Optimizing Data Quality Issues in Process Mining to Maximize Valuable Customer Service

Process mining is an early adolescent bird in the field of research. Process mining is constantly increasing with the amount of data recorded, including the data about events that provides detailed information on the history of the processes, necessity to improve and holds business processes in the aggressive and swiftly changing environment. Data mining and Process mining are complement to each other. Data mining differs from Process Mining in such a way that it operates with the data in general, whereas Process mining deals with the data about events. Process mining relaxes between Big Data with Data Mining on one side, and Business Process Modeling with Analysis on the other. Process Mining consists of Automated Process Detection (bringing out the process models from event logs), Conformance Checking (monitoring deviation by comparing model and event logs), defining the Organizational Structure, forecasting the Process Behavior in order to Extend Recommendations on the basis of the Process History. It is an important need for the Effective Customer Service which is relying on the Quality Issues over data, e.g., issues related to timestamps in event logs, event granularity, ambiguity in activity names, etc., Identification and analysis of these issues to maximize the consolidated efforts for valuable customer service is of primary importance in Business Process. Hence a mathematical model is conceived to Maximize Customer Service by optimizing Data Quality Issues and is attributed in this paper.