Mincing Data - Gain insight from data

This site will be a container for all my musings about the analytical pipeline.

For this reason it will be necessary to define the Analytical Pipeline (at least define this pipeline from my point of view). From a general perspective (be aware that this is my perspective) five activities are necessary to build an analytical pipeline, these steps are






 The overall goal of an analytical pipeline is to answer an analytical question. To achieve this overall goal, different data sources have to be targeted and their data has to be ingested into the pipeline and properly processed. During these first steps the data ingested into the pipeline often has to be stored in one or more different data stores, each is used or its special type of usage, finally the result of the data processing has to be delivered to its users, its audience. Depending on the nature of the question, different types of processing methods and also different data stores may be used along the flow of the data throughout the pipeline.


I put a direction to these activities, but I also added this direction to spur your critical mind, because

  • source data is of course also stored somewhere
  • to target the source can become a challenge in the pipeline
  • to deliver processed data to the its audience can become a another challenge  if you try to deliver to mobile workforce
Successfully ingested data often gets processed more than once to answer different questions, and during its existence (inside an analytical data platform) this data will be transformed into various shapes, wrangled by different algorithms, and ingested into different "data stores".

For this reason I believe that these activities are tightly related, and the above mentioned sequence of these activities will just aid as a guidance.


I will use blog posts to describe how different activities are combined to answer analytical questions. In most of my upcoming blog posts I will link to different topics from the activities used in the pipeline. Each activity has its own menu and is by itself representing an essential part in analytical pipeline.


Hopefully this site will help its readers as much as it helps me to focus on each activity always knowing that most of the time more than one activity has to be mastered to find an answer to an analytical question.

The dreaded Total in stacked charts

The September 2020 release of Power BI Desktop once again comes with a ton of features. Maybe the most important thing is that the “Store datasets using enhanced metadata format” is no longer a preview feature - it’s GA now.

But sometimes it’s the small things that get us excited, at least gets me excited. For this reason, I’m trying to direct your attention to this new feature, or as I would say, this capability.

If you are wondering what I’m talking about, Stacked charts now support the Total label. Until now, we haven’t been able to create a stacked bar chart that also displays the total sum like this:

To be honest, we have been able to create a chart like this, just by using the Line and stacked column chart visual, then changing the width of the line to zero and displaying the data label. There are some excellent videos and blogs available that explain how to achieve the above in great detail.

Unfortunately, this trick only works if all the values are positive. As soon as one segment value is negative, this trick does no longer work. The explanation of why this trick fails is simple. The easiest way to explain this is by using another screenshot.

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