Tractchun IT Helpdesk Analytics
Tractchun IT Helpdesk Analytics
A Power BI dashboard tracking 5 years of IT support tickets across two manufacturing plants; performance, satisfaction, and agent ranking in one place.
A Power BI dashboard tracking 5 years of IT support tickets across two manufacturing plants; performance, satisfaction, and agent ranking in one place.
Tractchun's IT support team handled thousands of tickets across two manufacturing plants over five years, but that history was spread across five separate yearly Excel files, plus three more reference tables for agents, employees, and job positions. Without a single view, it was hard to answer basic management questions: which categories of problems are most common, how long tickets stay open, how satisfied employees are with the support they get, and, critically, which agents are performing well and which ones need support.
Consolidated 5 years of raw data. Combined the yearly ticket files (2016–2020) into a single table using Power Query, joining in agent, employee, and job-position reference tables to add context (plant, shift, department) to every ticket.
Modeled the data as a star schema, with a dedicated date dimension table (built with Power Query M) to support clean time-based filtering and analysis.
Built DAX measures for the core KPIs: total tickets, tickets per agent, average satisfaction, average days open, and a ranking measure (RANKX) to score and rank agents.
Designed two report pages:
An Overview page with KPI cards, a ticket trend over time, a satisfaction breakdown, tickets by category, and tickets grouped by days-to-resolution — with a toggle to segment everything by ticket type (Problema vs. Solicitud).
An Agent Ranking page highlighting the top 3 performing agents (with photos) and a scrollable, sortable ranking table for the rest, using bookmarks to switch between "Ranking Agentes" and "Last Places" views.
Added interactive filtering by year, month, and agent (with search), so any stakeholder can slice the data to the period or agent they care about.
For example, filtering to January 2020 alone: 2,191 tickets were logged, averaging 44 tickets per agent, a 4.17/5 average satisfaction score, and 4.56 average days open. 83.2% of rated tickets came back positive (Excelente/Bueno). The largest category was System issues (833 tickets), followed by Access/Login (649); and most tickets (785) were resolved within 0–1 days, showing the team generally responds fast, even though a smaller tail of tickets drags on for 10+ days.
On the agent side, the ranking view surfaced clear top performers, like Alberto Gastelum (54 tickets, 4.78 avg. satisfaction), making it easy for managers to recognize strong agents and spot who might need coaching, instead of digging through raw ticket logs.