SINYOUNG AHN

A user experience designer, service designer and design researcher based in South Holland, who aims to creates meaningful interactions between people and emerging technology.



PROJECT HIGHLIGHTS

** 
- Hydro Scotland
- Redesign IKEA Comfort Guide
- DDD Toolkit

2022
- DDD Toolkit

2021
- Redesign IKEA Comfort Guide
- Mapping Beneficent AI

2020
- Pavel, design a travel
- Pavel to DMZ
- How to measure 2 Meter 
- Donggwoldo, poster design

-- 2019 
- Hydro Scotland
- O2 ABC Glasgow  
- Closed Circuit

EDUCATION 2021-Present
  Delft University of Technology
  MSc Design for Interaction

2017-2018
  Glasgow School of Art, Scotland
  M.Des Design Innovation & Service Design

2011-2015
  Kookmin University, South Korea
  BA Spatial Design
CONTACT ︎︎︎sii.ahn.kr@gmail.com

DDD Toolkit: Towards Designerly data donation in Practice, 2022-23


PROJECT BACKGROUND
Behavioural data that shows how and what people do could help designers unlock new ideas and perspectives about their users. However, collecting this data is expensive and time-consuming, and ethical concerns inevitably arise because the data often contains personal information. Ortega presents designerly data donation as an efficient and ethical approach that encourages the active participation of users to obtain contextualised data (Ortega et al., 2021). This subtle switch of attitude towards data collection will help designers reduce concerns about budget or invasion of privacy. Designers can build up proper triggers to inspire users to donate their data and provide enough information to enable donors to autonomously participate in their control and choice. While its potential has been defined, there are a few challenges to further integrating this concept in practice. In particular, designers must understand the whole system to plan the right strategies to call for donors, taking the right action at the right time. With this in mind, the main focus of this project was how to deliver the concept of designerly data donation as a design method for designers in practice. A design challenge and related activities were conducted with UX designers of The Valley and the data-centric design lab at TU delft, after which I proposed the initial shape of the DDD toolkit that can be used in practice as a result of this project.


RESULTS
︎︎︎Master’s Thesis



KEYWORDS
# Design Research
# UX/UI Design
# Master’s Project
# Design for Interaction, TU Delft



Introduction



The research focused on the integration of designerly data donation in practice, and it aimed to validate its value in the context of a digital design agency.
Therefore, it was expected to provide valuable insights by trying it out in a real context as a research activity. As such, a design challenge was planned as the main activity of this project. It was conducted with the cooperation of the UX designers of The Valley. During the design challenge, they attempted to use designerly data donation in their project context following the given guidance. With the empirical research approaches, a scenario for a new design method and a toolkit to deliver its concept was developed and validated.







Theme Exploration



‘Designerly Data Donation’


People live in a world where massive amounts of personal data are generated via connected products and services. These data provide rich insight that have the potential to understand the behaviours and lifestyles of people. Data donation enables designers and researchers to actively utilise personal data when it is given by donors for the informed context. In particular, current rules of the EU’s General Data Protection Regulation (GDPR) and the right to portability (2018), allow people to request data gathered about themselves by third parties in order to reuse it and share it.

In data donation, there are no economic profit motives that happen during its transfer. However, the practice of giving and receiving and its consequences cannot be understood outside of the personal, social and economic relations surrounding the donor and receiver. Although the donation is not directly reciprocal, it can affect the relationships between the people involved (Floridi and Taddeo, 2019). With this in mind, understanding the relationship between donors and receivers, how donation happens, in what situations, and the factors that motivate people to donate become important aspects of data donation.






‘Community Based Design Support (CBDS)’


Finding a right format for new interaction, a community-based platform is considered a suitable framework to deliver the method to designers for following reasons:
(1) Designerly data donation is an evolving concept and is to be established as a design method for practical implication It is mandatory to open the possibilities to edit and update the information.Therefore, it is limited to start in an inflexible format, such as printed publications or web repositories. (2) As this project is conducted with the cooperation of a research laboratory and a design agency, there are existing infrastructures and a community that could be considered as being involved. (3) With collaborative activities and relationships between a design laboratory and a design agency, they could expect different benefits from each other. The laboratory can have rich and broad insights about their research area through practical implementation in various cases, while the agencies are broadening their capabilities through experimental design approaches.(4) Moreover, to deliver a new design method to designers, it is essential to attract their attention. This means that understanding the mindset of designers who are engaging with the CBDS will be one of the core considerations when designing the structure of the platform.










Preliminary Research



‘Generative Session’

In this phase, empirical design research was conducted with both an expert and end- users of the final design. Overall, it aimed to explore research themes with stakeholders and gain insights from the interactions. The stakeholders were sorted into two groups: experts and end-users. During a session with an expert in Designerly Data Donation, a journey map was created that presented the overview of data donation from the perspective of the designers. With the end-user group, a semi-structured interview and a generative session were completed, and the key insights were framed to support the concept of the prototype for the design challenge.










