Joe Mako recently created a Tableau version of a Marimekko char inspired by an example from Jon Peltier, an Excel charting expert.
When I first saw this chart, I was curious as to the utility of this chart type for regular business decision-makers. I agree that for advanced analysts, it can offer a compact, contained means to present information across two categorical items (dimensions in Tableau) and metric (a measure in Tableau.) You can see this in the Marimekko chart created by Joe Mako in the left half of the dashboard below. When you examine the Marimekko chart you can see the dominant cities and the relative share of each segment within each city. You can also select the city names above the view to highlight a specific city. For example, you can easily discern that Almond Lovers are the biggest group of customers for this company and Delicious-n-new are the smallest group.
However, it is somewhat challenging to ascertain within Gainesville, FL which segment is the largest and smallest for this city. If you hover over each Gainesville, FL value in the original chart, you will see that two segments are identical in size; this is very hard to see without the hover values. This is due to the varying width and length dimensions for each tile in the chart.
Freakalytics has published a quick reference for building better graphs and dashboards. The Rapid Dashboards Reference Card has 64 tips readily available on four full-color, laminated pages. The card serves as a handy reference for yourself, your team and even your business audience during design meetings.
Bullet charts were added to Tableau in version 5.1. They are an original idea designed and advocated for by Stephen Few, at the University of California at Berkeley. The bullet chart is intended to enable easy examination of attainment relative to a target for categorical items.
According to Stephen's original specification, "The bullet graph was developed to replace the meters and gauges that are often used on dashboards. Its linear and no-frills design provides a rich display of data in a small space, which is essential on a dashboard."
I have shown the standard Tableau bullet chart and a wide array of variants in our public training course. Based on extensive attendee feedback, I will share how just a few minutes spent enriching your bullet charts will yield powerful enhancements for your dashboard audience.
As TDWI and vendors catch on to the success of “Agile BI” being created with products like Tableau, I find the need to reply to the idea that the same old technology will work in this new world. This article from TDWI energized me to write a response to this frequently cited idea. In case you aren’t familiar with the acronmym “BI”, it simply means business intelligence, typically referring to traditional tools like Cognos, Business Objects, Excel, Microstrategy, SAS and many other products.
The 1st workbook walks you through building a standard Waterfall chart in Tableau. Waterfall charts are intended to show you how cash balances change over time based on transactions that either add to or subtract from the cash account of a business.
As this example demonstrates, many non-standard chart types can be created in Tableau using advanced features of the product. This chart type is a frequent request of students in our public and on-site Tableau training courses.
Stephen's latest book is a brilliant interplay of simple to advanced statistical concepts with powerful visual equivalents and methods. It offers wonderful guidance with great examples around visualizing time series, correlations and multivariate analysis problems. It will have a major impact for many years to come in changing how business analysts work, think and improve their companies!
A friend of mine recently asked "... are statisticians becoming irrelevant" (hey, that's me!) with the advent of advanced visualization tools like Tableau. I think the answer is yes and no. New tools like Tableau combined with methods expounded by Stephen will allow daily business questions to be answered rapidly. This should allow the statisticians to focus on tackling very high value problems where even a 1% improvement in the outcome is a huge win.
I have personally found that combining my work in data mining with beautiful stories about the results (created with Tableau) is truly impressive and informative for guiding decision-makers. Business decision-makers gain the advantages offered by advanced data mining algorithms such as decision trees, logistic regression and neural networks to identify primary predictive patterns and variables. The analyst can then clearly explain the results with compelling visuals to easily convey the findings and recommendations.
I think Stephen's latest book will become the "gold" standard for data exploration books to follow. If you need the next step past "Show Me the Numbers", this is it! BTW- it is even better than "Show Me the Numbers", which is indeed very impressive. I plan to incorporate some of this book into my advanced Tableau course, "Data Exploration and Elegant Dashboards with Tableau".