Analysis Of IT Help Desk Data Using IBM Watson Analytics

Question: Discuss about the Analysis of IT Help Desk Data Using IBM Watson Analytics. Answer: Introduction With the implementation of the IT technologies in the business operations, it is more essential to improve the satisfaction level of the customers. There exist various problems at the cultural, technical and manageable level at the origination that is both avoidable and unavoidable in nature. The report focuses on analyzing a large set of data obtained from the service desk about the every ticket that was generated regarding any issues or incident. In this particular report, 100,000 tickets were taken into considerations for analyzing the underlying relation between them and how they impact the customer satisfaction of the organization. For the effective and quick analysis of the large set of data, IBM Watson Analytics tools have been utilized. About IBM Watson Analytics tool The IBM Watson Analytics is one of the significant cloud applications utilized for the processing and analyzing of a high volume of data. Moreover, the Watson Analytics tools assist in sophisticated visualization, data analysis and effective environment for the well collaboration and communication of the data (Guidi et al., 2016). IBM Watson Foundations have integrated the Analytics technology and IBMs portfolio of Big Data into their architecture. Figure 1: IBM Watson Analytics Logo (Source: IBM Australia 2016) The Watson Analytics tools break the barriers of dependency in the hardware resources and infrastructure of for analyzing the data. According to Miller, (2016), the IBM Watson Analytics, is defined as the collaboration of the natural-language generation, query and data discovery with a potential impact on the future generation of the user experience of the data dictionary. The Watson Analytics tool is utilized for enhancing the performance management in predictive modeling, operational reporting, narrative reporting, analytics and forecasting of data. The application of the Watson Analytics in the large volume of data assists in automatically revealing the hidden patterns from both structured and unstructured data. In addition to that, the Watson Analytics tool also helps in explaining the relationships between the data and provides a vivid view of the cause and effect correlation between them (Demirkan, Delen, 2013). The performance management system of IBM, when combined with the Watson Analytics, assists any business organization with improvement in workforce planning, revenue forecasting, capital expenditure, profitability modeling and scenario planning capabilities. The application of the IBM analytics assists in the better understanding of the data and revealing the underlying information within the data. The IBM Watson Analytics offers a beneficial way of conducting advanced analysis eradicating the complexity in the procedures. The data discovery application is available on the cloud that allows automatic predictive analysis, data exploration, creations of infographics and dashboards. The IBM Watson Analytics tools provide a visual decision within minutes of data entry. Explanation about data and variables In this particular report, the information about the 1,000,000 tickets has been taken into consideration. The high volume of data was utilized with the assistance of the Watson Analytics Tools. The dataset consists of ten variables. The different variables in the dataset are ticket, Requestor, RequestorSeniority, ITOwner, FiledAgainst, TicketType, Severity, Priority, daysOpen and Satisfaction. The ticket variables describe the ID of the ticket raised in the organization where the requestor describes the ID for the person who raised the particular ticket. The RequestorSiniority of the ticket was defined based on four variables namely 1-Junior, 2-Regular, 3-senior, and 4-management. The ITOwner variable in the dataset is the value representing the numeric number. The FiledAgainst data field represents the different domain where the ticket was raised. The different variable in the field were Systems, Software, Access/Login and Hardware. The ticket type is represented by issue or request field. The Severity field represents the severity of the ticket that represented by 1-minor, 2-normal, 3-major and 4-critical. The priority represents the priority of the tickets where 0-unassigned, 1-low, 2-medium, 3-High. The Days Open variable represents the number f days for which the ticket was kept open, and the satisfaction variable defines the satisfaction level of the customers. Analysis of satisfaction level Figure 2: Analysis of satisfaction level (Source: Created by Author in Watson Analytics) The above graph represents the satisfaction level of the customer against the tickets escalated for an issue or problem in the business. From 100,000 tickets, only less than 30,000 escalated against issues have been able to achieve highly satisfied customer response. On the other hand, more than 20,000 of the total escalated tickets have failed to achieve customer satisfaction. Raising the issue against any problems was easily tracked by the tickets raised with them. From the analysis of the data, dissatisfaction level of the customer is much high in the organization, but it is reflected that the organization can achieve the customer satisfaction rough the maintenance of the tickets. It is essential to understand the need and requirement of the customer and resolve the issue as quickly as possible to gain customer satisfaction. Relationship between Functional area the ticket was filed and satisfaction level Figure 3: Relationship between Functional area the ticket was filed and satisfaction level (Source: Created by Author in Watson Analytics) The satisfaction level of the customer against every issue raised for any problem has been broken down into a particular area of functionality within the organization. The issues raised against the system related issues in the business have the maximum satisfaction level. The system domain' is encountered for a maximum number of tickets. The system functionality is followed by the Access/login problems in the business. The number of tickets raised in the hardware domain is much less compared to any other domain, but the satisfaction level of the tickets are also much low. The issues related to the software are moderate in the business, but the customer satisfaction level is much low. Therefore, the IT implementation service team needs to focus more on the systems and access login functionality domain to reducing the time taken to resolve the issue and increase the satisfaction level of the customers. Relationship between Functional area the ticket was filed and severity level Figure 4: Relationship between Functional area the ticket was filed and severity level (Source: Created by Author in Watson Analytics) The application of the IBM analytics assisted in deriving the relationship between the severity level and the functional area where the ticket was filled. From the above diagram it can be stated that the severity level of a maximum of the raised tickets is normal for all the systems, software, and