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Deriving In-Depth Knowledge from IT-Performance Data Simulations

  • 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 dataKnowledge 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.show moreshow less
Metadaten
Author: Konstantin Petruch, Prof. Dr. Gerrit Tamm, Prof. Dr. Vladimir Stantchev
Parent Title (English):International Journal of Knowledge Society Research (IJKSR)
Document Type:Article
Language:English
Date of Publication (online):2014/03/06
Publishing Institution:SRH Berlin University of Applied Sciences
Release Date:2014/03/06
Tag:Cloud Governance; IT Governance; Knowledge Extension; Simulation; System Dynamics
Volume:3
Issue:2
First Page:13
Last Page:29
Note:
Online: https://www.researchgate.net/publication/276183994_Deriving_In-Depth_Knowledge_from_IT-Performance_Data_Simulations
Link author profile:https://www.srh-berlin.de/hochschule/hochschulteam/tamm-gerrit/
Link author profile:https://www.srh-berlin.de/hochschule/hochschulteam/stantchev-vladimir/
Peer-reviewed:Keine Angabe - No details
Institutes:Institute of Information Systems (IIS, SRH Berlin)
JEL-Classification:O Economic Development, Technological Change, and Growth
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung
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