```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) library(xlsx) library(readxl) library(dplyr) library(reshape2) library(ggplot2) library(knitr) library(imager) library(corrplot) ``` --- title: "HelpDesk Performance Analysis" author: "Thomas Zeutschler" date: "6/7/2017" output: html_document --- ## A. Business Understanding ### A.1 Business Problem The User Help Desk Team has received negative user feedback for our newly introduced & centralized IT Help Desk service. As for the acceptance of the overall IT department, IT support quality and efficiency and high user satisfaction is mission critical an understanding of root cause is required. ### A.2 Purpose and Goal of Data Analysis Understand negative user feedback: - Analyse availabe data from the new help desk service to identify and outline potential root cause for negative user feedback. - What causes high priority tickets? - Propose a list of (at least 3) instant actions for improving service quality. ```{r loading, include=FALSE} HelpDeskData <- read_excel("/Users/zeutsch/Documents/08-HSD/2018.SS/IT_in_Business_Analytics/Lecture07_UseCaseHelpDesk/HelpDeskData.xlsx") hdraw <- read_excel("/Users/zeutsch/Documents/08-HSD/2018.SS/IT_in_Business_Analytics/Lecture07_UseCaseHelpDesk/HelpDeskDataRaw.xlsx") ``` ## A. Data Understanding ### A.2 File Contents and Structure The User Help Desk Team has provided an Excel workbook (*HelpDeskData.xlsx*) containing the latest 10.000 records from the new ticketing system. The following table show the structure of the data file. ```{r, echo=FALSE} kable(HelpDeskData[1:5,], caption = "Table 1.: First 8 records of help desk data file:") ``` **Field Description** 1. **TicketID** ID of the help desk ticket 2. **Requestor** ID of the requesting user 3. **RequestorSeniority** Seniority of the requestor (junior to management) 4. **ITOwner** ID of the owner of the IT system or application 5. **FiledAgainst** Category of the ticket (login, systems, software, hardware) 6. **TicketType** Type of ticket (Issue or Request) 7. **Severity** Business impact of the request or issue 8. **Priority** Priority of the ticket. 9. **daysOpen** Number of days the ticket was open (unsolved) 10. **Satisfaction** User satisfaction with the resolution. ```{r, echo=FALSE} #summary(HelpDeskData) #str(HelpDeskData) summary(hdraw) str(hdraw) corrplot(cor(hdraw, use="pairwise.complete.obs") ) ``` ## C. Findings ## **What causes high priority tickets?** A high amount of high priority tickets (36% of all tickets) is caused by managers (33% of all high priority tickets). 1/3 of all high priority are failed against access/login issues, but 40% where caused by issues with systems. Regarding severity, 90% of of high priority tickets have "normal" severity. **How can we improve the ticket resolution time?** For Hardware issues, only a small amount of tickets can be resolved within the first 5 days. The mayority of the tickets need 6 to 12 days. This is due to the fact that all longer tickets are not issues but change request which require approvak or implementation. **Proposal:** Maybe redu ce lead time for change requests. If possible. Also for "Systems" most long running tickets are related to chamnge requests And also for "Software" the same pattern applys. Root cause might be the provisioing of new software. **Proposal:** Automated Software deployment tool. Redesign Approbal process to speed up. ## Proposal for 3 instant actions for improving service quality.## **Action 1 - ** Evalute approval process. Check if certain aproval processes can be automated (e.g. Software deployment) **Action 2 - ** System Access/login: Think of self service tool to reduce workload. A potential maxi. reduction by 29% of ticket is available. **Action 3 - ** Think abaout maximal Priority löevels for differenrt ticket types. (e.g. do not allow high priority ticket for lost passwords).