Big Data Analytics & beslutningstagning: Et teoretisk speciale med inddragelse af Novo Nordisk

Abstract

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

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

Theory –The theoretical foundation of this study includes generally acceptedtheoretical perspectives around decision making, including Simon Herbert’sbounded rationality, James G. March’s behavioral theory of the firm, KarlWeick’s sensemaking and Ikujiro Nonaka’s theory on dynamic knowledgeproduction. Furthermore, pioneering theories and reflections on big dataanalytics’ effect on decision making are applied to the theoretical framework,using eg. Cristiano Ciappei, Maria Dinque, Marijn Janssen and Haiko van derVoort.

Findings& conclusions – The main findings of this thesis are contributionstoexisting theoretical perspectives on organizational decision making, clarifyingthe impacts and implications that big data analytics have on organizationaldecision making. The first contribution to existing theory is the model “The 7principles of organizational decision making in data-driven organizations” thatarticulates how big data analytics have expanded the bounded rationality thatorganizations act within and how the decision-making process have beendecentralized. Furthermore, it was shown how big data analytics can act as atool of organizational decision power and how this in some situations cancreate a false security. Organizations must acknowledge that there is adistinction between data and knowledge and as long as human interaction is apart of the data processing, the knowledge conversion from big data will besubjective. Finally, our model “the data-driven decision-making process”clarifies how big data analytics have made the decision-making process moredynamic and non-hierarchical. Thus, we argue how wisdom originated from priordecision-making affects the sensemaking of environmental signs and how thehuman knowledge creation process will go back and forth between the datacollection and the knowledge conversion, creating a more dynamic relationshipbetween the two.