Fishy business : closing the gap between data-driven decision-making (DDM) and aquaculture : an analysis of incumbents in the norwegian aquaculture industry (NAI) and the use of big data for competitive advantage

Enterprises and industries are becoming heavily reliant upon data-driven decision-making (DDM) to maintain their competitive edge. Artificial Intelligence (AI), Machine Learning (ML) and sensor-technology through the Internet of Things (IoT) are allowing businesses to mitigate uncertainty central to...

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Bibliographic Details
Main Author: Evensen, Thomas
Other Authors: Rajsingh, Peter
Format: Master Thesis
Language:English
Published: 2020
Subjects:
Online Access:http://hdl.handle.net/10400.14/29687
Description
Summary:Enterprises and industries are becoming heavily reliant upon data-driven decision-making (DDM) to maintain their competitive edge. Artificial Intelligence (AI), Machine Learning (ML) and sensor-technology through the Internet of Things (IoT) are allowing businesses to mitigate uncertainty central to operations and increase overall business agility. As such new and ground-breaking disruptive innovations (DI) make their way into an industry, incumbents tend to fall victim to what Clayton Christensen term The Innovator’s Dilemma (TID). Somewhat naturally, as such technology does not yield a clear return on investment (ROI), incumbents tend to rather favour solutions catered to increasing efficiency and saving costs. The risk, however, is that addressing DI is substantial to maintaining a competitive advantage in the long run. The overall aim of this study was to investigate whether or not the Norwegian Aquaculture Industry (NAI) is falling victim to TID. An assumption of how the NAI has fallen behind in utilizing DDM emerged, and was tested through qualitatively gathered data in cross-sectional interviews. Our findings suggest a prominent misalignment between the NAI and what the field of DDM has to offer. Incumbents within the NAI is falling victim to TID, in which they view DDM as more of a supplement rather than the crucial investment it may show to be. Another reason as to why they are not addressing DDM more urgently, is because they cannot. The quality of data gathered within the NAI is simply too fragmented and poor in order to successfully implement DDM in their operations today. As empresas e os setores estão a tornar-se fortemente dependentes da tomada de decisão baseada em dados (DDM) de forma a manter a sua vantagem competitiva. Inteligência Artificial (IA), Machine Learning (ML) e tecnologia de sensores através da Internet das Coisas (IoT) estão a permitir que as empresas diminuam a incerteza central das operações e aumentem a produtividade geral dos seus negócios. À medida que estas inovações disruptivas (ID) chegam à indústria, os operadores tendem a ser vítimas do que Clayton Christensen apelida de “O Dilema da Inovação” (ODI). De certa forma, como esta tecnologia não gera um claro retorno sobre o investimento (ROI), os operadores tendem a dar preferência a soluções de aumento de eficiência e de contenção de custos. O ID é, no entanto, substancial para manter uma vantagem competitiva a longo prazo. O objetivo geral deste estudo foi perceber se a Indústria Aquícola Norueguesa (IAN) está ou não a ser vítima de ODI, sendo testado por meio de dados qualitativamente coletados em entrevistas transversais. As descobertas sugerem um desalinhamento entre o IAN e o que o campo do DDM oferece. Os operadores dentro do IAN são vítimas de ODI, nos quais consideram o DDM mais como um complemento do que o importante investimento que pode vir a ser. O DDM não está a ser abordado com mais urgência devido ao facto de tal ainda não ser possível. A qualidade dos dados coletados no IAN é ainda muito fragmentada para implementar com sucesso o DDM nas suas operações.