| Summary: | Within the last few decades, data science has risen towards the top of agendas in public services such as adult social care. Concomitant with this ever-increasing appetite for data science is an expanding catalogue of challenges associated with developing, deploying, and maintaining data science software: choosing the appropriate, if any, analytical technique to use; balancing competing conceptions of success; and sustaining user adoption. In my thesis, I argue that these are sociotechnical challenges: issues that arise in the tension between what people do, and how data science software supports, limits, and, crucially, changes what they do. Through design science research, this thesis will develop, demonstrate, and evaluate a design approach for data science software that attempts to address such sociotechnical challenges. The empirical sites in which these research activities will take place are live data science projects with local government organisations responsible for adult social care services in England. The resultant approach will include a conceptual model for sociotechnical data science, process guidance and methods for applying the design approach, and results from applying the approach in a real-world data science project in collaboration with an industry partner.
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