Student Theses and Projects: Leveraging Big Data Analytics: Implications of Applying Process Mining in Organizational Contexts
Thesis (MA/DA) - Reference number 2019-203
Advisor(s): Julia Eggers
Recent years have marked a paradigm shift in the way organisations leverage data for decision making (Davenport, 2006). While organisations used to collect structured data in discrete time intervals for preconceived organisational goals, nowadays, organisations are confronted with vast amounts of unstructured, heterogeneous data that originate in high velocity from various sources inside and outside the firm (Jones, 2019). In this era of Big Data organisations are challenged to leverage Big Data Analytics (BDA) to optimise business processes constantly through extracting and interpreting process data for decision making (Constantiou and Kallinikos, 2015). Therefore, process mining has received increased attention during the last decade. Process mining is a BDA technique aimed at discovering, monitoring and improving real business processes by analysing large amounts of event data that are readily available in today’s information systems (Van Der Aalst, 2011). The practical relevance of the technology is reflected by the German process mining start up Celonis with a valuation of over $1 billion.
However, the pathways from implementing BDA techniques, such as process mining, to realising business value are still unknown (Sharma et al., 2014). Research is still lacking knowledge on how organisations can design, implement and use BDA IT artefacts to derive knowledge and contribute to business value (Abbasi et al., 2016, Sharma et al., 2014). The aim of this thesis is therefore to investigate on one hand, how organisations introduce and utilize process mining to contribute to business value and, on the other hand, how the technology is impacting the organisational and social context it is embedded.
Potential research questions include, but are not limited to:
- How do organisations develop antecedents necessary to successfully implement process mining?
- How is the use of process mining impacting established decision-making processes in organisations (e.g. intuition-based vs. data-driven)?
- How are managers and analysts deriving insights from process mining?
- How are ethical considerations impacting the design and usage of process mining artefacts?
These are general tasks that we will adapt together to the specific research questions:
- Conduct a literature review in the domain of business value realisation from BDA and process mining
- Identify and conduct a case study on how an organisation is implementing and utilizing process mining
- Conduct and analyse at least 10 expert interviews at the case partner
- Synthesize recommendations/usage patterns/process model
- High degree of autonomy and individual responsibility
- Interest and experience in qualitative research
- Very good grades and a good command of English language are beneficial
The topic can be adapted according to your interests. The thesis can be written in English or German. If you have any further questions, please do not hesitate to contact me directly. Please send your application including our application form, "Notenauszug" from TUMonline, and CV to firstname.lastname@example.org. Please note that we can only consider applications with complete documents.
Abbasi, A., S. Sarker and R. H. Chiang (2016). "Big data research in information systems: Toward an inclusive research agenda." Journal of the Association for Information Systems 17 (2), I.
Constantiou, I. D. and J. Kallinikos (2015). "New games, new rules: big data and the changing context of strategy." Journal of Information Technology 30 (1), 44-57.
Davenport, T. H. (2006). "Competing on analytics." Harvard Business Review 84 (1), 98-107.
Jones, M. (2019). "What we talk about when we talk about (big) data." The Journal of Strategic Information Systems 28 (1), 3-16.
Sharma, R., S. Mithas and A. Kankanhalli (2014). "Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations." European Journal of Information Systems 23 (4), 433-441.
Van Der Aalst, W. (2011). Process mining: discovery, conformance and enhancement of business processes. Heidelberg: Springer.