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Skinny Experiences, Actual-world Challenges – BI Perception

Power BI Thin Reports, Real-world Challenges

I beforehand defined in a weblog put up what skinny experiences are and why we must always care about them. I additionally defined Report Degree Measures in one other weblog put up. On this put up, I attempt to increase some real-world challenges we face when growing skinny experiences. I additionally present an answer to these challenges.

Report Degree Measure Associated Challenges

Creating and utilizing Report Degree Measures is comparatively straightforward, however there are some challenges that we face every now and then, comparable to:

  • Distinguishing Report Degree Measures from Dataset Degree Measures
  • Report Degree Measure dependencies

Figuring out Report Degree Measures from Dataset Degree Measures

One of many challenges that Energy BI Builders face is creating many report degree measures. Sadly, Energy BI Desktop at present makes use of the identical iconography for each kinds of measures, making it laborious to differentiate the precise measures created throughout the dataset from the report degree measures. It will get much more difficult if we have to write technical documentation for an current skinny report. We now have to open the PBIX file of the skinny report within the Energy BI Desktop and click on each single measure. If the expression bar seems, the chosen measure is a report degree measure; in any other case, it’s a dataset degree measure.

So until we use third-party instruments, which I clarify on this put up, we should undergo the guide course of.

Report Degree Measure dependencies

One other ache level associated to the earlier problem is discovering the dependencies between the report degree measures. It’s essential to concentrate on the interdependencies when doing affect evaluation. We have to perceive how a change in a report degree measure impacts different report degree measures. Once more, Energy BI Desktop doesn’t at present have any choices supporting that, so we have now to click on each measure and browse via the DAX expressions to determine the dependencies or use the third-party instruments to save lots of growth time.

Dataset and Skinny Experiences Dependency Challenges

The opposite challenges are much more tough to beat relate to interdependencies between datasets and skinny experiences. Energy BI Service gives a lineage view that reveals the dependencies between a dataset and its related skinny experiences. However the challenges can get extra complicated to beat manually. The next are some real-world examples of extra complicated conditions:

  • What if we have to analyse the affect of modifications in a dataset measure on all report degree measures of the related skinny experiences?
  • How will we analyse the affect of modifications on a dataset measure on all related skinny experiences, together with the visuals, filters, and so on…?
  • What if we have to tune the efficiency and we need to discover a record of all unused tables or unused fields?

As you’ll be able to see, the state of affairs can get fairly complicated, so guide operations are nearly unimaginable.

However there’s a third occasion instrument we will use which gives heaps of capabilities with a few clicks.

Introducing A Third Get together Instrument That Can Assist

Happily, there’s a third occasion instrument that may assist to resolve all of the above challenges. The Information Vizioner workforce, myself included, labored laborious to implement an add-on for Energy BI Documenter that helps skinny experiences. Let’s get to it and see the way it works.

Getting a Record of Report Degree Measures and Their DAX Expressions utilizing Energy BI Documenter

We are able to at present use the out-of-box function to get all report degree measures and their DAX expressions within the Energy BI Documenter with out activating any add-ons. All you have to do is create an account if you happen to haven’t already carried out so. As you might know, Energy BI Documenter at present accepts Energy BI Template recordsdata (PBIT); so you have to open the skinny report in Energy BI Desktop and export it to PBIT, then observe these steps:

  1. Login to Energy BI Documenter
Logging into Power BI Documenter
Logging into Energy BI Documenter
  1. Click on the Add PBIT button
  2. Click on Browse and choose the PBIT file to add
Uploading PBIT files to Power BI Documenter
Importing PBIT recordsdata to Energy BI Documenter
  1. The Documenter detects the report sort is a skinny report
Power BI Documenter Detects the uploaded file is a Thin Report
Energy BI Documenter Detects the uploaded file is a Skinny Report
  1. Click on the skinny report and navigate to the Mannequin tab
  2. Develop the Report Degree Measures part
  3. Click on the Obtain as CSV file button
Getting a list of Report Level Measures and related DAX expressions
Getting a listing of Report Degree Measures and associated DAX expressions

As proven within the previous picture, you’ll be able to see the report degree measures, their DAX expressions, and the visuals utilizing them.

However wait, what concerning the different challenges we simply mentioned, the dataset to all skinny experiences dependencies, used and unused fields, and so on?

Allow us to see how Energy BI Documenter will help with these.

Skinny Report Add-on for Energy BI Documenter

As talked about, we labored laborious at Information Vizioner to organize an add-on for Energy BI Documenter. After activating the add-on in your Energy BI Documenter account, a brand new Analyse button seems on the highest proper of the Recordsdata web page.

Allow us to add a number of skinny experiences and their associated dataset recordsdata (PBIT) within the Documenter and see how straightforward it’s to get all of the dependencies in a few clicks:

  1. Click on the Add PBIT file button
  2. Click on Browse
  3. Choose all required PBIT recordsdata, together with the PBIT containing the dataset and all associated skinny experiences
  4. Click on Open
Uploading multiple PBIT files to Power BI Documenter
Importing a number of PBIT recordsdata to Energy BI Documenter

After the recordsdata are uploaded into the documented, the documented routinely detects the file sort as beneath:

Now, allow us to choose the dataset and all associated skinny experiences:

  1. Click on the ellipsis button on the specified file
  2. Click on the Choose associated experiences from the context menu
Selecting the dataset and all related thin reports in one go
Deciding on the dataset and all associated skinny experiences in a single go
  1. Now that every one associated experiences and their dataset are chosen, click on the Analyse button
  2. Choose the specified choice from the menu, the Documenter at present helps the next 4 choices:
    • Unused tables: downloads a CSV file containing a listing of the tables from the dataset that none of their fields is used wherever throughout the dataset itself and all chosen skinny experiences
    • Unused fields: downloads a CSV file containing a listing of all unused fields together with columns, calculated columns, measures, and report degree measures
    • Used tables: downloads a CSV file containing a listing of the tables that at the very least one in all their fields is used someplace throughout the dataset itself or any of the chosen skinny experiences
    • Used fields: downloads a CSV file containing a listing of the fields which might be used someplace both throughout the dataset or any of the chosen skinny experiences or their report degree measures
Analysing the dataset and all selected thin reports
Analysing the dataset and all chosen skinny experiences

There you go! You’ve got it. Within the subsequent part, we clarify what the CSV recordsdata give us.

