Text Analytics Index for Incident Root Cause Identification
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Solution Overview
Problem
Current methods for determining the root cause of customer service or manufacturing issues are inefficient due to reliance on historical data analysis and inexperienced representatives, leading to unnecessary latency and ineffective troubleshooting.
Innovation Solution
A system utilizing text analytics and a dictionary-based approach to analyze change records, generating an index of analyzed data that correlates candidate causes with the time frame of incidents, enabling quick identification of root causes through a user-friendly interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional historical warranty claims data analysis is used, then root cause determination is performed, but unnecessary latency is introduced
Solution Approach 1:
The system performs preliminary text analytics on change records to generate an indexed database of changes with associated time frames before incidents occur. This pre-processed index enables rapid querying and correlation when incidents are reported, eliminating the need to analyze raw historical data from scratch during troubleshooting.
Solution Approach 2:
The system creates a simplified copy or representation of the complex historical change records by generating an indexed database structure that captures essential change information. This indexed copy can be quickly searched and correlated with incident data without requiring access to the full complexity of original records.
2Reliability
If customer service representatives rely on experience and knowledge, then root cause determination is attempted, but effectiveness varies by representative expertise
Solution Approach 1:
The system performs self-service by automatically analyzing change records, generating indexes, and correlating incidents with potential root causes without requiring human expertise. The automated text analytics and correlation algorithms consistently process data to identify root causes, eliminating variability associated with different representative knowledge levels.
Solution Approach 2:
The system introduces an intermediary layer between incident reporting and root cause determination. This intermediary automatically processes incident data against the change record index using text analytics and correlation algorithms, providing consistent and reliable root cause identification regardless of the representative's expertise.
3Measurement precision
If detailed analysis of change records is performed, then accurate root cause identification is achieved, but processing complexity increases
Solution Approach 1:
The system segments the complex task of root cause analysis into distinct phases: text analytics phase to extract meaningful information from change records, indexing phase to organize extracted data with time frame metadata, and correlation phase to match incidents with potential causes. This segmentation reduces processing complexity at each stage while maintaining overall accuracy.
Data Source
AI summary
According to one embodiment of the present invention, a system analyzes one or more change records based on text analytics using dictionaries and rules for the analysis in order to generate an index of analyzed data that represents the one or more change records. The change records each include a change and corresponding time frame for occurrence of the change. Information from a request is applied to the index of analyzed data to determine one or more candidate causes for the incident and the corresponding time frame for occurrence of the change. A time associated with the request is correlated with the corresponding time frame for occurrence of the change to identify the one or more candidate causes in the one or more change records as causes for the incident. Embodiments of the present invention further include a method and computer program product for determining causes of an incident.