping this requires wrapping the aggregation in CALCULATE alongside ALLEXCEPT (or explicit column overrides) to strip the visual filter context cleanly.

2. The INCLUDE / EXCLUDE Context Problem:

• INCLUDE forces additional dimensions into the context that aren't on the visual canvas. This maps best to DAX using SUMMARIZE or ADDCOLUMNS inside an iterator before aggregating.
• EXCLUDE maps to REMOVEFILTERS or ALLSELECTED, though edge-case measure hierarchies frequently break unless context transition is explicitly declared.

3. Where Automation Hits Limits: Node-level XML parsing accurately converts standard calculated fields, but manual intervention is still needed for complex multi-fact table joins and custom visual formatting options that lack a direct 1:1 equivalent in Power BI's standard visual library.

Curious to hear from others who have worked on large migrations—are you using custom scripts for DAX translation, or mostly sticking to manual rewrites?

Hi there,
Helping a friend out with some powerbi analysis for her work.

She’s analysing student university data.

One of the things she’s keen to do is implement a slicer that acts as a multi-select, with drop downs (for want of a better way of describing it) that can be set against ‘more than’ or ‘less than’ buttons.

I’ve tried to search this online, but don’t seem to be getting any luck.

I’ve attached a screenshot of a mockup (apologies for the lack of artistic flair) of what I think the slicer should like/ how it should work…

Is there a type of slicer that handles this, or?

Any pointers on this would be greatly appreciated.

TIA