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Cheat Sheet Case Study

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The data provided consists of information on 100,000 user submitted tickets. Each ticket consisted of: the requestor, their seniority, the IT Owner, the area the ticket was filed against, the type of ticket, the user assigned severity, the IT assigned priority, and the number of day between submission and resolution.
I analyzed the data using IBM’s Watson Analytics tool. Initial assessment with this tool provided a strong correlation between the type of ticket and the number of days open. Specifically, out of all of the ticket types, hardware was open for a significantly longer period of time than either the systems or the software tickets. Although Watson did not find what it would consider to be other strong correlations within the data, a deeper analysis revealed a correlation between how IT prioritized a ticket and the seniority of the requestor. The first item of note that Watson discovered was that hardware tickets were open far longer than tickets classified as systems or software. The average time open for a …show more content…

When IT receives the ticket, they evaluate the ticket, and reassign the priority based on their initial analysis. It appears that the user’s seniority is a significant influencer on the IT assigned priority. 49% of the junior level critical tickets were reassigned a low priority. This is in stark contrast to the 1% of management-submitted critical rickets that received a low priority by IT. Not only did IT lower the priority based on the user’s seniority, but they also raised the priority based on the user’s seniority. 85% of the management tickets that were submitted as minor were assigned a high priority, while only 5% of the minor level tickets submitted by junior level staffers were elevated to high priority by the IT staff. IT consistently changed the priority of tickets based on seniority, regardless of the user submitted

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