The sample dataset consists of approximately 190,000 individual data fields. (If interested, you can download the raw data here). In the examples, we focus on the following aspects and query the data using visual means:
Which products from the range sell well? How do they compare to other products? Can trends be identified?
In addition to general revenue figures by customer segment, the visualization also shows their development over a given reporting period, as well as their geographical distribution, so that local sales promotions or offers, for example, could be considered as measures.
Which customers generate the highest absolute profit (as opposed to pure revenue) and which ones produce losses? Where are they located geographically?
Can statements be made about the type, quantity, and development of individual orders, especially regarding their geographic origin?
How many - and specifically which - orders were shipped on time, and how many were late? How much time elapsed between order and actual shipping in each case? Are there differences that can be identified, e.g., by products, product groups, and customer segments?
How profitable are individual areas, broken down by product groups and customer segments ("which products/product groups are most profitable")?
Although only a few more or less random questions were selected for our example, the amount of information contained in this simple spreadsheet has proven to be significantly more extensive and informative than initially anticipated.
Thanks to Tableau, the time, technical, and financial effort involved is only a fraction of what would be necessary with "conventional" means - such as Excel formulas, filters, and charts, SQL queries, or custom programming.
And: The type of queries would already have to be specified in detail in advance - i.e. one would basically only find exactly what one has already anticipated, and would thereby lose the chance to discover completely new, previously unsuspected, things in the data and their relationships.
But even more important: The information gained through the various visualizations can now be used profitably. Once the information hidden in the data has been recognized and understood, practical steps can be derived from it and implemented profitably.