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Recent Advances in Data-Driven Business Process Management

Lars Ackermann, Martin Käppel, Laura Marcus, Linda Moder, Sebastian Dunzer, Markus Hornsteiner, Annina Liessmann, Yorck Zisgen, Philip Empl, Lukas-Valentin Herm, Nicolas Neis, Julian Neuberger, Leo Poss, Myriam Schaschek, Sven Weinzierl, Niklas Wördehoff, Stefan Jablonski, Agnes Koschmider, Wolfgang Kratsch, Martin Matzner, Stefanie Rinderle-Ma, Maximilian Röglinger, Stefan Schönig, Axel Winkelmann

TL;DR

This paper addresses the emergence of data-driven BPM as a paradigm shift driven by expanding data sources and AI advances. It proposes a lifecycle-based framework and maps five research fields—data processing and quality, process discovery, hyperautomation, automated process redesign, and predictive/prescriptive monitoring—across the BPM lifecycle, augmented by enablers like unstructured data, NLP, and transfer learning. Key contributions include outlining concrete research directions, proposing runtime recommender systems, cross-process transfer learning, and process-aware automation as core techniques, and emphasizing interdisciplinary collaboration. The work highlights potential practical impacts in improved data quality, cross-system analytics, and automated, evidence-based process improvement, while calling for collaboration to advance the next generation of data-driven BPM.

Abstract

The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm shift in data-based management and decision-making. Since business processes are at the core of organizational work, these developments heavily impact BPM as a crucial success factor for organizations. In view of this emerging potential, data-driven business process management has become a relevant and vibrant research area. Given the complexity and interdisciplinarity of the research field, this position paper therefore presents research insights regarding data-driven BPM.

Recent Advances in Data-Driven Business Process Management

TL;DR

This paper addresses the emergence of data-driven BPM as a paradigm shift driven by expanding data sources and AI advances. It proposes a lifecycle-based framework and maps five research fields—data processing and quality, process discovery, hyperautomation, automated process redesign, and predictive/prescriptive monitoring—across the BPM lifecycle, augmented by enablers like unstructured data, NLP, and transfer learning. Key contributions include outlining concrete research directions, proposing runtime recommender systems, cross-process transfer learning, and process-aware automation as core techniques, and emphasizing interdisciplinary collaboration. The work highlights potential practical impacts in improved data quality, cross-system analytics, and automated, evidence-based process improvement, while calling for collaboration to advance the next generation of data-driven BPM.

Abstract

The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm shift in data-based management and decision-making. Since business processes are at the core of organizational work, these developments heavily impact BPM as a crucial success factor for organizations. In view of this emerging potential, data-driven business process management has become a relevant and vibrant research area. Given the complexity and interdisciplinarity of the research field, this position paper therefore presents research insights regarding data-driven BPM.
Paper Structure (13 sections, 10 figures)

This paper contains 13 sections, 10 figures.

Table of Contents

  1. Introduction
  2. Research Challenges in Data-Driven
  3. Navigating the Process Lifecycle in Data-driven Process Management
  4. Research Field: Data Processing and Data Quality
  5. Research Field: Process Discovery
  6. Research Field: Hyperautomation
  7. Research Field: Automated Process Redesign
  8. Research Field: Predictive and Prescriptive Business Process Monitoring
  9. Runtime Recommender Systems
  10. Transfer Learning
  11. Process-Aware Automation, Mining, and Prediction
  12. Outlook and Call to Action
  13. Research Community

Figures (10)

  • Figure 1: Process lifecycle according to Dumas et al. dumas2018fundamentals
  • Figure 2: Enablers for innovation in data-driven
  • Figure 3: Assignment of research topics (rows) to lifecycle phases (columns); database symbol = data as enabler, gear symbol = techniques as enabler; Sec. = section discussing the topic as part of a broader research field (see Fig. \ref{['fig:framework-extended']} in Appendix \ref{['sec:research_community']} for a breakdown of the thematic interests of the research groups involved in this paper).
  • Figure 4: Data Processing and Quality - now and in the future
  • Figure 5: Process discovery - now and in the future
  • ...and 5 more figures