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Scenario:
This walkthrough of SAP BusinessObjects Cloud uses a public dataset from LendingClub, a peer to peer money lending company from San Francisco, to showcase the product and features, specifically geospatial functionality.
In this demo session, you will perform the following exercises within SAP BusinssObjects Cloud
* Disclaimer:
- For the best experience, please use Google Chrome and refrain from using the "Upload File" functionality
- When logging in for the first time and choosing a new password, the password must have at least eight characters and contain at least one of the following character types: at least one uppercase, at least one digit, and lowercase.
Let's get started!
Log on to SAP BusinessObjects Cloud with the log in information you were provided. If you don't have the log in information, you can request an invite here. |  |
| The application provides you with additional tool tips to get started easily. You can choose to keep this turned on or turn it off at any point of the demo. |  |
Get familiar with the home screen. It gives you an overview of the actions you can take Note : Based on the account permission setting only a limited set of actions might be available. |  |
| To continue the analysis you started last time, navigate to the menu on the top left corner and select "Stories" |  |
| Open the "LendingClub Demo" story under the "All" tab. |  |
The story will open. As you only have read rights, use the "Save As" option to create your own copy of the document. |  |
Please include your name in the new story title - Example: LendingClub Demo - YourName. Click "OK" to save the story. |  |
| You can then take a look at the existing charts. The LendingClub marketing division has set up this page to give an overview of lending patterns. You can read more about each chart and what they represent below. |  |
Chart 1 – Total Return per Date This chart shows information about when the most popular times to loan and worst times to loan. This information helps the company plan their campaigns accordingly. You can see that Q3 had the most money loaned. You can drill down to see the specific months in that quarter to find out which month had the most money loaned (July). |  |
Chart 2 – Total Return per Grade Lending Club offers loans to various different customers who are graded based on a number of factors. A (Low risk, low interest rates) is the highest, and G (High risk, high interest rates) is the lowest grade. You can see that grade C is the best performing (highest return), most likely due to the balance between medium risk and return. You can also drill down to see which Subgrade of C is performing the best. This chart shows valuable information regarding customer type and revenue. It can be used to make important decisions as to which type of loans should be approved/declined. |  |
Chart 3 – Annual Income per Region This chart summarizes customer’s average income per region. You can drill down to display average incomes for each city/state within the region. As a result you can see that most annual incomes are fairly similar, but customers in the western region seem to make slightly more. |  |
Chart 4 – Current Balance per Loan Status This chart shows the status of customers’ loans. This information is useful for the finance or accounting department to see where the money is and can then account for defaults/loans that have been charged off. Using a Pie Chart/Donut Chart, we can see how different loan statuses are distributed. However, you can see that certain numbers don’t show up well in this chart type (such as Loans that are in Default status). Perhaps another chart type would show information more clearly. |  |
Now you would like to look into location specific data. Add a new page and select "Canvas". Add a new geo chart. Select the "Lending_Club_Model" for the chart and click "Ok". |    |
A map is dropped onto the canvas. In the Builder menu on the top right side, add a bubble layer. Under Geo Dimension, choose the dimension “Locations”. This was already created in the model (geo enriching by adding latitudes and longitudes). |   |
Enlarge the chart to fit the canvas and zoom in until you see the data points. You notice the large amount of customers located in the south. This can be explained using the power of data visualization. Let’s test to see if this influx is caused by hurricanes and other natural disasters in the Southern states. |  |
Let’s add a Point of Interest (POI) layer. Add a new layer and click the Bubble icon to change to Point of Interest layer. Under Geo Dimension, select the dimension “Hurricane POI”. |   |
| You can immediately see that the hurricanes are centered right where the large number of lenders are. |  |
Now you want to prove this theory using the data you already have. Create a new chart. Select Lending_Club_Model and click OK. |   |
An empty chart is dropped onto the canvas. In the Builder menu, under Axis one, add in the measure Initial Loan Amount. Under Color, select the dimension Purpose |    |
We want the chart to only show information for the South East region. In the same Builder menu, click on the + icon next to the Filter section. Select City. Click on South East to select that region and click OK.. |   |
You can then sort the chart to display the data from highest to lowest. Click on the Sorting icon on the side of the chart and select Lowest to Highest. |  |
Now you can clearly see that the top reason people get loans in the South Eastern region is Natural Disasters. This confirms your theory! |  |
Thank you!
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