Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
As data science (DS) becomes integral to business strategies, standardizing DS processes and improving their execution are becoming increasingly important. To address this, researchers have proposed several data science process models (DSPM). Despite their recognized efficiency gains, organizations are reluctant to adopt these models. A major challenge to the adoption of DSPM is the need for more process guidance. Our study introduces proDASC, a process guidance system (PGS) designed to support the execution of DS processes to facilitate the adoption of DSPM and promote its practical application. We employ a design science research approach to investigate DSPM implementation issues, derive design decisions from existing design principles, and develop and evaluate the innovative artifact proDASC. Our research presents a methodologically grounded PGS prototype that has the potential to improve the execution of the DS processes and enhance process knowledge. It supports the adoption of DSPM and expands the PGS knowledge base.
Recommended Citation
Rösl, Stefan; Auer, Thomas; and Schieder, Christian, "Towards Operational Excellence in Data Science: Designing a Process Guidance System to Support Data Science Process Execution" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/da/data_science/2
Towards Operational Excellence in Data Science: Designing a Process Guidance System to Support Data Science Process Execution
Hilton Waikoloa Village, Hawaii
As data science (DS) becomes integral to business strategies, standardizing DS processes and improving their execution are becoming increasingly important. To address this, researchers have proposed several data science process models (DSPM). Despite their recognized efficiency gains, organizations are reluctant to adopt these models. A major challenge to the adoption of DSPM is the need for more process guidance. Our study introduces proDASC, a process guidance system (PGS) designed to support the execution of DS processes to facilitate the adoption of DSPM and promote its practical application. We employ a design science research approach to investigate DSPM implementation issues, derive design decisions from existing design principles, and develop and evaluate the innovative artifact proDASC. Our research presents a methodologically grounded PGS prototype that has the potential to improve the execution of the DS processes and enhance process knowledge. It supports the adoption of DSPM and expands the PGS knowledge base.
https://aisel.aisnet.org/hicss-58/da/data_science/2