Power BI Complexity Pyramid

Inspired by a blog by Matt Allington at Eceleratorbi.com.au, I did an image to represent the learning complexity of Power BI.

I acknowledge that the judgement of complexity is very subjective but this is just a guide for me to explain someone what they need to learn. I possibly might have missed some key areas but will revise this as I remember or find out more.



Data Visualisation

Extract from Wikipedia :

Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics.


Data Analytics

Extract from Wikipedia :

Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making. In other words, analytics can be understood as the connective tissue between data and effective decision making, within an organization.
Analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.
Organizations may apply analytics to business data to describe, predict, and improve business performance.

Analysis vs. Analytics

Analysis is focused on understanding the past; what happened.

Analytics focuses on why it happened and what will happen next.


Visual Analytics

Extract from Wikipedia :

Visual analytics is “the science of analytical reasoning facilitated by interactive visual interfaces.”
It can attack certain problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. Visual analytics advances science and technology developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition.




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