  Consensus on the definition of data science remains low despite the
widespread establishment of academic programs in the field and continued demand
for data scientists in industry. Definitions range from rebranded statistics to
data-driven science to the science of data to simply the application of machine
learning to so-called big data to solve real-world problems. Current efforts to
trace the history of the field in order to clarify its definition, such as
Donoho's "50 Years of Data Science" (Donoho 2017), tend to focus on a short
period when a small group of statisticians adopted the term in an unsuccessful
attempt to rebrand their field in the face of the overshadowing effects of
computational statistics and data mining. Using textual evidence from primary
sources, this essay traces the history of the term to the 1960s, when it was
first used by the US Air Force in a surprisingly similar way to its current
usage, to 2012, the year that Harvard Business Review published the enormously
influential article "Data Scientist: The Sexiest Job of the 21st Century"
(Davenport and Patil 2012), while the American Statistical Association
acknowledged a profound disconnect between statistics and data science. Among
the themes that emerge from this review are (1) the long-standing opposition
between data analysts and data miners that continues to animate the field, (2)
an established definition of the term as the practice of managing and
processing scientific data that has been occluded by recent usage, and (3) the
phenomenon of data impedance -- the disproportion between surplus data, indexed
by phrases like data deluge and big data, and the limitations of computational
machinery and methods to process them. This persistent condition appears to
have motivated the use of the term and the field itself since its beginnings.
