Creating value from mobility-driven big data - Exploring the possibilities of value creation in big data for an actor with mobility supporting solutions as their main product

Examensarbete för masterexamen

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Type: Examensarbete för masterexamen
Master Thesis
Title: Creating value from mobility-driven big data - Exploring the possibilities of value creation in big data for an actor with mobility supporting solutions as their main product
Authors: Buchholz, Andrea
Fredrik, Söderbergh
Abstract: In a world where the amount of data produce from connected products is increasing rapidly, there is a great possibility for a company to get insights and create value from this data. The aim with this thesis was therefore to create value models for companies within mobility-driven solutions in handling their mobility-driven big data. The narrowing into mobility-driven big data was to understand the movement and actions made by the user and the effect on the movement on the product. The term mobility-driven big data is defined in this thesis as: Mobility-driven big data is collected from mobility supporting solutions such as cars, busses, trains, airplanes or other forms of transportation. On top of this, there is a demand for large volume, variety and velocity of data that requires new innovative ways to analyze the information available in order to answer the specific questions in a real time setting. Through a broad theory review, interviews with knowledgable actors as well as a deep dive into a case at a company pursuing this endevour - five important axes where identified to answer two highly relevant questions to answer in creating value from mobility-driven big data. The first question is “How do we collect data, and what data are we interested in collecting ourselves?” and the second question is “Why is this data relevant, and what possibilities and consequences does this create?” The overlaying axes is that of data policy and the implications of not having a static application. The two frameworks that came from the other four axes were analyzed on the basis of a SWOT analysis and patterns of behaviour and possibilities were identified - where it is important to note that the shift on an action between fields is of great interest. Through analysing the frameworks we came to the conclusion that to make use of mobility-driven big data and gain value from it there needs to be a more open data policy in place. Many companies today apply a relatively closed data policy in order to protect itself from the competition. This is in our opinion a mistake and the companies in question need to adopt a more dynamic policy which adapts itself to the situation at hand instead of being static. Allowing access to a system and sharing data will in turn lead to more beneficial partnerships with multiple co-dependencies which would be inhibited by static policies. This in turn will create opportunities and maximize the value of the data at hand by allowing external sources to create unexpected and innovative ways of using the data. As Alan Turing would put it: “Sometimes it is the people no one imagines anything of who do the things no one can imagine.” - Alan Turing (Grossman and Tyldum, 2014)
Keywords: Transport;Övrig industriell teknik och ekonomi;Transport;Other industrial engineering and economics
Issue Date: 2017
Publisher: Chalmers tekniska högskola / Institutionen för teknikens ekonomi och organisation
Chalmers University of Technology / Department of Technology Management and Economics
Series/Report no.: Master thesis. E - Department of Technology Management and Economics, Chalmers University of Technology, Göteborg, Sweden : E2017:060
Collection:Examensarbeten för masterexamen // Master Theses

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