Profiling users online and directed online advertising has become a major business with companiessuch as Google and Facebook as frontier companies. Through incidents such as the CambridgeAnalytica scandal, the public has started to take notice of both the positive and the negative sides of thebusiness. The data given to companies with a data driven business model can make the user experiencemore personalized and therefore better. On the other hand the data collected could be seen as privacyreducing and exploitation of users. This study aims to foresee opportunities and new ways to develop adata driven business model which has the user's interests in mind and still remains profitable. Withempirical data through interviews and theories the study will show that data driven business modelshave big potential to be profitable and simultaneously make the user more aware or even make datadelivery in the user’s best interest. The main methods to do this is by implementing privacy dashboards,transparency and moving the pieces in the business model to make the user central in the businessmodel.

Over the past years, the value of data and DDDM have increased significantly as technological advancements have made it possible to store and analyze large amounts of data at a reasonable cost. This has resulted in completely new business models that has disrupt whole industries. DDDM allows businesses to rely their decisions on data, as opposed to on gut feeling. Up until this point, literature is eligible to provide a general view of what are the major challenges corporations encounter when implementing a DDDM strategy. However, as the field is still rather new, the challenges identified are yet very general and many corporations, especially B2B MNCs selling consumer goods, seem to struggle with this implementation. Hence, a single-case study on such a corporation, named Alpha, was carried out with the purpose to explore what are their major challenges in this process. Semi-structured interviews revealed evidence of four major findings, whereas, execution and organizational culture were supported in existing literature, however, two additional findings associated with organizational structure and consumer behavior data were discovered in the case of Alpha. Based on this, the conclusions drawn were that B2B MNCs selling consumer goods encounter the challenges of identifying local markets as frontrunners for strategies such as the one to become more data-driven, as well as the need to find a way to retrieve consumer behavior data. With these two main challenges identified, it can provide a starting point for managers when implementing DDDM strategies in B2B MNCs selling consumer goods in the future.

Business model innovation has in recent year become more important for firms looking to gain competitive advantage on dynamic markets. Additionally, incorporating data into a firm’s business model has been shown to lead to improved performance. This development has led to interest in the connection between data utilisation and business model innovation. This thesis provides an in-depth case study of a Swedish space firm active within the satellite industry. The firm operates within an increasingly dynamic market, and ongoing disruptions in the form of new market entrants and rapid technological advancements has led to a search for new business opportunities. As a result, novel ways of utilising the increased amounts of data from space are of significant importance. While the firm is still realising profits utilising their incumbent business model, the firm must simultaneously explore new business opportunities to avoid extinction. The findings show that novel data utilisation, in the form of data processing, leads to business model innovation. Furthermore, the degree of business model transformation is dependent on how many of the business model's underlying elements are affected by data utilisation. Furthermore, the study concludes that a lack of trial-and-error learning impedes radical innovation efforts and hinders the development of ambidextrous capabilities within the firm. Lastly, the study finds a novel connection between the introduction of large-scale projects and improved ambidextrous capabilities. / Innovation av affärsmodeller har under senare år blivit alltmer viktigt för företag som vill uppnå ökad konkurrenskraft på dynamiska marknader. Vidare har det visat sig att företag som använder data för att förändra sin affärsmodell når bättre resultat än sina konkurrenter. Detta har lett till ett intresse för kopplingen mellan datautnyttjande och innovation av affärsmodeller. Detta examensarbete består av en fallstudie av ett svenskt rymdföretag, som har del av sin verksamhet inom satellitbranschen. Företaget verkar på en alltmer dynamisk marknad, och pågående störningar i form av nya marknadsaktörer och tekniska framsteg har lett till att företaget nu måste söka efter nya affärsmöjligheter. Som ett resultat av detta blir nya sätt att använda de ökade mängderna data från rymden av stor betydelse. Fastän företaget fortfarande framgångsrikt nyttjar sin befintliga affärsmodell, måste företaget samtidigt undersöka nya affärsmöjligheter för att undvika att hamna efter marknadsutvecklingen. Studiens resultat visar att nya sätt att använda data, i form av databehandling, leder till innovation av företagets affärsmodell. Dessutom beror graden av innovation på hur många av affärsmodellens underliggande byggstenar som påverkas av införandet av data. Studien drar vidare slutsatsen att en brist på lärande genom ’trial-and-error’ inom företaget hindrar radikala innovationsinsatser och leder till begränsade förutsättningar för att hantera organisatorisk ambidexteritet. Slutligen finner studien att storskaliga innovationsprojekt kan förbättra förutsättningarna för organisatorisk ambidexteritet.

Background: The business model (BM) is an essential part of firms and it needs to be innovated continuously to allow firms to stay or become competitive. The process of business model innovation (BMI) unfolds incrementally by re-designing or developing new activities in order to provide value propositions (VP). With increasing availability of data, pressure on BMI to orchestrate their activities towards putting data as a key resource and develop data-driven business models (DDBM) is growing. Problematization: The DDBM provides valuable possibilities by utilizing data to optimize current businesses and create new VPs. However, the development process of DDBMs is outlined as challenging and scarcely reviewed. Purpose: This study aims to explore how a data-driven business model development process looks. More specifically, we adopted this research question: What are the phases and activities of a DDBM development process, and what characterizes this process? Method: This is a qualitative study in which the empirical data was collected through 9 semi-structured interviews where the respondents were divided among three different initiatives. Empirical Findings: This study enriches the existing literature of BMI in general and data-driven business model innovation in particular. Concretely, this study contributes to the process perspective of DDBM development. It helps to unpack the complexity of data engagement in business model development and provides a visual process model as an artefact that shows the anatomy of the process. Additionally, this study resonates with value logics manifestation through the states of artefacts, activities, and cognitions. Conclusions: This study concludes that the DDBM development process is structured with two phases as low data-related and high data-related activities, inheriting seven sub-phases consisting of different activities. Also, this study identified four underlying characteristics of the DDBM development process comprising value co-creation, iterative experiment, ethical and regulatory risk, and adaptable strategy. Future research: Further work is needed to explain the anatomy and structure of the DDBM development process in different contexts to uncover if it captures various complexities of data and increases its generalizability. Furthermore, more research is required to differentiate between different business models and consequently customizing the development process for each type. Future research can also further explore the value co-creation in developing DDBM. In this direction, it would be interesting to consider connecting the field of open innovation to the field of DDBM and, specifically, its role in the DDBMs development process. Another promising avenue for future research would be to go beyond the focus on merely improving the VP to maximize the data monetization, and instead focus on the interplay and role that data has on sustainability.