DxC received 300,000 change and service requests from DB. All the requests were written in free text. DxC sales team required to categorize the changes to bill DB in an accurate and speedy way. We compared multiple Machine Learning algorithms to gain the best accuracy on classifications. Delivered a classification model with 85?curacy, resulting in a 15% increase in recurring annual revenue.

DxC DB account receives approximately 250,000 incidents per month. It was required to reduce the incidents volume by analyzing those and create problem tickets based on identified root cause. Worked on incidents categorization. Identified bulk, duplicate and repetitive incidents and present those to the SMEs.

As a preventive analysis, we correlated the incidents with change request, space utilization and network operations. Predicted top CI could have been the top polluter based on historical data. Overall Indecent reduction 30%.

DxC finance team required to match a good number of keywords through some websites for risk analysis.