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Skinny Stories, Actual-world Challenges – BI Perception


Power BI Thin Reports, Real-world Challenges

I beforehand defined in a weblog submit what skinny stories are and why we must always care about them. I additionally defined Report Degree Measures in one other weblog submit. On this submit, I attempt to elevate some real-world challenges we face when creating skinny stories. 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 so often, 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 at present makes use of the identical iconography for each forms of measures, making it exhausting to differentiate the precise measures created inside 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 until we use third-party instruments, which I clarify on this submit, 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 impression 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 now we have to click on each measure and skim by the DAX expressions to establish the dependencies or use the third-party instruments to save lots of growth time.

Dataset and Skinny Stories Dependency Challenges

The opposite challenges are much more troublesome to beat relate to interdependencies between datasets and skinny stories. Energy BI Service supplies a lineage view that exhibits the dependencies between a dataset and its related skinny stories. 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 impression of modifications in a dataset measure on all report degree measures of the related skinny stories?
  • How will we analyse the impression of modifications on a dataset measure on all related skinny stories, together with the visuals, filters, and so forth…?
  • What if we have to tune the efficiency and we need to discover a checklist of all unused tables or unused fields?

As you’ll be able to see, the scenario can get fairly complicated, so guide operations are just about unimaginable.

However there’s a third social gathering instrument we will use which supplies heaps of capabilities with a few clicks.

Introducing A Third Social gathering Instrument That Can Assist

Luckily, there’s a third social gathering instrument that may assist to resolve all of the above challenges. The Knowledge Vizioner staff, myself included, labored exhausting to implement an add-on for Energy BI Documenter that helps skinny stories. 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 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 want to do is create an account if you happen to haven’t already executed so. As it’s possible you’ll know, Energy BI Documenter at present accepts Energy BI Template recordsdata (PBIT); so you want 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. 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’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 stories dependencies, used and unused fields, and so forth?

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 arrange 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 stories 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 stories
  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 robotically detects the file sort as under:

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

  1. Click on the ellipsis button on the specified file
  2. Click on the Choose associated stories from the context menu
Selecting the dataset and all related thin reports in one go
Deciding on the dataset and all associated skinny stories in a single go
  1. Now that every one associated stories and their dataset are chosen, click on the Analyse button
  2. Choose the specified possibility 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 stories
    • 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 a minimum of one among their fields is used someplace inside the dataset itself or any of the chosen skinny stories
    • Used fields: downloads a CSV file containing a listing of the fields which might be used someplace both inside the dataset or any of the chosen skinny stories or their report degree measures
Analysing the dataset and all selected thin reports
Analysing the dataset and all chosen skinny stories

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

The Definition of Used and Unused

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

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

To know these classes now we have 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 now we have confidence within the output, it is necessary for the customers to know that they should sense examine earlier than deleting the unused objects from their mannequin.

The Definition of Used Fields’ definition will change as we add further features, so all the time examine for the most recent definition.

The Definition of Used Fields

A discipline, from a Tabular object mannequin perspective, consists of columns, calculated columns, and measures. A used discipline is a discipline that seems in any of the next throughout the dataset and all skinny stories 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
    • Wherever 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 a minimum of one discipline 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’ll have already famous, figuring out the dependencies between dataset objects and all related skinny stories 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 stories, and their complexity. We’re additionally conscious that CSV shouldn’t be the simplest format to know and interpret the knowledge, so we goal to arrange a user-friendly UI sooner or later. However for now, let’s decide one possibility 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 stories. The next picture exhibits the ends in the downloaded CSV file for Used Fields appears to be like just like the under 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 now we have in all skinny stories?

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

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

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

To reply this query we have to filter the CSV when each the Desk and Sort columns’ worth is Date. The next picture exhibits 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 impression of adjusting the Transport Value, a dataset measure, on report degree measures?

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

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

The next picture exhibits 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 Stories 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 brief 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 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 price 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 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 depart your message within the feedback part under.

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