Data Governance for Decision Support Systems focusing on a Telecommunications Company

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TABLE OF CONTENTS

LITERATURE REVIEW1

Decisions Support Systems (DSS)1

Evolution of Decision Support Systems and Recent Research3

Data Governance for Decision Support Systems6

DSS Frameworks and Concepts8

DSS Types17

DSS and Modelling Applications for Quality-Management18

REFERENCES26

LITERATURE REVIEW

Decisions Support Systems (DSS)

For the last 35 years, researchers and practitioners have conducted many studies or written on the subject of DSS (Golafshani 2003 597). Power (2002) defined DSS as interactive computer-based tool used since the 1960s by decision-makers to help answer questions, solve problems and support or refute conclusions. In 2005, Power extended the definition of DSS to include an interactive computer-based system or subsystem intended to help decision makers use communication technologies, data, documents, knowledge and/or models to identify and solve problems, complete decision process tasks, and make decisions.

The problem in understanding the term DSS lies not in the definition, but in the variety of systems falling under its realm. It is very difficult to gain a consensus on what constitutes application processing information to improve decision making. Liang and Huang (2000) stated DSS allows people at many different levels to systematically analyze problems before making a decision. For instance, managers in one industry use DSS for different reasons than managers in other industries. It is also important to understand the process of decision making from one organization to another. The sophistication of DSS varies in definition from user to user. However, users at every level acknowledge these systems extend the range decision-making process by organizing and assembling data into relevant and valuable information (Golafshani 2003 597).

A DSS can take many different forms and the term can be used in many different ways (Kumar and Palvia 2001 153). DSS assists management when decisions are unique, change rapidly, and are not specified easily in advance (Asemi and Zavareh 2011 164). DSS is very different from traditional IS in that they are based on fixed logic patterns and are mainly report generators. DSS function at the management level and analyze data for semi-structure and unstructured decision making. Asemi and Zavareh (2011) noted examples of DSS include “regional sales analysis, production scheduling, profitability analysis, and contract cost analysis” (Asemi and Zavareh 2011 164).

DSS is able to provide these analyses by uploading corporate data from enterprise systems such as enterprise resource systems (ERP), supply chain management (SCM), or customer relationship management (CRM). Yet, the question remains if these types of decision systems are truly used by users. A concern with DSS is its vulnerability to leaking information outside the organization. Nevertheless, as Wende & Otto (2006) pointed out “organizations can reduce information leaks by requiring employees to authenticate logins (password codes, using magnetically coded badges, or applying voiceprint analysis) and file levels (read-only, write-only, or read-and-write access) before logging into DSS” (Wende & Otto 2006 43). Another major area of concern is managers occasionally are not properly trained to use features available through DSS. This occurs when managers simply do not understand the features available to them for decision making and is ...