Deriving In-Depth Knowledge from IT-Performance Data Simulations

Deriving In-Depth Knowledge from IT-Performance Data Simulations

Konstantin Petruch (Deutsche Telekom AG, Germany), Gerrit Tamm (SRH Hochschule, Germany) and Vladimir Stantchev (FOM Hochschule fuer Oekonomie und Management, Germany)
Copyright: © 2012 |Pages: 17
DOI: 10.4018/jksr.2012040102
OnDemand PDF Download:
$30.00
List Price: $37.50

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.
Article Preview

1. Introduction

The ubiquity and fast availability of computer-based processing systems allows enterprises and organizations to conduct a wide range of operative processes electronically. This results not only in improved processing with respect to time and quality, but also in an (more or less) automatically stored performance data about the processing (e.g., how many requests were processed, what was the duration of each processing task). Such data is often generated as a byproduct of these systems and is often neglected as a source for potential knowledge. Furthermore, data gathered by such operative systems often hides further nontrivial insights.

Visualization can be a first step towards a more in-depth data comprehension. This is the application domain of the business dashboards. One specific example is the usage of Google analytics (http://www.google.com/analytics/) to visualize web server log files. More complex application scenarios involve the aggregation of multiple data sources and the subsequent analytical processing of data within a data warehouse.

Data logged by operative transaction processing systems can provide insights about two distinct types of measurements key goal indicators (KGIs) which provide insights about the results of an operative task, and key performance indicators (KPIs) which define the way these results were achieved (e.g., speed, transaction rate). When we define such indicators with respect to specific business processes we can apply an approach known as process mining (Gerke & Tamm, 2009).

In this article (the article is an extended version of a paper we presented at WSKS 2011) we extend this approach as follows: (1) we aim to derive more in-depth knowledge with respect to both qualitative and quantitative assessments, and (2) we introduce and use more complex simulation techniques to accomplish this objective.

Our focus lies on KPIs and KGIs we address the question whether an extended data analysis using simulation methodologies can provide an additional value as compared to standard data visualization.

1.1. Work Structure

The rest of this article is structured as follows: Section 2 presents the state of the art in the measurement of process indicators, the terminology we use, as well as related work in the area of knowledge discovery. In Section 3 we posit our hypothesis and give an overview of our assessment framework for indicators in the area of IT operations. In Section 4 we describe our experimental results regarding a specific data transformation and simulation process that we have assessed based on real-life data from an international telecommunications provider. Section 5 contains a summary of our experimental results and outlook on our future research activities.

2. Preliminaries

In this section we present the motivation for performance and output metrics. We also discuss the possibilities for data and log file analysis in this context and also reflect on specific aspects of knowledge discovery and generation.

2.1. Concepts of Indicators

As stated, indicators can be generally divided into two groups key performance indicators (KPIs) and key goal indicators (KGIs). KPIs measure how well a process is performing and are expressed in precisely measurable terms. KGIs represent a description of the outcome of the process, often have a customer and financial focus and can be typically measured after the fact has occurred (Van Grembergen, 2003). While KGIs specify what should be achieved KPIs specify how it should be achieved.

Complete Article List

Search this Journal:
Reset
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing