The origins of business analytics and implications for the information systems field

ABSTRACT

Like many other disciplines, the information systems (IS) community has embraced big data analytics and data science. However, in the rush to exploit the popularity of this latest trend, the areas of big data analytics and data science that are most relevant to the IS field are not made clear. While many consider data analytics as an evolution of decision support systems (DSS), that is, as a technology that needs to be managed or enhanced, this essay traces the complex origins and philosophy of analytics instead back to Luhn’s text analytics in the late 1950s, Naur’s Computing as a Human Activity and his datalogy, Tukey’s Future of Data Analysis of the 1960s, and Codd’s relational database schema in the 1970s, well before big data analytics and data science became industry buzzwords. Many of what is now considered mainstream thinking in big data analytics and data science can be traced back to these visionaries. This essay examines the implications of the complex origins of data analytics and data science for the IS field, specifically on how those different discourses impact future research and practice.

KEYWORDS:

• History of analytics
• business intelligence
• data warehouse
• text analysis
• data science
• big data analytics
• business analytics

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. Davenport generally uses the term “analytics” without adding the qualifier “business” in his writings. One exception was a 2005 Babson College research report that predated Davenport and Harris (2007) where Davenport, Cohen, and Jacobson (2005) refers to “business analytics.”