hardware and login domain of the IT implementation of the business. The rest of the tickets raised in every functional domain are every less composed of different severity level. In addition to that, the above picture depicts that the severity of the system issues has the maximum number of tickets raised. On the other hand, the number of tickets is followed by the severity of the tickets in the Access/login domain. Moreover, the hardware domain in encountered for the less number of tickets with less severity followed by the software domain. Relationship between Functional area the ticket was filed and priority level Figure 5: Relationship between Functional area the ticket was filed and priority level (Source: Created by Author in Watson Analytics) The priority levels of the tickets are assigned to for solving the problems as soon as possible. The customer satisfaction is dependent on the effectiveness of resolving the issues. The above graphical representation shows that the maximum high priority has been assigned to tickets regarding the system issues. At the same time, the tickets with unassigned priority are also acknowledged in the system domain. The numbers of high priority tickets are followed by the Access/Login domain in the system. Throughout all the domains in the business, the numbers of unassigned tickets are almost same as the number of tickets of high priority. Although the number of tickets raised in the hardware domain is low, maximum numbers of tickets are allocated to highest priorities. The unassigned priorities of the tickets are maximum in the entire functional domain considered together. Therefore it is essential to resolve the high priority tickets raised in every domain. Apart from that, the Access/Logi n domain of the business has the second highest high priority ticket level. Therefore, it can be predicted that the issues raised in the access/login functionality are critical and requires immediate actions. Ticket type vs. numbers of days open Figure 6: Ticket type vs. numbers of days open (Source: Created by Author in Watson Analytics) The above graph represents the number of tickets of its type with the number of days open. The request ticket type consists of the maximum number of tickets raised. The issue related tickets are much less compared to the request type of tickets raised in the organization. The previous analysis of the tickets has shown that the customer satisfaction is much higher in terms of tickets raised. Moreover, the tickets rose with issues if kept for a longer period of times unattained or unresolved has a negative impact on the customer satisfaction. The request tickets are kept open for longer days that can be easily managed and resolved. Relationship between numbers of days opens vs. satisfaction level Figure 6: Relationship between numbers of days open vs. satisfaction level (Source: Created by Author in Watson Analytics) The above-stated graph reflects that the satisfaction level of the customers is related to the number of days the tickets remained kept. Most of the tickets raised were closed in 0 days from opening. But the above graph shows that the in spite of the maximum number of tickets being closed in zero says, the satisfaction levels of the customer are not high. The tickets with dissatisfaction are also being closed at zero days from creating. In many cases, the tickets are remained open for many days for achieving customer satisfaction. But, the extended number of days of ticket open can also lead to the cause of dissatisfaction of the customer. Conclusion The analysis of the data with the utilization of the IBM Watson Analytics tools helped in quick and easy understanding and revealing the underlying relations between data. The dissatisfactory level of the customers is high in the System domain. On the other hand, the number of tickets with severity has also been filed in the System domain. In spite of the high priority assigned a maximum number of tickets in the system domain, it fails to provide effective and sufficient solutions to the issues that impact the customer satisfaction. Therefore, it is essential for IT service providers to provide special attention to improving the system functionality. The high priority of the tickets raised in both the system and access/login domain are prominent in the IT system. The management needs to take more attention and care in effectively resolving the tickets with high priority. The number of days taken for resolving a ticket impacts the customer satisfaction. The business management needs to ensure that the raised tickets need to be resolved as soon as possible. Further, the number of days the ticket being open are impacted the customer satisfaction level. Therefore, it is essential to ensure the tickets IT department needs to ensure a quick and effective solution for the every ticket raised for ensuring high satisfaction of the customers. References Demirkan, H., Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412-421. IBM - Australia. (2016).Ibm.com. Retrieved 30 August 2016, from https://www.ibm.com/au-en/ Miller, J. D. (2016). Learning IBM Watson Analytics. Chen, Y., Argentinis, J. E., Weber, G. (2016). IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. Clinical therapeutics, 38(4), 688-701. Gandomi, A., Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. Guided and automated analytics from the cloud. (2016).IBM Watson Analytics. Retrieved 30 August 2016, from https://watson.analytics.ibmcloud.com/product?S_PKG=AWcm_mmc=Search_Google-_-IBM+Analytics_AP+Horizontal+Watson+Analytics-_-WW_IN-_-+watson++analytics+beta_Broad_AWcm_mmca1=000000WBcm_mmca2=10000752mkwid=3c9fa265-14dd-48b9-9ea9-d48b7662bc4c|510|90774088658 Guidi, G., Miniati, R., Mazzola, M., Iadanza, E. (2016). Case Study: IBM Watson Analytics Cloud Platform as Analytics-as-a-Service System for Heart Failure Early Detection. Future Internet, 8(3), 32. Koyama, T., Jones, S., Utro, F., Ma, Y., Rhrissorrakrai, K., Shen, Y., ... Norel, R. (2015, May). Implementation of Watson Genomic Analytics processing to improve the efficiency of interpreting whole genome sequencing data on patients with advanced cancers. In ASCO Annual Meeting Proceedings (Vol. 33, No. 15_suppl, p. e12549). Kozub, V., Alexandrov, A. (2016). Application of ibm watson for the best speciality option prediction for prospective students. Murtaza, S. S., Lak, P., Bener, A., Pischdotchian, A. (2016, January). How to effectively train IBM Watson: Classroom experience. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 1663-1670). IEEE. Suchanek, F. M., Weikum, G. (2014). Knowledge bases in the age of big data analytics. Proceedings of the VLDB Endowment, 7(13), 1713-1714. van den Heever, S. (2015). Decision Optimization: the Key Differentiator for Next-Generation Analytics. Zadrozny, W. W., Gallagher, S., Shalaby, W., Avadhani, A. (2015, February). Simulating IBM Watson in the classroom. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 72-77). ACM. Zhu, W. D. J., Foyle, B., Gagn, D., Gupta, V., Magdalen, J., Mundi, A. S., ... Triska, M. (2014). IBM Watson Content Analytics: Discovering Actionable Insight from Your Content. IBM Redbooks.