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
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.
From a data visualization point of view I'm more excited about the 2nd option, than about the addition of Python to the toolstack.
Assigning the data category "Image Url" to a mearsure opens up new possibilities to create measures that return a simple text string where the text represents an svg image. If you are not familiar with svg images,
this will get you started: https://en.wikipedia.org/wiki/Scalable_Vector_Graphics
Here you will find
extensive tutorials: https://developer.mozilla.org/en-US/docs/Web/SVG/Tutorial
What I love about the usage of svg next to the other options for data visualization is the simplicity with wich svg charts can be used. They do not extensive knowledge of a programming language like JS, R or Python (I'm pretty sure, that some consider the composing of svg charts also as some kind of programming :-) ). Another great advantage is that they just can be used inside the Power BI table and matrix visual, without any dependencies to other programming environments.
I have to admit that I'm quite excited about the possibility to use svg charts from inside the table and matrix visual. This easily allows to create microcharts and small multiples.
In this post (it's more than likely that this will not be my last one) I use 2 percentage values to create layered segments of a pie.
This kind of visualization is called "Harvey Ball".
From my point of view a slice is one of the ideal forms to represent a percentage (a part of the whole), this is due to the resemblance of an reduced watch dial the accompanying pbix file contains a lot more information
The underlying data is sample data, where project performance is measured by 2 values:
"Percent completion" represents how much tasks have been accomplished and "Time spent" reflects how much time has been spent, here the percentage reflects the actual spent time in comparison to the planned time. Basically it's better if more tasks are completed in less time. "Red" segments are shown when more time is spend than planned:
I also created an angular version of the Harveyball chart from above. I call this version Harveybox. Here I use the "cost" measure "Time spent" on the left side from the box and the "revenue" measure "percent completion" on the right side. This leads to a rising line that symbolizes "success" in many societies, whereas a slopy line often symbolizes failure: