Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Extending IS theories of data and strategy that assume data are ultimately used for predictive analytics, this paper explores how data may be used as a strategic resource beyond the statistical predictions of analytics tools. Our point of view is that a choice exists of which relations in data — abstract statistical relations for predictive analytics, or domain-specific, conceptual relations for understanding — are to be enrolled in knowledge creation. We present evidence from the choice of data variables in 162 scientific papers in a subfield of metagenomics, supplemented by analysis of 231 patents from the same subfield. We discuss how accounting for the strategic use of data beyond analytics has important implications for IS theories regarding the value of domain knowledge and the location of bottlenecks in digital ecosystems.
Recommended Citation
Steinberger, Tom; Jung, Ju Yeon; and Cho, Lily, "Data as a Strategic Resource beyond Predictive Analytics" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 7.
https://aisel.aisnet.org/hicss-57/os/innovation/7
Data as a Strategic Resource beyond Predictive Analytics
Hilton Hawaiian Village, Honolulu, Hawaii
Extending IS theories of data and strategy that assume data are ultimately used for predictive analytics, this paper explores how data may be used as a strategic resource beyond the statistical predictions of analytics tools. Our point of view is that a choice exists of which relations in data — abstract statistical relations for predictive analytics, or domain-specific, conceptual relations for understanding — are to be enrolled in knowledge creation. We present evidence from the choice of data variables in 162 scientific papers in a subfield of metagenomics, supplemented by analysis of 231 patents from the same subfield. We discuss how accounting for the strategic use of data beyond analytics has important implications for IS theories regarding the value of domain knowledge and the location of bottlenecks in digital ecosystems.
https://aisel.aisnet.org/hicss-57/os/innovation/7