Many dashboards and reports present large volumes of data, but not all of them drive decisions.

In your experience, what differentiates a report that influences action from one that simply displays metrics?

Is it clarity of KPIs, storytelling, stakeholder alignment, visualization design, or something else?

Would appreciate insights on best practices, common mistakes, and real-world examples of reporting that actually moved the needle.

Hey everyone,

I’m working on a Power BI report and could use some help figuring out the best way to visualize order details.

Note: The real use case I’m working on is a bit more complex and domain-specific (finance related), but I’ve simplified it here using an order/orderline example that structurally behaves the same.

Order dimension → contains OrderID, date, customer info, etc.

• OrderLine dimension → contains OrderLineID, OrderID (1 order → many lines)

• A fact table → contains OrderLineID and columns like:

  • Amount

  • Discount

  • Tax

  • etc.

All relationships are properly set up, and granularity is at the OrderLine level.

When I select an Order, I want to show a matrix where:

• Each OrderLine is displayed as a column

• Each attribute (like Amount, Discount, Tax) is listed as a row

• The intersecting cells show the actual values

See the example attached.

In the search for Machine Learning Engineer (MLE) roles, it’s becoming evident that a significant portion of these positions — though certainly not all — appear to favor candidates with PhDs over those with master’s degrees. LinkedIn Premium insights often show that 15–40% of applicants for such roles hold a PhD. Within large organizations, it’s also common to see many leads and managers with doctoral degrees.

This raises a concern: Is there an unspoken glass ceiling in the field of machine learning for professionals without a PhD? And this isn’t just about research or applied scientist roles — it seems to apply to ML engineer and standard data scientist positions as well.

Is this trend real, and if so, what are the reasons behind it?