Dissecting IT Tickets & Agents

In this project, I established a database within PostgreSQL by importing the provided .csv file. Later, I transformed the flat table into dimension and fact tables, constructing a star schema within PostgreSQL. The Entity Relationship Diagram (ERD) of the database and the star schema can be referred to in the comments.

I imported this database into PowerBI, where I employed a range of time intelligence functions to present Year-over-Year (YoY), Quarter-over-Quarter (QoQ), and Month-over-Month (MoM) changes. These functions were also instrumental in highlighting positive and negative variations compared to the previous year (PY) in Key Performance Indicators (KPIs).

The dashboard firstly examines the tickets across various categories, highlighting relationship between significant categories. The goal was to identify actionable steps for enhancing Customer Satisfaction Score (CSAT).

Furthermore, the dashboard displays ticket trends by day of the week and Service Level Agreement (SLA) trends over distinct time intervals. This visualization underscores the importance of optimizing resource scheduling, providing training for existing agents, and potentially expanding the team.

Lastly, the dashboard offers an agent performance overview.

Questions or any thing you'd have done differently with this dataset? Do let me know in the comments.