Problem

Our team was facing a problem where there was no mechanism in place to track the usage of automation tools. This made it difficult to determine which tools were being used, how often they were being used, and how many cases were being processed with how much time to process it. As a result, we were unable to determine the usage of the tool and how many cases tools are not able to run and determined time being taken to execute for better capacity planning.

This lack of visibility into our automation tools was causing inefficiencies in our processes and making it difficult to optimize our workflows. We needed a solution that would provide us with insights into how our automation tools were being used so we could make data-driven decisions about their usage.

Solution

To address this problem, we decided to implement a Python dashboard that would track the usage of automation tools. The logs from these tools would be stored in an RDMS database, providing us with real-time information about their usage.

The dashboard would provide us with insights into which automation tools were being used, how often they were being used, and the number of processes executed along with the time taken. Additionally, it would also help us identify cases that were out of scope for our automation tools.

Impact

Efficient use of resources: With insights into how our automation tools are being used, we can ensure that they are being utilized efficiently. This will help us optimize our workflows and reduce the time spent on manual tasks.
Increased productivity: The dashboard provides insights into how automation tools can be used to automate tasks, which will help us reduce the amount of time that employees spend on manual tasks. This will increase productivity and allow us to focus on more strategic initiatives.

"The Python dashboard had an incredible impact on our team. It helped us to improve the efficiency of our workflows, increase productivity, and make better decisions about the use of our automation tools."

Team

Team Member #1 - Data Analyst
- Developed the Python dashboard to track automation tool usage
- Implemented the RDMS database to store logs from automation tools
Team Member #2 - Software Engineer
- Built the backend infrastructure for the Python dashboard
- Integrated the dashboard with existing automation tools

Python Dashboard Improves Automation Tool Tracking