Paper ID
1787
Paper Type
full
Description
The explosion of interest in business analytics (BA) comes with multiple problems. With as many as eleven distinct disciplines teaching analytics, it is not clear which areas of study constitute the BA field. If the information systems (IS) field is to exert a significant influence in analytics, what the IS researcher and practitioner need to focus on has to be made clear. Using a paradigmatic historiographical analysis of the field of analytics this study provides evidence for the bifurcation of analytics into data science and BA as founding disciplines of computer science, mathematics and statistics, machine learning and IS contribute to the analytics movement. The results from this analysis also identify a set of conceptual foundations for BA that takes advantage of both the intellectual strengths of the IS field without sacrificing the necessary depth of data science.
Recommended Citation
Hassan, Nik Rushdi, "Where are we headed in business analytics? A framework based on a paradigmatic analysis of the history of analytics" (2019). ICIS 2019 Proceedings. 5.
https://aisel.aisnet.org/icis2019/research_methods/research_methods/5
Where are we headed in business analytics? A framework based on a paradigmatic analysis of the history of analytics
The explosion of interest in business analytics (BA) comes with multiple problems. With as many as eleven distinct disciplines teaching analytics, it is not clear which areas of study constitute the BA field. If the information systems (IS) field is to exert a significant influence in analytics, what the IS researcher and practitioner need to focus on has to be made clear. Using a paradigmatic historiographical analysis of the field of analytics this study provides evidence for the bifurcation of analytics into data science and BA as founding disciplines of computer science, mathematics and statistics, machine learning and IS contribute to the analytics movement. The results from this analysis also identify a set of conceptual foundations for BA that takes advantage of both the intellectual strengths of the IS field without sacrificing the necessary depth of data science.