Cognitive Computing to Optimize IT Services

TL;DR

This paper presents a cognitive computing platform that analyzes both structured and unstructured data from IT services to proactively identify issues, reduce ticket volume, and improve customer satisfaction through advanced NLP techniques.

Contribution

The paper introduces a comprehensive cognitive solution that integrates NLP-based analysis of unstructured data with traditional structured data analysis for IT service optimization.

Findings

Reduced ticket volume by 18-25% on average

Enhanced insights through NLP techniques like summarization and sentiment analysis

Improved operational efficiency and customer satisfaction

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.