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BIME Analytics : Parallel set - Turning a Data Table into a Tree

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BIME : PARALLEL SET Turning a Data Table into a Tree Parallel sets are a new way to analyse categorical data such as gender, age or product category. It is particularly well suited for answering how many members of column A are also in column B? Let’s take the example of this table that is the canonical example used for parallel sets: statistics about Titanic survivals. And turn it into a parallel sets: Women and children (in that order) first!! Women and children (in that order) before!! Reading just the bars in Figure 12-2, it is easy to see that the crew was the largest class of people on the Titanic, with the third class close behind. The first class was much smaller than the third class, but was actually larger than the second class. It is also quite obvious that there was a majority of men (almost 80%) on the ship, and that only roughly one-third of all people on board survived. Ribbons connect categories that occur together, showing how often, for example, first class and female intersect, thus making it possible to tell what proportion of the passengers in first class were women. So the parallel set is a new tool in your toolset for categorical data. As with most visualizations (except maybe the column chart) it has advantages and drawbacks. With high-cardinality columns it becomes a bit messy and it takes time to be used to how to read quickly, for instance, but on the other hand it is very interesting to use with a lot of low-cardinality columns. Extra bonus: it looks intriguing and hence increase engagement on your dashboards. About BIME : BIME delivers a simple-to-use yet powerful data analysis and dashboarding cloud platform accessible everywhere that enables modern organizations to explore, understand and communicate data with style. Its UI combines the visual simplicity and elegance of the best consumer apps with powerful features to connect to all major data sources, on-premise or online - from spreadsheets and traditional relational databases to Google Analytics, Facebook, Twitter, YouTube, Salesforce and Zendesk and up to Big Data sources such as Google BigQuery, Amazon Redshift, Microsoft Azure and SAP HANA. BIME runs entirely in the cloud so enterprises avoid spending tens of thousands of dollars on servers to upload and refine their data. No other BI service is as flexible and powerful, as beautiful and enjoyable and as affordable as BIME. In the mobile first, cloud first world, maximizing ease of use is the name of the game for any business application. BIME is setting a new industry standard for business intelligence UI.
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  • 3. And turn It into a parallel sets: bime nl'
  • 4. Women and children (in that order) first! ! Women and children (in that order) before! ! Reading just the bars in Figure 12-2, it is easy to see that the crew was the largest class of people on the Titanic, with the third class close behind. The first class was much smaller than the third class, but was actually larger than the second class. lt is also quite obvious that there was a majority of men (almost 80%) on the ship, and that only roughly one-third of all people on board survived_ Ribbons connect categories that occur together, showing how often, for example, first class and female intersect, thus making it possible to teII what proportion of the passengers in first class were women_ So the parallel set is a new tool in your toolset for categorical data. A5 with most visualizations (except maybe the column Chart) it has advantages and drawbacks. With high-cardinality Columns it becomes a bit messy and it takes time to be used to how to read quickly, for instance, but on the other hand it is very interesting to use wlth a lot of Iow-cardinallty columns_ Extra bonus: It looks lntrlgulng and hence Increase engagement on your dashboards. bime mIÍ
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