Design of a Process Mining Alignment Method for Building Big Data Analytics Capabilities

Pfahlsberger, Lukas ORCID: https://orcid.org/0000-0002-1367-9441 and Mendling, Jan (2021) Design of a Process Mining Alignment Method for Building Big Data Analytics Capabilities. Proceedings of the 54th Hawaii International Conference on System Sciences.

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Abstract

Process mining is a big data analytics technique that supports business process management in an evidence-based way. Nowadays, companies struggle to build the required capabilities that lift process mining beyond technical proof-of-concept implementations. As research on process mining is largely limited to algorithm design and project management recommendations, current research does not understand well how process mining and complementary resources and capabilities can be aligned. By understanding those interrelations, companies learn to leverage their organizational potential during the execution of process mining more effectively and efficiently. In this paper, we address this research gap by using the design science research approach to develop a process mining alignment method. Our method supports companies mapping their individual technical requirements of process mining to their underlying organizational resources. We evaluate our method through a series of interviews with IT consultants.

Item Type:	Article
Keywords:	Business Intelligence, Business Analytics and Big Data: Innovation, Deployment and Management, big data analytics capability, process-mining alignment, process-orientation, resourced-based view
Divisions:	Departments > Wirtschaftsinformatik u. Operations Mgmt > Data, Process and Knowledge Management > Mendling
Version of the Document:	Published
Depositing User:	Gertraud Novotny
Date Deposited:	07 Apr 2021 11:16
Last Modified:	07 Apr 2021 11:16
Related URLs:	https://scholarspace.manoa.hawaii.edu/ha... https://scholarspace.manoa.hawaii.edu/ha...
FIDES Link:	https://bach.wu.ac.at/d/research/results/99160/
URI:	https://epub.wu.ac.at/id/eprint/8064

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