The era of business analytics: identifying and ranking the differences between business intelligence and data science from practitioners’ perspective using the Delphi method

Abstract

The concepts associated with business analytics, such as business intelligence and data science, are generally murky. However, this misconception has a harmful impact on both academics and practitioners. This uncertainty may cause universities to develop misleading or incoherent curricula. This lack of clarity may also cause enterprises to choose an inappropriate analytical solution to a business problem, resulting in project failure and wasted resources. Despite its significance; it appears that only practitioners and major consulting firms are exerting significant effort to address this matter. Hence, this study aims to fill this void and uses the Delphi method to indicate that business intelligence and data science may be classified using eight dimensions which are: types of analytics, analytics process, skill set, data sources, business value, the scope of analytics, methods & techniques, and finally, technological platforms & tools. Significant implications for theory and practice are offered.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 13 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.