Thin Reports, Report Level Measures vs Data Model Measures

Thin Reports, Report Level Measures vs Data Model Measures

The previous post explained what Thin reports are, why we should care and how we can create them. This post focuses on a more specific topic, Report Level Measures. We discuss what report-level measures are, when and why we need them and how we create them.

If you are not sure what Thin Report means, I suggest you check out my previous blog post before reading this one.

What are report level measures?

Report level measures are the measures created by the report writers within a Thin Report. Hence, the report level measures are available within the hosting Thin Report only. In other words, the report level measures are locally available within the containing report only. These measures are not written back to the underlying dataset, hence not available to any other reports.

In contrast, the data model measures, are the measures created by data modellers and appear on the dataset level and are independent from the reports.

Why and when do we need report level measures?

It is a common situation in real-world scenarios when the business requires a report urgently, but the nuts and bolts of the report are not being created on the underlying dataset yet. For instance, the business requires to present a report to the board showing year-to-date sales analysis but the year-to-date sales measure hasn’t been created in the dataset yet. The business analyst approaches the Power BI developers to add the measure, but they are under the pump to deliver some other functionalities which adding a new measure is not even in their project delivery plan. It is perhaps too late if we wait for the developers to plan for creating the required measure, go through the release process, and make it available for us in the dataset. Here is when the report level measures come to the rescue. We can simply create the missing measure in the Thin Report itself, where we can later share it with the developers to implement it as a dataset measure.

How do we create report level measures?

Currently, we can create report level measures only in Power BI Desktop when Connect Live to either a Power BI dataset, an SSAS Tabular model (on-premises), or Azure Analysis Services (AAS). For this blog post, I Connect Live to a Power BI dataset. Open Power BI Desktop first and follow these steps.

  1. Click the Data hub dropdown button from the Home tab
  2. Click the Power BI datasets
  3. Select the desired dataset
  4. Click Connect
Connecting Live to a Power BI dataset from Power BI Desktop
Connecting Live to a Power BI dataset from Power BI Desktop
  1. Right-click the table that you’d like to host the report level measure and select New measure
Creating a new report level measure in Power BI Desktop
Creating a new report level measure in Power BI Desktop
  1. Type your DAX expressions as usual and press enter
Typing DAX expressions to create report level measures
Typing DAX expressions to create report level measures

A new measure (Avg Unit Price) is now created on the Internet Sales table. As explained earlier, the new measure is only available in the current report and not at the dataset level and that is why this type of measure is the so-called Report Level Measure. All other measures are dataset level measures, therefore, they are available on the current report and any other thin reports we create in the future on top of the same dataset.

We can now use the Avg Unit Price as usual in our data visualisation.

Using report level measures in visuals in Power BI Desktop
Using report level measures in visuals in Power BI Desktop

As perhaps have noticed already, we can also create report level measures using the Quick Measures capability.

As a side note, you can also see the underlying data model by clicking the Model view tab as shown in the following image:

Model view tab in Power BI Desktop when connected live to a Power BI dataset
Model view tab in Power BI Desktop when connected live to a Power BI dataset

Have you used this capability? What challenges have you faced in using Report Level Measures? I would love to know your thoughts, so feel free to leave your comments below.

3 thoughts on “Thin Reports, Report Level Measures vs Data Model Measures

  1. The Information Logistics – the Admin – is still a challenge.
    When the data source, DL, DW, etc is renamed/built – as a Principle – the Business/Report Level Measures should continue to work.

  2. I am very new to Power BI so I’m sure some of this is over my head. However, the name of this article is “Thin Reports, Report Level Measures vs Data Model Measures” and yet, other than in the title, the phrase “report level measure” is not mentioned. Perhaps you would add an addendum and explain the differences between these three terms more directly. Thank you.

    1. Hi Sam,

      Thank you for your comment.
      I am a bit confused, the entire blog is about “Report level measures” that are only meaningful in the realm of “Thin reports“.
      Can you please elaborate on “other than in the title, the phrase “report level measure” is not mentioned”?

      Thank you!

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