Abstract
Data science has the potential to create value and deep customer insight for service and software engineering. Companies are increasingly applying data science to support their service and software development practices. The goal of our research was to investigate how data science can be applied in software development organisations. We conducted a qualitative case study with an industrial partner. We collected data through a workshop, focus group interview and feedback session. This paper presents the data science process recommended by experienced data scientists and describes the key characteristics of the process, i.e., agility and continuous learning. We also report the challenges experienced while applying the data science process in customer projects. For example, the data scientists highlighted that it is challenging to identify an essential problem and ensure that the results will be utilised. Our findings indicate that it is important to put in place an agile, iterative data science process that supports continuous learning while focusing on a real business problem to be solved. In addition, the application of data science can be demanding and requires skills for addressing human and organisational issues.
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References
Begel, A., Zimmermann, T.: Analyze this! 145 questions for data scientists in software engineering. In: ICSE, pp. 12–22 (2014)
Bener, A., Misirli, A.T., Caglayan, B., Kocaguneli, E., Calikli, G.: Lessons learned from software analytics in practice. In: The Art and Science of Analyzing Software Data, pp. 453–489 (2015)
Bird, C., Menzies, T., Zimmermann, T.: Past, present, and future of analyzing software data. In: The Art and Science of Analyzing Software Data, 1st edn., pp. 1–13 (2015)
Cao, L., Science, D.: A comprehensive overview. ACM Comput. Surv. 59(3) (2017). Article No 43
Creswell, J.W.: Research Design-Qualitative, Quantitative, and Mixed-Methods Approaches, 4th edn. SAGE, California (2014)
Cruzes, D., Dyba, T.: Recommended steps for thematic synthesis in software engineering. In: International Symposium on Empirical Software Engineering and Measurement (ESEM), pp. 275–284 (2011)
Davenport, T.H., Patil, D.J., Scientist, D.: The Sexiest Job of the 21st Century, Harvard Business Review, pp. 70–76 (2012)
Fisher, D., DeLine, R., Czerwinski, M., Drucker, S.: Interactions with big data analytics. Int. Mag. 19(3), 50–59 (2012)
Kandel, S., Paepcke, A., Hellerstein, J.M., Heer, J.: Enterprise data analysis and visualization: an interview study. IEEE Trans. Vis. Comput. Graph. 18(12), 2917–2926 (2012)
Kim, M., Zimmmermann, T., DeLine, R., Begel, A.: The emerging role of data scientists on software development teams. In: ICSE, pp. 96–107 (2016)
Kontio, J., Lehtola, L., Bragge, J.: Using the focus group method in software engineering: obtaining practitioner and user experiences. In: ISESE, pp. 271–280 (2004)
Liamputtong, P.: Focus Group Methodology-Principles and Practices. SAGE, California (2011)
Patton, M.Q.: Qualitative Research & Evaluation Methods, 3rd edn. SAGE, California (2002)
Strawn, G.: Data Scientist, IT Pro, pp. 55–57. Computer.org
Zhang, D., Han, S., Dang, Y., Lou, J.-G., Zhang, H., Xie, T.: Software analytics in practice. IEEE Softw. 30(5), 30–37 (2013)
Acknowledgments
This work was supported by TEKES as part of the N4S Program of DIMECC (Digital, Internet, Materials & Engineering Co-Creation). We would also like to thank the case company Reaktor for the possibility to conduct this research.
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Riungu-Kalliosaari, L., Kauppinen, M., Männistö, T. (2017). What Can Be Learnt from Experienced Data Scientists? A Case Study. In: Felderer, M., Méndez Fernández, D., Turhan, B., Kalinowski, M., Sarro, F., Winkler, D. (eds) Product-Focused Software Process Improvement. PROFES 2017. Lecture Notes in Computer Science(), vol 10611. Springer, Cham. https://doi.org/10.1007/978-3-319-69926-4_5
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