Cognitive Computing to Optimize IT Services
- URL: http://arxiv.org/abs/2201.02737v1
- Date: Tue, 28 Dec 2021 09:56:44 GMT
- Title: Cognitive Computing to Optimize IT Services
- Authors: Abbas Raza Ali
- Abstract summary: A Cognitive solution goes beyond the traditional structured data analysis by deep analyses of both structured and unstructured text.
In experiments, upto 18-25% of yearly ticket volume has been reduced using the proposed approach.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, the challenges of maintaining a healthy IT operational
environment have been addressed by proactively analyzing IT Service Desk
tickets, customer satisfaction surveys, and social media data. A Cognitive
solution goes beyond the traditional structured data analysis by deep analyses
of both structured and unstructured text. The salient features of the proposed
platform include language identification, translation, hierarchical extraction
of the most frequently occurring topics, entities and their relationships, text
summarization, sentiments, and knowledge extraction from the unstructured text
using Natural Language Processing techniques. Moreover, the insights from
unstructured text combined with structured data allow the development of
various classification, segmentation, and time-series forecasting use-cases on
the incident, problem, and change datasets. Further, the text and predictive
insights together with raw data are used for visualization and exploration of
actionable insights on a rich and interactive dashboard. However, it is hard
not only to find these insights using traditional structured data analysis but
it might also take a very long time to discover them, especially while dealing
with a massive amount of unstructured data. By taking action on these insights,
organizations can benefit from a significant reduction of ticket volume,
reduced operational costs, and increased customer satisfaction. In various
experiments, on average, upto 18-25% of yearly ticket volume has been reduced
using the proposed approach.
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