Machine Learning Initiative: Enterprise Text Mining & Pattern Recognition for Operational Intelligence

🎯 Problem

300,000+ ServiceNow incidents contained valuable operational insights buried in unstructured text data. Without systematic analysis, patterns in system failures, user issues, and process bottlenecks remained invisible to leadership.

😖 Pain Points

• Critical patterns hidden in massive text datasets
• No systematic approach to incident categorization
• Manual review was time-intensive and inconsistent
• Limited visibility into operational themes and trends

🧪 Research & Process

• Data preprocessing: Cleaned and normalized 300k+ enterprise incident records using Python NLP techniques
• Mixed-methods analysis: Combined text processing pipeline (SpaCy, NLTK, TF-IDF) with quantitative metrics analysis (priority levels, urgency ratings, resolution time, reassignment counts)
• Unsupervised learning: Implemented K-means clustering on both textual and numerical features to identify natural incident groupings
• Topic modeling: Used LDA (Latent Dirichlet Allocation) to extract thematic patterns from incident descriptions
• Statistical analysis: Analyzed resolution time distributions, reassignment patterns, and priority escalation trends
• Visualization: Created Python word clouds, ServiceNow Performance Analytics dashboards, classification (LDA) and cluster (K-means) visualizations for stakeholder communication
• Statistical validation: Applied scikit-learn for model evaluation and pattern verification

✅ Outcome

• Identified distinct operational failure patterns previously unknown to leadership
• Revealed seasonal trends in incident types enabling proactive resource planning
• Created data foundation for predictive maintenance strategies