Deriving In-Depth Knowledge from IT-Performance Data Simulations

Abstract

Knowledge of behavioral patterns of a system can contribute to an optimized management and governance of the same system, or of similar systems. While human experience often manifests itself as intuition, intuition can be notoriously misleading, particularly in the case of quantitative data and subtle relations between different data sets. This article augments managerial intuition with knowledge derived from a specific byproduct of automated transaction processing performance and log data of the processing software. More specifically, the authors consider data generated by incident management and ticketing systems within IT support departments. The authors’ approach utilizes a rigorous analysis methodology based on System Dynamics. This allows for identifying real causalities and hidden dependencies between different datasets. The authors can then use them to derive and assemble knowledge bases for improved management and governance in this context. This approach is able to provide more in depth insights as compared to typical data visualization and dashboard techniques. In the experimental results section, the authors demonstrate the feasibility of the approach. It is applied on real life datasets and log files from an international telecommunication provider and considered different improvements in management and governance that result from it.