During recent weeks and months I have been studying data and decision analysis in general and especially the use of Tableau Software for applying data analysis to large datasets in order to transform the data into knowledge which then can be used to answer important questions, solve important problems, make decisions and implement them by effective actions.
Tableau software has its roots in the Stanford University Science Department research project which is aimed at increasing people’s ability to rapidly analyze data. Tableau’s main approach to visual design is to connect to a data source, and drag data fields to its workspace.
Tableau Desktop is a software package for data analysis. It’s easy to learn, easy to use, and extremely fast. It allows you to use your natural ability to see patterns, identify trends and discover visual insights.
You can connect to data and perform queries without writing a single line of code. You can follow your natural train of thought as you shift between views with drag-and-drop technology.
You can connect directly to data for live, up-to-date data analysis or extract data into Tableau’s fast data engine and take advantage of breakthrough in-memory architecture, or do both, for 2, 3, or even 10 different data sources and blend them all together. Tableau has a large number of native connectors to data sources. A list of connectors can be viewed at
Multiple views can be combined into interactive dashboards. Data can be filtered and highlighted to show relationships between variables. Content can be shared using the web-based Tableau Server or Tableau Online. Content can also be embedded into website pages, including blogs.
Tableau has powerful analytical tools. You can filter data dynamically, split trends across different categories or run an in-depth cohort analysis. You can double-click geographic fields to put data on a map. In addition it can be integrated with R.
You can go deeper into your data with new calculations on existing data. You can ake one-click forecasts, build box plots and see statistical summaries of your data. Run trend analyses, regressions, correlations, ….
There is a large amount of material on Tableau and its application to data analysis available on the Tableau website (http://www.tableausoftware.com/), in blogs, and in a number of books. Some of these books are available on Kindle.
Tableau is an ideal analysis and visualization tool in that it possesses the following attributes:
Simplicity – easy for non-technical users to master
Connectivity – seamlessly connects to a large variety of datasources
Visual competence – provides appropriate graphics
Sharing – facilitates sharing of knowledge, understanding and insight
Scaling – handles large data sets