What Can Be Learnt from Experienced Data Scientists? A Case Study

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Riungu-Kalliosaari , L , Kauppinen , M & Männistö , T M 2017 , What Can Be Learnt from Experienced Data Scientists? A Case Study . in M Felderer , D Méndez Fernández , B Turhan , M Kalinowski , F Sarro & D Winkler (eds) , Product-Focused Software Process Improvement : 18th International Conference, PROFES 2017 Innsbruck, Austria, November 29 – December 1, 2017 Proceedings . Lecture Notes in Computer Science , no. 10611 , Springer , Cham , pp. 55-70 , International Conference on on Product-Focused Software Process Improvement , Innsbruck , Austria , 29/11/2017 . https://doi.org/10.1007/978-3-319-69926-4_5

Title: What Can Be Learnt from Experienced Data Scientists? A Case Study
Author: Riungu-Kalliosaari, Leah; Kauppinen, Marjo; Männistö, Tomi Matti
Editor: Felderer, Michael; Méndez Fernández, Daniel; Turhan, Burak; Kalinowski, Marcos; Sarro, Federica; Winkler, Dietmar
Contributor: University of Helsinki, Empirical Software Engineering research group / Tomi Männistö
University of Helsinki, Department of Computer Science
Publisher: Springer
Date: 2017
Language: eng
Number of pages: 16
Belongs to series: Product-Focused Software Process Improvement 18th International Conference, PROFES 2017 Innsbruck, Austria, November 29 – December 1, 2017 Proceedings
Belongs to series: Lecture Notes in Computer Science
ISBN: 978-3-319-69925-7
978-3-319-69926-4
URI: http://hdl.handle.net/10138/235296
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.
Subject: 113 Computer and information sciences
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