To participate in the CHALLENGE DATA GEEK I developed this document demonstrating how to use the tool and its SAP Visual Intelligence tools.
I will demonstrate how to use some tools provided by SAP Visual Intelligence, to be able to extract quickly and easily information from our databases.
For this I used:
SAP Visual Intelligence – https://www.sap.com/campaign/ne/free_trial/visual_intelligence/index.epx?URL_ID=SAPanalytics
Data NHL Final Analystic – http://www.sdn.sap.com/irj/scn/index?rid=/library/uuid/f04e9b22-24ff-2f10-89b2-b1686f735d02
For DATA GEEK we must rely on the following issues:
How did you prepare the data?
How you analyzed and manipulated data?
What SAP visual intelligence functions you used?
What problems you solved?
What workflows you used?
What business questions did you answer?
1 – Data was extracted from the SAP Web site. And loaded into the tool SAP Visual Intelligence (as can be seen in the course of explanation).
2 – Data were analyzed from the perspective of an NHL league manager, data were manipulated within the SAP tool Visual Intelligence and saved within it.
3 – The following functions were used dento SAP Visual Intelligence:
– Merge sheet‘s
– Adding columns with formulas
– Creating hierarchies by regions and dates
– Data corretion with find and replace
4 – We solved the problems of hierarchies, using the generation function hierarchies.
5 – Load and analysis.
6 – Questions answered businesses:
– Average age of players per country.
– Number of players per team in each country (eg Anahain Ducks team).
– The number of American’s playing for time.
– Increased average salary per division.
– Cost benefit by dividing (x GOALS Average salary).
– Average amount paid X Performance.
To load the information and begin our work we start a new document:
You will see the screen to select the type of source that will be used:
In this case we will use as a source of data in excel file.
We will load the data from the sheet needed. Here we load all worksheets (although we use only 2):
After selecting the data sheet will be loaded into the grid.
Load all sheets:
Check sheets loaded by clicking on the Change button:
With data loaded to start the manipulation of information to have the data exactly as required for analysis.
We first checked whether there was some information that was necessary to run a correction or Split. For Split nothing was needed but for correction was decided to correct the name of the city of Québec, where he was set to Quebec.
Select the column and after that used the option (right bar) for manipulation of information, among the options for data manipulation tools we used the Find / Replace:
After all completed using the button Do it
After changing the scenario is presented below:
After the manipulations of the data necessary to use the merge function to the junction of sheets and with this additional information between the sheets.
To use the merge function will select the Merge button on the right side of the screen:
Screen appears with Sheets that can be merged:
Select the fields that will be the key liaison between the sheets:
After selecting the fields click on Merge.
After the junction of the fields you will have a single table with all the fields together.
After making the junction with all the sheets you want, now go to an important stage: Hierarchy.
Let’s create hierarchies of locality, so that the values can be displayed in map correctly.
Selects a feature of location and right select Create a geographic hierarchy => By Latitude / Longitude:
As these data are contained latitude and longitude, we mount the hierarchy based on this information.
Select the fields latitude and longitude that are on sheet:
There will scale geographic fields to filters and selections:
It is still possible to generate hierarchy dates, as demonstrated below:
Similarly as in the case of geographical hierarchy columns have now divided:
Create items to measure values in our analysis.
Select the field you would like to generate the values right and select Create a mensuare:
After the creation of the items that will be measured, will be created on the left side of the columns that can be used:
To start the analysis may change the form of visualization:
With the new view we start we want to be answered.
– Average age of players by country:
– Average amount paid X Performance.
Any questions feel free.