Key Issues and the Requirements for an Effective Enterprise Decision-Making Using an Ontology-Based GOAL-Framework for Evaluation of the Organizational Goals Achievement

Key Issues and the Requirements for an Effective Enterprise Decision-Making Using an Ontology-Based GOAL-Framework for Evaluation of the Organizational Goals Achievement

Tengku Adil Tengku Izhar (Universiti Teknologi MARA, Shah Alam, Malaysia), Torab Torabi (La Trobe University, Melbourne, Australia) and M. Ishaq Bhatti (La Trobe University, Melbourne, Australia)
Copyright: © 2019 |Pages: 22
DOI: 10.4018/IJKBO.2019040102

Abstract

This article proposes the GOAL-Framework for the evaluation of organizational goals based on an ontology. The aim is to capture and analyze relevant data for the organization goals because determining relevant data is a key to delivering value from massive amounts of data for better decision-making in relation to the organizational goals achievement. The framework will allow the domain experts and entrepreneurs to evaluate relevant organizational data to assist the decision-making process with respect to the organizational goals. Hence, they will be able to identify to what extent certain organizational goals could be achieved. In order to test the flexibility and applicability of the GOAL-Framework, a case study is presented to explain how the framework is implemented and applied to a real world situation. The outcome of the case study demonstrates that the framework can be applied for analysis and decision-making based on the metrics using the dashboard to evaluate the extent to which the organizational goals could be achieved.
Article Preview
Top

Introduction

CEOs of organizations have certain requirements of the data they access as they use this data to support the decision-making process. Especially in today’s technological age, organizations accumulate a huge amount of data. However, information technology has developed beyond the storage, transmission and processing of data (Seng & Chen, 2010). Organizations rely on resources such as data, information and knowledge for business plan, the design of business strategies and decision-making for the specific organization priorities. Organizational resources, such as organizational data, must be relevant to assist the decision-making process in order to evaluate the extent to which the organizational goals could be achieved (Izhar et al., 2013). Typically, relevant data for decision-making is extracted from the organizational data sources (Romero & Abello, 2010). Therefore, organizations should have the ability to manage their resources (Omerzel & Antoncic, 2008; Schalenkamp & Smith, 2008; Smith et al., 2007). However, the growth in the amount of the organizational resources available nowadays poses major difficulties as well as challenges to decision-making (Mikroyannidis & Theodoulidis, 2010).

There is a shortcoming when it comes to evaluating organizational data in relation to the organizational goals during the development of organizational modelling. Modelling the organizational goals is limited to business processes and organizational processes (Fox et al., 1996; Fox et al., 1998; Mansingh et al., 2009; Rao et al., 2012; Sharma & Osei-Bryson, 2008). Most of the previous studies focus on process modelling, workflow analysis, computer-supported cooperative work and design problem solving (Popova & Sharpanskykh, 2011; Zhang & Yao, 2015). Despite this shortfall, there are a number of tools for modelling organizational processes most of which support mathematical modelling on organizational performance (Vergidis et al., 2008; Arun, 2015). Structuring a small organization is less complicated than a large organization. This is because different organizational structures, processes and a vast amount of data make it more difficult to identify relevant organizational data in relation to the organizational goals. Therefore, it is also important to identify metrics that can measure the relevance of organizational data in relation to the organizational goals.

An ontology provides explicit and formal specifications of knowledge, especially implicit or hidden knowledge (Cho et al., 2006). An ontology is considered as an approach to support data dependencies (Pundt & Bishr, 2002). Therefore, an ontology assists the creation of knowledge to develop a model in relation to the organizational goals and can be used to improve the communication and collaboration between the decision makers and the users (Selma et al., 2012), which is, in this research, the decision makers in relation to the organizational goals (Izhar et al., 2013). However, in many ontology studies, there is a lack of studies reporting on such metrics in relation to the organizational goals (Rao et al., 2012; Valiente et al., 2012). We suggest that metrics is important to enable both domain experts and entrepreneurs to evaluate the relevance of organizational data in relation to the organizational goals (Izhar et al., 2013) and measure the value of the analysed organizational data. Furthermore, the organizational goals ontology assists domain experts to apply such knowledge in relation to the organizational goals (Izhar et al., 2013).

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 10: 4 Issues (2020): 1 Released, 3 Forthcoming
Volume 9: 4 Issues (2019)
Volume 8: 4 Issues (2018)
Volume 7: 4 Issues (2017)
Volume 6: 4 Issues (2016)
Volume 5: 4 Issues (2015)
Volume 4: 4 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing