Skinny Reviews, Actual-world Challenges - BI Perception - Geo Tv News

Skinny Reviews, Actual-world Challenges – BI Perception


Power BI Thin Reports, Real-world Challenges

I beforehand defined in a weblog publish what skinny experiences are and why we should always care about them. I additionally defined Report Degree Measures in one other weblog publish. On this publish, 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 simple, however there are some challenges that we face now and again, akin 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 presently makes use of the identical iconography for each varieties of measures, making it exhausting to tell apart 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 present 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 except we use third-party instruments, which I clarify on this publish, we should undergo the handbook 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 presently have any choices supporting that, so we’ve got to click on each measure and skim by means of the DAX expressions to establish the dependencies or use the third-party instruments to avoid wasting improvement time.

Dataset and Skinny Reviews Dependency Challenges

The opposite challenges are much more tough to beat relate to interdependencies between datasets and skinny experiences. Energy BI Service offers a lineage view that reveals the dependencies between a dataset and its linked 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 adjustments in a dataset measure on all report degree measures of the linked skinny experiences?
  • How can we analyse the affect of adjustments on a dataset measure on all linked skinny experiences, together with the visuals, filters, and many others…?
  • What if we have to tune the efficiency and we wish to discover a checklist of all unused tables or unused fields?

As you may see, the state of affairs can get fairly complicated, so handbook operations are just about unattainable.

However there’s a third social gathering instrument we are able to use which offers heaps of capabilities with a few clicks.

Introducing A Third Celebration Device That Can Assist

Luckily, there’s a third social gathering instrument that may assist to resolve all of the above challenges. The Knowledge Vizioner crew, myself included, labored exhausting 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 Checklist of Report Degree Measures and Their DAX Expressions utilizing Energy BI Documenter

We will presently 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 should do is create an account for those who haven’t already performed so. As chances are you’ll know, Energy BI Documenter presently accepts Energy BI Template recordsdata (PBIT); so you should 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 kind 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. Increase 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 may see the report degree measures, their DAX expressions, and the visuals utilizing them.

However wait, what in regards to the different challenges we simply mentioned, the dataset to all skinny experiences dependencies, used and unused fields, and many others?

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

Skinny Report Add-on for Energy BI Documenter

As talked about, we labored exhausting at Knowledge 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 Information web page.

Allow us to add a number of skinny experiences and their associated dataset recordsdata (PBIT) within the Documenter and see how simple 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 kind 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 presently 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 anyplace 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 not less than certainly one of 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 can 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 gotten 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’ve got to have a definition for used objects the place the objects are Tabular mannequin objects. We presently do not issue the Energy Question objects and their interdependencies within the evaluation. So, whereas we’ve got confidence within the output, it’s important 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 features, so all the time test for the most recent definition.

The Definition of Used Fields

A subject, from a Tabular object mannequin perspective, contains columns, calculated columns, and measures. A used subject is a subject 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)
    • Type 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 readily available, the unused ones are these fields that don’t seem within the checklist of used fields.

The Definition of Used and Unused Tables

A used desk is a desk with not less than one subject showing within the checklist of used fields. Conversely, an unused desk is a desk with no fields showing within the used fields’ checklist.

Understanding the CSV Output

As you might have already famous, figuring out the dependencies between dataset objects and all linked skinny experiences is a fancy course of. So the scale of generated CSV file varies relying on the dataset measurement, its complexity, the variety of linked skinny experiences, and their complexity. We’re additionally conscious that CSV just isn’t the simplest format to grasp and interpret the data, so we goal to organize a user-friendly UI sooner or later. However for now, let’s decide one choice and see what we get within the CSV file and the right way to 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 will filter the title to reply many questions akin to the next:

What report degree measures do we’ve got in all skinny experiences?

To reply this query we simply have to filter the CSV when the Kind 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 Kind 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 adjusting 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 Identify column to Transport Value
  • Filter the Kind 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 Reviews add-on in Energy BI Documenter

These are only some examples of questions we are able to reply utilizing the CSV output of the Skinny Report add-on within the Energy BI Documenter as you may 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 identify of this function implies it’s an add-on that you could allow in your Energy BI Documenter account. We presently allow this add-on solely through 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 1000’s of customers. You don’t wish to see us bankrupted, do you? So I encourage you to precise your curiosity by filling out the next kind and we get again to you as quickly as we course of your request:

As all the time, I’d love to listen to your ideas. So please go away your message within the feedback part beneath.



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