The recent developments in data science and end-user data tools indicate an opportunity for designers to adapt new data tools for design enquiry. Data has an unquestionable role in the future of the design practice for creating new digital products and services. Today's data deluge also opens up new ways of enquiring about the world through data. The current work explores how designers could appropriate a data science workflow in their design research process. Two studies are conducted to explore how a data science workflow could be adapted into a design research process. We present how the participants appropriated data techniques for creative uses and how they synthesized a data-centric enquiry into their research process. We found that designers appropriate data using their creative capacities in hypothesis forming for data collection and exploratory data analysis, and we highlight some implications of this. Our findings can inform the design space of the creativity support of future data tools and future data-centric design methods.

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Even though emerging city-makers are increasingly organized to trigger social changes, it is still hard to apprehend their real power to transform space and the way we live together. In this chapter, we explore how designerly approaches, such as hacking, making, and prototyping, can empower emerging city-makers to trigger a broader change and transformation process. It can be concluded that hackable citymaking can make a difference when combining top-down public management with bottom-up social innovation. A patchable plug-in platform might enable emerging city-makers to create value for the city and for society. However, it asks for new ways of participatory governance that enable these emerging, heterogeneous city-makers to participate actively in exploring the collaborative envisioned potential and to have constructive dialogues aiming for transformational change for the common good. Keywords City-making • Urban interaction design • Societal challenges Systemic change

The increasing availability of large-scale datasets such as sensor data or social media data and increasingly accessible data science tools create unique opportunities for design. However, the relationship between data science practices and design methods is still underdeveloped. In this paper, we propose that data exploration activities can be effectively embedded within a broader design inquiry framework and define a new design method, coined Data Exploration for Design, to support methodical designerly data exploration. The design method addresses the novice’s learning curve and supporting developing a data exploration inquiry mindset with procedures and curated tools. The empirical evaluation highlights support for producing exploration outcomes that are worth the additional technical effort. We close the paper by positioning the findings in design methodology literature and motivating data exploration principles for design inquiry.The principles urge to acknowledge biases in data collection, spending time with the data, using visualizations as a means-to-an-end, and designers being part of the data collection.

The current work elaborates upon a Generative Data Exploration method, which is a design technique aiming at supporting designers in integrating data in their design activities. Digital data offers new opportunities in all sort of professional domains, yet existing approaches and tools to manipulate data are predominantly targeted at data experts. As access to data is becoming democratised, new types of techniques are needed to leverage the agency of designers and to empower them to utilise data in the design process. Designers without prior data experience can benefit from the techniques, know-how, best practices of experts, if such expert knowledge is codified in design methods and tools. The aims of a Generative Data Exploration method are twofold. First, the method facilitates a learning curve on gaining holistic data literacy. Second, the method supports designing where digital data, exploration of data and sense-making of data is part of the process.