Data science in design innovation

Abstract

For organisations, the capability to exploit data for innovation efforts has already become imperative to their survival in an ever more competitive market. However, many organisations are currently still struggling with how, where and when to use data science for innovative purposes. The consulting industry is a significant driver for the development of such knowledge for organisations. In addition, in the past decades, consultants have adopted design thinking as a practice to support client's innovation efforts. The digital consultant firm (DCF) provides professional services to connect the enterprises' business strategy with implementation across digital fields, including design innovation and data science. Based on initial research, the collaboration between data scientists and designers is found to be an issue. This is problematic, considering the importance of data for their enterprise client's innovation efforts. Is the DCF able to achieve synergy between data science and its current design innovation approach, or is polarity simple to large? This thesis aims to explore and design practical support for digital consulting firms to integrate data science in their current design innovation approach. This is done by answering two research questions: (1) how can digital consulting firms integrate data science in design innovation? (2) how can this data-design integration be facilitated in digital consulting firms? To answer these questions, a design science research approach in the information systems is taken. Based on empirical research, including two collaborative workshops between the DCF's data scientist and designers, two significant findings are drafted. First, data-informed design is a viable opportunity for the DCF to use data science in design innovation client projects. However, a lack of cross-functional learning and a lack of cross-functional decision-making constrain the DCF from exploiting this opportunity. In addition to integrating the two teams, this results in missed revenue and a risk of high overhead costs. The firm is argued to facilitate the data-design integration by using a person-to-person knowledge strategy to tackle this challenge. The firm should institutionalise an internal alignment meeting before proposals are shared with the client. This meeting aims to support the data and design team's decision-makers with drafting viable proposals, increase the number of collaborative projects. In addition, a framework for interdisciplinary decision making is designed to support the person-to-person knowledge transfer to the decision-makers. Using a newly proposed method, job prototyping, a new role in the organisation is iteratively developed together with employees. The final concept is validation during an actual internal alignment meeting with the DCF's data and design decision-makers and a potential job-holder. The firm should create a new position in the organisation, 'the data design lead'. This person serves as a hinge between the two teams and aims to transfer his knowledge by acting as a sparring partner during the internal alignment meeting. In addition, a set of guidelines are designed to increase the impact of this new role on the data-design integration. The research findings provide a strategic and viable direction for digital consulting firms to facilitate data science integration in their design innovation process.