This is the 2nd part of the Greek Tax Data Analysis with SAP Lumira that aims to highlight the majority of Lumira’s amazing features and capabilities.
I would like to thank you for your positive comments on the 1st part of this analysis (http://scn.sap.com/community/lumira/blog/2013/12/09/jjj) and I hope you will also enjoy this one!
Once again, the data came from the open data of the General Secretariat of Information Systems (http://www.gsis.gr/gsis/info/gsis_site/PublicIssue/Statistics.html).
For this 2nd part we will use a dataset that contains the following tax data:
Number of Taxpayers, Declared Income and Total Tax Paid for both Attica Region and the rest of the country, as well as for all the different Income Categories from 2006(FY) to 2010(FY).
1) To begin with, a Waterfall Chart illustrates the Total Tax Paid by Income Categories.
2) Selecting to display the same information in Sort Descending it becomes easier for us to understand which
Income Categories pay the most taxes.
3) A Heat Map can help us visualize more complicated information. The following Heat Map displays the Number of Taxpayers (tile size) and
the Total Tax Paid (tile color) for all the different Income Categories.
4) With a Bubble Chart the analysis can get even more multidimensional. The two axes display the Number of Taxpayers and the Declared Income,
the size of the bubble represents the Total Tax Paid and each Income Category is marked with a different bubble color.
5) The problem with the numerous Income Categories is that it becomes difficult to draw conclusions for the different ranges of Income Categories.
SAP Lumira has the solution for this problem. It allows grouping the data of a column in smaller groups so as to be easier to analyze the results.
In this case, four different income groups are created summarizing the initial Income Categories.
As you can see in the following image, a new grouping column is created (without deleting the original column). The four different group categories are: Low Income (0€-12.000€), Medium Income (12.000€-30.000€), High Income(30.000€-100.000€) and Very High Income(100.000- >900.000€).
6) Returning to the previous Bubble Chart, we select to visualize the Grouped Income Categories instead of the Income Categories. As a result, we get the following Chart, which allows a much easier analysis than before.
7) Next, we want to display the Number of Taxpayers by Region (data for Attica and the Rest of the country) and Year in a Bar Chart. The problem is that the names of the two regions are both written in two different ways. This is a common mistake occurring when a database is updated by different users who refer to the same value but write it in a different way.
8) In SAP Lumira, it is very easy to “clean” our data. We go to the Prepare Tab and after clicking on the problematic column we group the selected values under a common value.
9) Going back to the Bar Chart, we can see that this problem no longer exists.
10) Last but not least, let’s see what happens when our database is updated with new records for the following year (2011). Keep in mind that the new data have the same problem in the Region column (same value written in two different ways).
In SAP Lumira, it takes only one click to update the charts we have already created by selecting “Refresh document”.
As we can see, the Bar Chart we created before, not only does get updated with the data of 2011, but also it is automatically cleansed according to the rule we had set before.
This is the end of the Greek Tax Data Analysis. With SAP Lumira the analysis became fast, easy and fun.
Feel free to write your comments.