Big Data Analytics & Decision Making

Mathias Kampf Andersen & Frederik Lange Poulsen

Student thesis: Master thesis

Abstract

Purpose – In recent years we have seen a digitalization of society and corporations all over the world. Organizations have started integrating big data analytics, affecting multiple aspects of their business processes. Thus, the purpose of this master thesis was to investigate how the development and integration of big data analytics in organizations are influencing organizational decision-making processes and to clarify possible impacts and implications these technological developments have on current and accepted theoretical frameworks of organizational decision-making.

Methodology – This master thesis is a theoretical study of how big data analytics are influencing organizational decision-making and existing theoretical frameworks. The empirical foundation of the thesis has been a combination of a single-case study of Novo Nordisk and the usage and impacts of the big social data analytics tool Sprinklr in the Corporate Communication department. The problem statement was examined through 8 expert interviews with interviewees from Novo Nordisk, Sprinklr and leading universities such as University of Michigan, Copenhagen Business School and Denmark’s Technical University. The epistemological social constructivist approach has been applied as the theoretical pivotal point, which recognize that no objective reality exists and that the findings therefore originate in a specific context. However, using the inductive method and reflecting upon accepted theoretical perspectives this thesis aims to put forth a contribution to existing theory.

Theory – The theoretical foundation of this study includes generally accepted theoretical perspectives around decision making, including Simon Herbert’s bounded rationality, James G. March’s behavioral theory of the firm, Karl Weick’s sensemaking and Ikujiro Nonaka’s theory on dynamic knowledge production. Furthermore, pioneering theories and reflections on big data analytics’ effect on decision making are applied to the theoretical framework, using eg. Cristiano Ciappei, Maria Dinque, Marijn Janssen and Haiko van der Voort.

Findings & conclusions – The main findings of this thesis are contributionsto existing theoretical perspectives on organizational decision making, clarifying the impacts and implications that big data analytics have on organizational decision making. The first contribution to existing theory is the model “The 7 principles of organizational decision making in data-driven organizations” that articulates how big data analytics have expanded the bounded rationality that organizations act within and how the decision-making process have been decentralized. Furthermore, it was shown how big data analytics can act as a tool of organizational decision power and how this in some situations can create a false security. Organizations must acknowledge that there is a distinction between data and knowledge and as long as human interaction is a part of the data processing, the knowledge conversion from big data will be subjective. Finally, our model “the data-driven decision-making process” clarifies how big data analytics have made the decision-making process more dynamic and non-hierarchical. Thus, we argue how wisdom originated from prior decision-making affects the sensemaking of environmental signs and how the human knowledge creation process will go back and forth between the data collection and the knowledge conversion, creating a more dynamic relationship between the two.

EducationsMSc in Organisational Communication, (Graduate Programme) Final Thesis
LanguageDanish
Publication date2019
Number of pages226
SupervisorsHenrik Køhler Simonsen