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Data-Driven Disruption of Established Industries in the Era of Big Data on the Example of the Automotive Industry

Written by L. Clarenbach

Grade 1,3

Paper category

Master Thesis

Subject

Business Administration>Management

Year

2021

Abstract

Master Thesis Data-driven Disruption: Hypothesis 1: Data is the new „natural resource“ and an essential input factor in new business models. Hypothesis 2: Data-driven business models have the potential to disrupt entire industries and to overthrow well-established industry players. Hypothesis 3: There are already several industries which were disrupted by new market entrants or newer competitors exploiting new data-driven business models by leveraging data as the superior resource, giving them a competitive advantage and new monetization models. In these best practices are several lessons that OEMs in other well-established industries, that are vulnerable to future disruption, can utilize to better their future market position. Hypothesis 4: Data will also be the new essential resource and factor in establishing new models in the automotive industry and the established players (VW, BMW etc.) won ́t have another option than to adapt new business models which make mainly use of self-collected (in the car) or third-party data (from advertisers e.g. to combine it with own data). Disruption of well-established industries Established competitors who are market leaders or dominant players in a well- established industry are called incumbents. As a result of the developments described above, these incumbents are increasingly facing new challenges in various industries in the form of innovative start-ups and technologically advanced global corporations from outside the industry. (Hirt & Wilmott, 2014; Wirtz, 2019, p. 176, p. 181; Jong et al., 2015; Jong & van Dijk, 2015). Established companies in particular should therefore not focus solely on their current cash cows, as is traditionally recommended, but rather constantly critically examine their business models to identify disruptive potential at an early stage and act on it faster than these new or existing competitors (Abbosh et al., 2018; Gassmann et al., 2020, p. 8). ndustry disruption is defined as “a process whereby a smaller company with fewer resources is able to successfully challenge established incumbent businesses” (Abbosh et al., 2018). The term can be traced back to Schumpeter ́s theory of creative destruction, which states that innovators, in creating a new product or service with a new combination of resources, necessarily displace and thereby destroy the old, now obsolete (Kurz & Sturn, 2011, pp. 109-110; Schumpeter, 1942 after Wirtz, 2020, p. 13). 9 In the following, "incumbents" will be used synonymously with "long-established companies in an established industry" according to the definition presented. This thesis focuses on the disruption of established industries, defined as the mature phase in the industry life cycle. In the mature phase, companies are settled in the market, certainty is high and the market grows to an predictable extent. The mature industry is signified by established structures, partnerships and processes. Innovating activities are in comparison to the previous phases of the industry life cycle low and focus more on incremental than major improvements. (Williamson, 1975, pp. 215-216). One of the most common false beliefs or myths about industry disruption is that the occurrence of disruption is a random and unforeseeable event, as such that actively seeking to disrupt an industry is not in the hand of the individual companies (Abbosh et al., 2018; Gassmann et al., 2020, pp. 13-14). In fact, companies are advised to actively initiate disruption by seeking out new business models rather than passively waiting for developments in the industry (Christensen et al., 2016). The authors of Accenture ́s “Disruptability Index” (Abbosh et al., 2019) define four different development stages of industries based on their state of disruption and future probability to be disrupted. For this thesis most relevant is the stage of vulnerability, which is defined in the study by a “lack of innovation and insufficient investment”. In this vulnerability to disruption phase, the established industry players rely too much on the so far sufficiently high barriers to market entry, which formerly prevented disruption by smaller players with less investment power. These well-established industries are at risk of disruption because of their long history in the market and accumulated assets, structures and processes that served them well so far. As presented in section 2.1. above, companies at a later stage of maturity, that are categorized as vulnerable, focus mainly on efficiency rather than creation. That means they will tend to double down on already made investments instead of seeking out new opportunities and thus miss out on new and disrupting developments in the industry (Christensen et al., 2016; Jong & van Dijk, 2015). Initially, the new players entering the market and creating new service offerings don ́t disrupt the market on a large scale, the established companies thus don ́t act quickly enough on the threat and fail to invest in new ideas that will secure their market share and success in the long-term future (Abbosh et al., 2018). Data-driven business models 3.1. Data as a resource & data science The rapid expansion of the global available data mass and the resulting changing user expectations push companies still using traditional business models in recent years to revisit and innovate their approach (Otto et al., 2019, p. 22). Globally, the amount of annual data creation, capture, copy and consumption is predicted to reach 149 zettabytes or 149 billion terabytes in 2024, which is an increase of more than 150% compared to 2020. Looking even a little more than five years back from today, the total data volume of 2015 made up just over 26% of the amount recorded in 2020 (IDC & Statista, 2020). This development can be attributed to the rapid development of Internet of Things (IoT) systems, which enable the inexpensive collection of increasingly large amounts of data without time delays (Farboodi & Veldkamp, 2021, p. 2; Opher et al., 2016, p. 2). Big data is commonly characterized by the “three Vs”, which are “volume”, ”velocity” and “variety”. Volume describes the above presented mass of generated structured and unstructured data, velocity the processing and analysis speed possible today. Variety is concerned with the diverse types of available data sources and formats, which have to be connected and put into context. Newer definitions also include the elements "veracity", which represents the need for sufficiently valid data quality, and "value", which describes the concrete business value of the data (Boobier, 2016; Buhl et al., 2013; Knorre, 2020, p. 6; Kornwachs, 2020, p. 5). This thesis focuses specifically on big data in the sense of “data which is being analyzed by machine learning, AI or new big data technologies” (Farboodi & Veldkamp, 2021, p. 2), because the data volume exceeds the capabilities of conventional data processing (Kornwachs, 2020, p. 5). Today it is argued in multiple publications that big data in the context of business analytics has become an essential production factor, thus the new natural resource in the information age of business in the 21st century, it is also described as “the new oil” (Brownlow et al., 2015, p. 1; Müller, 2018, p. VII; Opher et al., 2016, p. 2). The most valuable companies in the world like for example Google and Amazon employ data- driven business models, those companies are proven to be more efficient with resources and more effective in their business strategies (Opher et al., 2016, p. 8; Farboodi & Veldkamp, 2021, p. 1; Gottlieb & Weinberg, 2019). The need for businesses to actively curate their data strategy to not fall behind in the modern digital economy was recognized and predicted to rapidly increase in the following years already in 2014 by the European Commission. In a communication paper aimed at the European Parliament, the European Council and the European Economic Committee among others, data was declared to be at “the centre of the future knowledge economy and society” (European Commission, 2014, pp. 4-5) and the necessity to develop the ecosystem to move “towards a thriving data-driven economy” was emphasized. Research by Marshall, Mueck & Shockley (2015, p. 32) on data & analytics in the context of business innovation found out that data is an essential key element of industry leaders to facilitate sustained business growth even in unstable environments through enhanced understanding of customers. The report further revealed that “organizations using big data and analytics within their innovation processes are 36% more likely to beat their competitors in terms of revenue growth and operating efficiency”, this finding was also supported by, among others a McKinsey report in 2019 (Gottlieb & Weinberg). Read Less