14 Data Science for Librarians example of this is given by Michael Palmer (2006), “Data is just like crude. It’s valuable, but if unrefined it cannot really be used.” Looking Ahead Academic librarians and many other information professionals have recently raised key concerns about how patron data is captured or gathered by various library discovery tools, especially how and with whom the data is shared. Universities and colleges are making hefty investments in modern data infrastructure across their campuses. With this in mind, academic librarians will have to be well prepared to contribute to these efforts. “Good data management is not a goal in itself, but rather is the key con- duit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse by the community after the data pub- lication process” (Wilkinson et al., 2016). Not too long ago, the Association of College and Research Libraries (ACRL) 2015 Environmental Scan identified the requirement for more advanced and sophisticated data curation services and urged librarians to have an in-depth knowledge and understanding of domain research practices in order to enable them to help professional researchers with data sharing, management, and preservation. Libraries look to offer more efficient and refined services (such as dis- covery interfaces, marketing, and collection use) however, these improve- ments can be produced or informed only via the effective analysis of user activity, which can create a conundrum between user privacy and user ser vice. Proxy servers, for example, may collect user IDs (and relevant user demographic information) and relate them to the use of specific resources that originate from individual users. This is why issues of data aggregation and retention vs. privacy should be considered and carefully balanced against library service enhancement. This usually necessitates sustained and effec- tive communications between the library and campus IT. The good news is that there are several continuing education programs, informal workshops, and training camps oriented toward teaching academic librarians’ technical skills. It is important to emphasize that we’re still at the start of the data science journey. Therefore, there’s plenty of work still to be done to increase awareness, to get inspiring success stories out in the open, and to develop a cohesive and tight-knit community of engaged library man agers and enthusiastic data science library prac ti tioners. References Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analy- sis, 15, 3–9. Bloom, T., Ganley, E., and Winker, M. (2014). Data access for the open access literature: PLOS’s data policy. PLOS Biology. 12(2). https://doi.org/10 .1371 / journal . pbio . 1001797 Breuer, M. A. (2005, May). Let’s think analog. IEEE Computer Society Annual Symposium on VLSI: New Frontiers in VLSI Design (ISVLSI’05), pp. 2–5. Buckland, M. K. (1991). Information as thing. Journal of the American Soci- ety for Information Science, 42(5), 351–360. Elragal, A., and Klischewski, R. (2017). Theory-driven or process-driven pre- diction? Epistemological challenges of big data analytics. Journal of Big Data, 4(1), 19.
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