The transformation of data towards knowledge in eyes of the Positivist and the Interpretivist Author: Sean Imamkhan Student number: 11394404 University of Amsterdam - Faculty of Science (FNWI) Thesis Master Information Studies: Business Information Systems (BIS) Final version: 17-08-2018 Supervisor: dhr. ir. A.M. (Loek) Stolwijk Examiner: dhr. drs. A. (Arjan) Vreeken Abstract. Positivism and interpretivism are respected epistemological standpoints, which are concerned with the question of ‘how to come to knowledge’ regarding society. The positivist uses the natural science approach to come to their knowledge of society, where the interpretivist uses a more humanistic approach of coming to their knowledge of society. In this study, the widely recognized DIKW hierarchy is been applied to the standpoints of the positivist and the interpretivist. The DIKW hierarchy states that data generate information, information generate knowledge and knowledge generate wisdom. Furthermore, as a frame of reference of how the positivist and interpretivist transform data into knowledge, data, information and knowledge are also separately defined as objects from the ontological positions of objectivism and subjectivism. Both ontology and epistemology refer to the definition of knowledge. Ontology is concerned with the question ‘knowledge of the existence of objects in the world’. The objectivist believes objects in the world exist apart from the social actor, where the subjectivist believes objects in world exist interdependent of the social actor. The ontological positions facilitate the transformation process of data towards knowledge, in the eyes of the positivist and the interpretivist. The findings of this study shows that the positivist follows a linear approach in coming to knowledge from information and data, this mean data, information and knowledge are following each other up in the transformation process, which is well in line with the assumption of the DIKW hierarchy. The interpretivist follows a non-linear approach in coming to knowledge from information and data, which mean there is no clear view of the transformation elements; data, information and knowledge in what is following each other up. In this study, the line between data and information is thin or even blurred, in case of the interpretivist. Keywords. data, information, knowledge, data to knowledge, data transformation to knowledge, positivist, interpretivist, positivism, interpretivism, knowledge perspectives, objectivist, subjectivist objectivism, subjectivism, ontology, epistemology, ontological, epistemological, DIKW hierarchy, DIKW pyramid, DIKW assumption, DIKW, DIK, generation of knowledge, knowledge as input. Table of Contents 1. Introduction 1 2. Literature Review 2 2.1. DIKW 2 2.2. Definition of Knowledge 5 2.2.1. Epistemology (Positivism & Interpretivism) 6 2.2.2. Ontology (Objectivism & Subjectivism) 7 2.3. Bridge to Research Questions (relevance research) 8 2.4. Research Questions 8 2.5. Conceptual Framework 10 3. Methodology 11 4. Results 14 4.1. What is Data? 14 4.2. What is Information? 15 4.3. What is Knowledge? 16 4.4. How is Data been achieved? 18 4.5. How is Information been achieved? 19 4.6. How is Knowledge been achieved? 20 4.7. The transformation of data towards knowledge in eyes of the Positivist and the Interpretivist 21 5. Conclusion 22 6. Discussion, Limitations and Future research 23 6.1. Scientific Implication (contribution of this research) 23 6.2. Practical Implication (contribution of this research) 24 6.3. Counterargument (a side-view) 24 References 26 Annex A: The Information Theory of Shannon 31 1. Introduction What is knowledge? What is data? And what leads to the development of data to knowledge? Is data really a precursor in the hierarchy towards knowledge (Rowley, 2007)? Or is data founded on the knowledge of the human mind (Tuomi, 1999)? What constitutes knowledge? The definition of knowledge is quite hard to pin down given the different perspectives on ‘what do we consider to be knowledge’ (Henriques, 2013; Rowley, 2007). Henriques (2013) mentioned that the oldest concept of knowledge refers to the theory of: Justified True Belief (JTB), stated by the Greek philosopher Plato. The JTB theory consist of: a mental representation about a state of affairs that corresponds to the actual state of affairs. This mean the actual state is true and the representation can be validated by logical and empirical factors of the believer (Henriques, 2013). But what is data? And how does data relate to information? Data from a computer point-of-view, is something in its digital form, where it is been presented by binary values for transporting and showing the data or information on-screen (Rouse, 2017). This concept is based on the work of the father of information theory: Claude E. Shannon. Shannon prescribes data and information as logistics processes, where splitting the information in the smallest possible chunks of data (i.e. bits having the ability of possessing only two values; 0 or 1), plays a fundamental role in sending and receiving the (total) information (Jha, 2016). Russell Ackoff, a professor in organizational change and a system theorist, describes data as symbols that contain properties of objects and events. Information is something that consist out of processed data (Ackoff, 1999). Ackoff (1999) mention an example of that idea by illustrating the census taking concept. Census takers collect data and convert the results into tables. The converting step is where the data is been processed into information, in this case by presenting it in tables. As stated before, there are different ways in approaching the concept of data, information and knowledge. In this research, the focus is on the scientific perspective of a positivist and an interpretivist, regarding the transformation of data towards knowledge. According to Huizing (2007), the positivist perspective on knowledge is ubiquitous in knowledge and information theories. However, a millennium went over in discussing what knowledge really means, resulting in an unclear definition of knowledge. Therefore, the question of how to come to knowledge has always been a respected branch of philosophy (Huizing, 2007). Positivism and interpretivism are both scientific standpoints, or disciplines of how to come to knowledge (Bryman, 2012). In 2007, Rowley found out that there is a less clear unified concession on the processes which leads to the transformation of data towards knowledge. According to her findings it is not clear if data, information and knowledge can be approached as three distinct concepts (‘objects’). In contrast however, Rowley (2007) mentioned that a certain relationship can be established from data to information, and from information to knowledge, hence at two levels instead of three. Data towards information is explained in terms of structuring data for attaining meaningfulness, usefulness, relevance and value to the data. Information towards knowledge is defined by 1 understanding the operationalization or actionability of information (Rowley, 2007), for example, understanding how to put the information in practice (e.g. organizations). In this research, the focus also lies in the transformation of data towards knowledge, but then studied in the eyes of the positivist and the interpretivist. The researcher is curious how those scientific standpoints come to knowledge, when data and information as (distinct) objects are been taking as precursors towards their knowledge claims. How do the positivist and the interpretivist come to know something? When the well-recognized assumption (Rowley, 2007) is taken that data generate information and information generate knowledge (Ackoff, 1989, 1999). In section 2.4. the elaborated research questions can be found. Furthermore, this research is structured as follows: In the next chapter (2. Literature Review) the hierarchical development of data towards knowledge is explained. After that, the notions of positivism and interpretivism are further described, as they are scientific perspectives of how to come to knowledge, and the main subject of this study. Also, the ontology theory is explained, as it acts as a frame of reference and facilitator of how the positivist and interpretivist transform data into knowledge. Finally, the research questions along with the conceptual framework follows from that. In the chapter after the literature review (3. Methodology), the focus is on the methodology that is been used to gather the results of this research. This includes: A literature research is executed to 2 positivism and 2 interpretivism studies, regarding the transformation of data towards knowledge. Also, the ontology definitions of data, information and knowledge are studied in the literature to facilitate that transformation process. In chapter 4 (4. Results), the outcomes of the research questions are construed, which answers how the positivist and interpretivist transform data into knowledge, funded on the ontological definitions of what is data, information and knowledge. Chapter 5 (5. Conclusion), provides a short summary and a conclusion of this study. The conclusion unfolds that the positivist and the interpretivist both have their own way of coming to knowledge, from having information and data as precursors towards that generation of knowledge. The positivist follows a linear approach, which mean data, information and knowledge are following each other up in the transformation. The interpretivist follows a non-linear approach,