THE REAL WORK OF DATA SCIENCE: HOW TO TURN DATA INTO INFORMATION, BETTER DECISIONS, AND STRONGER ORGANIZATIONS

In 1962, John Tukey called for a reformation of academic statistics. In a famous paper titled “The Future of Data Analysis,” he pointed to the existence of an as-yet unrecognized science, whose subject was learning from data. He called it “data analysis.” In 1997, C. F. Jeff Wu, upon his inauguration lecture as Carver Professor of Statistics at University of Michigan, presented a talk titled “Statistics = Data Science?” in which he advocated that statistics be renamed data science and statisticians data scientists. The talk frameworks that support the real work of data science. The first framework is a simple approach for assessing impact labeled practical statistical efficiency (Kenett et al, 2003). The second one emphasizes a life-cycle view of statistics, starting from problem elicitation on through impact assessment (Kenett, 2015). The third framework is based on eight dimensions for assessing information quality, labeled InfoQ (Kenett and Shmueli, 2016). These dimensions can serve as a research roadmap for data analytics in and industrial engineering. Examples of research in two of the InfoQ dimensions, Data Structure and Data Integration, will be mentioned. Moreover, building on John Tukey’s 1962 paper and Jeff Wu’s 1997 address, the role of data science (and data scientists) in organizations will be discussed on the basis of a forthcoming book on this topic (Kenett and Redman, 2019). Specifically, the talk will refer to the evolution of the leader in statistical methodology or Director of Statistical Methods advocated by Deming as necessary in organizations who aim to become more competitive (Deming, 1986). So, responding to Tukey, “the future is here, and it is called data science”, and Statistics has a major role in it.

Keywords: data science, data scientist, Deming, director of statistical methods, information quality (InfoQ), practical statistical efficiency (PSE), life cycle view, data integration, data structure, compositional data.

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4. Kenett, R.S and Shmueli, G. (2016), Information Quality: The Potential of Data and Analytics to Generate Knowledge, John Wiley and Sons.
5. Kenett, R.S. and Redman, T.C. (2019), The Real Work of Data Science: How to turn data into information, better decisions, and stronger organizations, John Wiley and Sons.
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