Configbot DF - Impact Analysis Roles - Roles Features Matched

Impact Analysis Roles - Role Features Matched

The following article explains how to generate Impact Analysis Roles for different roles and their features using ConfigBot.

1.     Enter your Username and Password and click at the submit button to login.

2.     Navigate towards the "Home screen" and click on the "Impact Analysis" module tile under Impact Analysis.

3.     Select the workspace that best suits your preferences or requirements.

4.  Navigate to the workbook (in my case, Iteration #1) and open the workbook.

5.     The workbook will appear in a new tab on your browser. Navigate to the worksheet tab labeled Role Features Matched, here you can see empty sheet now we will generate analysis for Role Features to see the similar and different features among different roles in same instance.

6. To generate the analysis, navigate back to your workspace, open the hamburger menu, and select the ‘Generate Analysis’ option. In the Generate Analysis window, choose the ‘Roles Features Matched’ option and select the source iteration from which data would be fetch. Once the selections are complete, click the ‘Confirm and Start’ button to initiate the analysis process.

7. You can track the progress of iteration by clicking on Information Icon (i). 

8. Once the execution is completed, open the workbook and navigate to the relevant sheet. Review the data by the numbers generated with percentages which indicates similarities and differences, like "100" indicates the similar Roles features assigned to Each Roles and "0" indicates different features inside the role.

In conclusion, ConfigBot’s analysis functionality provides clear visibility into data similarities and differences within source iterations, enabling users to evaluate the Roles. By using numerical indicators and percentage-based insights, where values such as “100” represent complete similarity in Roles Features among Roles and “0” indicate no similarity. By generating and reviewing this analysis, users can effectively identify patterns of similarity and variation prior to implementation and this reduces the risk of inconsistencies, and supports more informed, data-driven decision-making.

Updated on: 07/01/2026