The Transformation of Data Science
Preface
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 in the 1990s 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 and the practice to the early 1960s, when it was first used by the US Air Force in a surprisingly similar way to its current usage, to the years immediately following 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). Among the themes that emerge from this review are (1) a continuous and consistent meaning of data science as the practice of managing, processing, and extracting value from scientific data from the 1960s to the present, (2) a long-standing opposition between data analysts and data miners that has animated the field since the 1980s, and (3) the phenomenon of what is here called “data impedance” — the structural 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.