Special Interest Group Data-driven Work Innovation
Organisation team
Coordinator
Core team
Objective
In our daily work, we are all involved in different work processes, such as processing student information, scheduling meetings, asking for travel expense reimbursements, and hiring new employees. Technological advancements made it possible to store large volumes of process data in the information systems that support these work processes. The challenge is to analyze, interpret, and turn these process data into real value.
Process mining is the discipline that combines approaches for learning from process data to understand and improve work processes. For this, process mining brings together traditional model-based process analysis and data-centric techniques by linking data science and process science [1]. In this context, the objective of our SIG Data-driven Work Innovation is to exchange knowledge within the university about Process Mining, Process Analytics, and Business Process Management (BPM).
The focus areas of this SIG are the following:
- Evidence-based process transparency
- Process diagnosis and analytics: bottleneck and workaround analysis (Check our Workaround mining lab)
- Process redesign and improvement
- Performance and compliance of processes
Furthermore, any initiative that is related to work processes is in the scope of this SIG.
Aims
- Create awareness on turning process data into value within the university and beyond
- Stimulate collaboration between researchers and staff to initiate projects understanding and improving work processes
- Disseminate the knowledge and experience on process mining applications
- Familiarize researchers with the potential benefits of process mining in their field
Stakeholders in a data-driven work innovation initiative
The figure below (adapted from “Stakeholders in the BPM lifecycle” explained in [2]) summarizes the roles in an organization that are involved in improving work processes by learning from data.
Professor Hajo Reijers talks about improving work processes by learning from data in one of the Future of Work seminar series.
Participate
The SIG is open to all researchers, practitioners, and support staff at UU or UMCU who are interested in data-driven work innovation. If you would like to be updated about the activities of this SIG, please register for our newsletter.
Contact
For further inquiries, please contact Ünal Aksu
[1] Van der Aalst, W.M.P., “Process mining: data science in action”, 2nd ed., Springer, Heidelberg, 2016.
[2] Dumas, M., La Rosa, M., Mendling, J., & Reijers, H.A., “Fundamentals of business process management”, 2nd ed., Springer, 2018.