The Definition of Used and Unused

Because the previous picture reveals, we analyse the info into the next 4 classes:

  • Unused tables
  • Unused fields
  • Used tables
  • Used fields

To know these classes we have now to have a definition for used objects the place the objects are Tabular mannequin objects. We at present do not issue the Energy Question objects and their interdependencies within the evaluation. So, whereas we have now confidence within the output, it will be significant for the customers to grasp that they should sense test earlier than deleting the unused objects from their mannequin.

The Definition of Used Fields’ definition will change as we add extra capabilities, so at all times test for the most recent definition.

The Definition of Used Fields

A area, from a Tabular object mannequin perspective, consists of columns, calculated columns, and measures. A used area is a area that seems in any of the next throughout the dataset and all skinny experiences chosen by the person:

  • Dataset degree dependencies
    • Relationships
    • Tabular object dependencies in DAX
      • Calculated column expressions
      • Measure expressions
      • Calculated desk expressions
    • Calculation teams
    • Safety
      • Row Degree Safety (RLS)
      • Object Degree Safety (OLS)
    • Kind by column
  • Report degree dependencies
    • Filters
      • Report filters
      • Web page filters
      • Visible filters
    • Anyplace on Visuals together with however not restricted to
      • Axis or values
      • Conditional formatting
      • Dynamic conditional formatting
      • Tooltips
    • Report degree measures
    • Report degree measure’s dependencies
      • Dependency on different report degree measures
      • Dependency on dataset fields

The Definition of Unused Fields

By having the definition of the used fields available, the unused ones are these fields that don’t seem within the record of used fields.

The Definition of Used and Unused Tables

A used desk is a desk with at the very least one area showing within the record of used fields. Conversely, an unused desk is a desk with no fields showing within the used fields’ record.

Understanding the CSV Output

As you’ll have already famous, figuring out the dependencies between dataset objects and all related skinny experiences is a posh course of. So the dimensions of generated CSV file varies relying on the dataset dimension, its complexity, the variety of related skinny experiences, and their complexity. We’re additionally conscious that CSV will not be the simplest format to grasp and interpret the data, so we purpose to organize a user-friendly UI sooner or later. However for now, let’s choose one choice and see what we get within the CSV file and how you can interpret the info.

In my pattern, I chosen a dataset and 11 skinny experiences. The next picture reveals the ends in the downloaded CSV file for Used Fields appears to be like just like the beneath when opened in Excel:

Unused fields CSV output from Thin Report Add-on in Power BI Documenter
Unused fields CSV output from Skinny Report add-on in Energy BI Documenter

We are able to filter the title to reply many questions comparable to the next:

What report degree measures do we have now in all skinny experiences?

To reply this query we simply have to filter the CSV when the Sort column is REPORT_MEASURE. The next picture reveals the outcomes:

Report level measures across all thin reports using Thin Reports add-on in Power BI Documenter
Report degree measures throughout all skinny experiences

The place the Date column from the Date desk is used throughout the dataset and skinny experiences?

To reply this query we have to filter the CSV when each the Desk and Sort columns’ worth is Date. The next picture reveals the outcomes:

All dependencies on the Date column from the Date table using the Thin Report add-on in Power BI Documenter
All dependencies on the Date column from the Date desk utilizing the Skinny Report add-on in Energy BI Documenter

What’s the affect of fixing the Transport Value, a dataset measure, on report degree measures?

To reply this query we simply have to filter the CSV as follows:

  • Filter the Discipline Title column to Transport Value
  • Filter the Sort column to Measure
  • Filter the Dependent Report column and exclude Blanks
  • Filter the Dependent Discipline Expression column and exclude Blanks

The next picture reveals the outcomes:

Dataset measure to report level measures dependencies using This Reports Add-on in Power BI Documenter
Dataset measure to report degree measures dependencies utilizing Skinny Experiences add-on in Energy BI Documenter

These are only some examples of questions we will reply utilizing the CSV output of the Skinny Report add-on within the Energy BI Documenter as you’ll be able to think about. For extra details about how the Skinny Report add-on works watch the next quick video:

Do you want what you see? In case your reply is sure, proceed studying.

Enabling Skinny Report Add-on in Energy BI Documenter

Because the title of this function implies it’s an add-on that you could allow in your Energy BI Documenter account. We at present allow this add-on solely by way of request. I hear you ask Why? As talked about earlier, the method of figuring out all interdependencies between the dataset objects and all skinny report objects is fairly resource-intensive that may value us some huge cash. So we can’t allow it for hundreds of customers. You don’t need to see us bankrupted, do you? So I encourage you to precise your curiosity by filling out the next type and we get again to you as quickly as we course of your request:

As at all times, I’d love to listen to your ideas. So please depart your message within the feedback part beneath.

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