An ontology-based goal-framework for evaluation of organizational goals achievement

Submission note: A thesis submitted in total fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Engineering and Mathematical Sciences, College of Science, Health and Engineering, La Trobe University, Bundoora.

Today, people have access to more data in a single day than most people had in the previous decade. The problem is that data is found in many different forms which makes it almost impossible to understand the existing relationships between different data elements. For example, government agencies and large, medium and small private enterprises in many domains, such as engineering, education and manufacturing, are drowning in an ever increasing deluge of data. This is because they create and collect massive amounts of data in their daily business activities. Thus, having an ability to analyse data in a timely fashion can ensure businesses have a competitive edge to improve productivity in relation to their organizational goals. Data is the most important asset to assist the decision-making process and achieve the organizational goals. However, the trustworthiness of organizational data in relation to the organizational goals is often questionable due to the huge amount of data within the organization. Therefore, it is difficult to identify the dependency relationship between organizational data and organizational goals. As a result, some of this data are not relevant to the organizational goals. Even though professionals, such as data analysts, are trained to analyse data, the increased amount of organizational data has become a major challenge to achieve the organizational goals. The aim of this research is to propose a methodology to analyse organizational data in order to evaluate the extent to which the organizational goals could be achieved. In order to achieve this aim, we develop a framework, named the GOAL-Framework which is based on an ontology. This 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. The GOAL-Framework is associated with the organizational goals ontology and aims to provide a step-by-step process to evaluate the extent to which the organizational goals could be achieved. This involves identifying the dependency relationship between organizational goal elements. After we identify the organizational goal elements, we develop the dependency relationship between organizational data elements and organizational goals. Metrics is defined for this dependency to evaluate relevant organizational data in relation to the organizational goals. We design and develop a tool in the framework to assist domain experts with the application of the framework. This tool provides the flexibility to identify which goals are to be evaluated and to identify the relevant organizational data from the huge amount of datasets that relate to the organizational goals. The GOAL-Framework can be customized and adjusted without being affected by changes to the organizational goals and organizational data. This is because organizational goals might be changed from time to time with organization create new data every day. In order to test the flexibility and applicability of the GOAL-Framework, two case studies are presented to explain how the framework is implemented and applied to a real world situation. The flexibility of the framework allows for diverse application to various organizational fields which range from small scale (La Trobe University, Australia) to large scale (the Australian economy). For example, in case study 2, we analyse the data to define the weight to evaluate which business size contributes more to the Australian economy based on the goals in terms of the number of people employed and value added. The outcome of the case studies 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.