Delegating responsibilities for ensuring data quality to employees close to operations is generally regarded as beneficial according to academia, rather than allowing the IT department full authority. The business employees are suitable for this task, as they have both a practical understanding for the business and a role that often is regarded as data-driven. However, delegation of responsibilities is argued to be one of the biggest barriers concerning data quality. This study examines this phenomenon by connecting delegation of responsibilities for ensuring data quality to research on the motivational aspects of controlling data, which lays the foundation for the empirical investigation.  This single case study was conducted qualitatively using semi-structured interviews. The data sample consisted of a business department, including six business controllers and their manager, at a large Swedish manufacturing company. Further, the data collection was complemented by conducting an interview with a representative at the IT department.  The study shows how business controllers are dependent on the quality of data, and due to their awareness of the benefits in avoiding data errors they are autonomously motivated to ensure good data quality. This strengthen the argument of delegating control of data quality to the business department, which gives the study practical significance.

The disruptive nature of innovations and technological advancements present potentially huge benefits, however, it is critical to take caution because they also come with challenges. This author holds fast to the school of thought which suggests that every organisation or society should properly evaluate innovations and their attendant challenges from a strategic perspective, before adopting them, or else could get blindsided by the after effects. Big Data is one of such innovations, currently trending within industry and academia. The instinctive nature of Organizations compels them to constantly find new ways to stay ahead of the competition. It is for this reason, that some incoherencies exist in the field of big data. While on the one hand, we have some Organizations rushing into implementing Big Data Projects, we also have in possibly equal measure, many other organisations that remain sceptical and uncertain of the benefits of "Big Data" in general and are also concerned with the implementation costs. What this has done is, create a huge focus on the area of Big Data Implementation. Literature reveals a good number of challenges around Big Data project implementations. For example, most Big Data projects are either abandoned or do not hit their expected target. Unfortunately, most IS literature has focused on implementation methodologies that are primarily focused on the data, resources, Big Data infrastructures, algorithms etc. Rather than leaving the incoherent space that exists to remain, this research seeks to collapse the space and open opportunities to harness and expand knowledge. Consequently, the research takes a slightly different standpoint by approaching Big Data implementation from a Strategic Perspective. The author emphasises the fact that focus should be shifted from going straight into implementing Big Data projects to first implementing a Big Data Strategy for the Organization. Before implementation, this strategy step will create the value proposition and identify deliverables to justify the project. To this end, the researcher combines an Alignment theory, with Digital Business Strategy theory to create a Big Data Strategy Framework that Organisations could use to align their business strategy with the Big Data project. The Framework was tested in two case studies, and the study resulted in the generation of the strategic Big Data Goals for both case studies. This Big Data Strategy framework aided the organisation in identifying the potential value that could be obtained from their Big Data project. These Strategic Big Data Goals can now be implemented in Big data Projects.

Organisationer har idag tillgång till stora mängder data som inte kan hanteras av traditionella Business Intelligence‐verktyg. Big data karakteriseras av stor volym, snabb hastighet och stor variation av data. För att hantera dessa karaktärer av data behöver organisationer upphandla ett Big data‐system för att ha möjlighet att utvinna värde. Många organisationer är medvetna om att investeringar i Big data kan bli lönsamma men vägen dit är otydlig. Studiens syfte är att undersöka vilka utmaningar organisationer står inför i samband med upphandling av ett Big data‐system och var i upphandlingsprocessen dessa utmaningar uppstår. Det empiriska materialet har samlats in från tre stora svenska företag och myndigheter som har upphandlat ett Big data‐ system. Analys av materialet har genomförts med Critical Incident Technique att identifiera utmaningar organisationer upplever i samband med upphandling av ett Big data‐system. I studiens resultat framgår det att organisationer upplever utmaningar med att förstå behovet av ett Big data‐system, skapa projektgruppen, välja projektmetod, skapa kravspecifikationen och hantera känslig och personlig data. / Organizations today have access to massive amounts of data that cannot be handled by traditional Business Intelligence tools. Big data is characterized by big volume, high velocity and variation. Organizations need to acquire a Big Data system, in order to handle the characteristics of the data and be able to generate business value. Today’s organizations are aware that investing in Big Data can be profitable but getting there is a challenge. The purpose of this study is to investigate the challenges the organizations may experience in the process of acquiring a Big Data system and when these challenges arise. The empirical data has been collected by interviewing three large Swedish companies and authorities which have acquired a Big Data system. The Critical Incident Technique has been used in order to identify the challenges which the organizations had experienced in the process of acquiring a Big Data system. The findings of the study shows that organizations experience challenges when they are understanding the need of the Big data‐system, creating the project team, choosing the project method, defining the requirements of the system and managing sensitive and personal data.

In recent years there has been an increasing interest in understanding how organizations can utilize big data in their process management to create value and improve their processes. This is due to new challenges for process management which have arisen from increasing competition and the complexity of large data sets due to technological advancements. These large data sets have been described by scholars as big data which involves data that are so complex traditional data analysis software are not sufficient in managing or analyzing them. Because of the complexity of handling such great volumes of data there is a big gap in practical examples where organizations have incorporated big data in their process management. Therefore, in order to fill relevant gaps and contribute to advancements in this field, this thesis will explore how big data can contribute to improved process management. Hence, the aim of this thesis entailed investigating how, why and to what extent big data is used in process management. As well as to outline the purpose and challenges of using big data in process management. This was accomplished through a literature review and a survey, respectively, in order to understand how big data had previously been used to create value and improve processes in organizations. From the extensive literature review, an analysis matrix of how big data is used in process management is provided through the intersections between big data and process management dimensions. The analysis matrix showed that most of the instances in which big data was used in process management were in process analysis & improvement and process control & agility. Simply put, organizations used big data in specific activities involved in process management but not in a holistic manner. Furthermore, the limited findings from the survey indicate that the main challenges and purposes of big data use in Swedish organizations are the complexity of handling data and making statistically better decisions, respectively.