Designerly Data Donation and its process through different types of materials. The aim was to learn about the difficulties designers face in learning a concept and trying it out for the first time before creating a prototype for the upcoming design challenge. The session lasted for 90 minutes and started with written descriptions being provided. Then, a storyboard with three
different cases was presented and a Participants brainstorming session was held on certain key words. Paper boards was provided to participants for brainstorming and write down their thoughts (Figure C.6). Finally, designers were asked to use all the ingredients generated through the previous activities to initiate a plan themselves. The aim was to learn about the difficulties designers face in learning a concept and trying it out for the first time before creating a prototype for an upcoming design challenge










Empirical Research



‘Prototype’

This section outlines the setup of the design challenge and methods used for each stage.
Firstly, a prototype was designed based on the results of literature reviews and preliminary research with stakeholders and it was built with Figma,
including the essential content and interactions of the design toolkit that represented the new design method.

The prototype was validated and developed through a design challenge where end- users explore the artifacts in the context of their use.
This empirical research approach allowed to define the design of the toolkit and explain the strategies of the method in a broader context for the stakeholders.
As the duration of the research was limited, the aim was to take the complete experience into account not just the quality of the case results.



















‘Design Challenge’

The design challenge aimed to give designers the experience of using designerly data donation and a prototype of the toolkit to solve design problems across a series of processes and generate insights throughout. Following aspects were considered: (1) Difficulties: What are the difficulties during the journey?, (2) Missing Information: What information needs to be included in the method?, (3) Design Opportunities: How to solve the problems?, (4) Value Define: What are the values for the stakeholders derived from the method?
The activities were planned under a semi- structured format with the aim of collecting the results in such a way that data could be explored and analysed simultaneously.
I recruited the participants recruited from The Valley, the same UX designers who participated in the preliminary research. The design challenge was conducted in three phases of generative sessions, as well as an extra session, and lasted for six weeks of two-hour sessions with the participants. I supported the participants with one-to-one guidance during the session (Figure D.5).






The ‘say-do-make’ technique is an approach that records and combines the entire structure of a design challenge. As the name implies, it looks at what people say, do, and make. Conducting a generative session includes those three elements. As such, the planning of activities and the consideration of different information layers can complement and reinforce each other to provide additional value. In this regard, the study was divided into three phases. First, interviewing participants to hear what they ‘say’ about emotions in their own words. Second, observing what people ‘do’ while they are using the prototype and experiencing the data donation process. Finally, examining what people ‘make’ or think about when trying to improve their experience. Through these activities, participants were expected to gradually explore the experience from an explicit to a latent level.







               







DDD Toolkit



‘Service Blueprint Map’

The results of the design challenge led to several design opportunities to develop a method for designers. These can be summarised as follows: (B)Iterative Process: The DDD journey is an iterative process. One of the easiest ways to learn a new method is to try it out. For the novice, it is difficult to make a perfect plan. Therefore, through iteration, designers will be able to reach the solutions they need to resolve their design problems. (A) Project Repository: Designers tend to rely on case studies. For data receivers, it can be unclear what kind of results can be obtained with this method. As such, the question of which situation they could use it can also be unclear. One of the most obvious yet frequent discoveries is that designers always look at various cases to get inspiration and outline possible outcomes. In particular, having various examples of which data can be used to get certain insights and how to trigger more donors to get involved was a valuable aid in planning and completing the journey. Therefore, it is important to have resources available where people can refer to many cases. (C) Ethics Checklists: Ethics checking is an important and time-consuming process. While ethical issues are a crucial and practical part of data donation, designers can lack the knowledge on how to prevent ethical issues from arising during the journey. Since the data collected through data donation is diverse, it can include intimate user data, which may not be a usual source for those UX designers. (D) Collaboration: DDD opens up collaborations with stakeholders. The tasks involved in processing data, such as cleaning and visualising to produce meaningful information, are not within most designers’ comfort zones. Therefore, cooperation with data specialists should be considered in accordance with the data type and designers’ data knowledge.



Vision in product design




‘Roles and Relationships’

• Designers (Data receiver/Practitioner): Designers working in business sectors or running design projects can be the data receivers and main users of the DDD toolkit. Designers can improve their skills in data-centric design by learning DDD through the shared knowledge in the toolkit. At the same time, designers can contribute to the community by sharing their experience of their own DDD journey.

• Researchers (Data receiver/Researcher): Researchers developing the methodology in academia with the theme of data-centric design or desigenrly data donation. Researchers keep developing and updating the current knowledge and sharing its value through research projects and collaboration with third party stakeholders. Those people can encourage more designers to be involved in their research theme and get insights from broader cases.

• (Potential) Users (Data donor/Citizens): Data donors will be determined through the planning process. They can be any users or people relevant to the project context. Donors can participate in this journey with motivations of self-interest, the expectation for personalised service, or improved current service. Those values should be determined by data receivers depending on their project context.

• Employees managing ethics: The Human Research Ethics Committee (HREC) at TU Delft. Potential ethical risks can arise during the journey. As such, consent should be confirmed by employees from a company following their rules. The materials and guideline from HREC can also be a useful resource.

• Clients: Clients of design agencies will be an indirect stakeholder. Although designers are using DDD for a client’s project, depending on its planning, the client’s agreement is not mandatory. DDD can be an independent exercise for designers, and this can be an efficient way to gather data outside of their own products or services.

• Data specialist: Data specialists can cooperate with designers for planning the DDD journey, cleaning and reshaping the data for the analysis and contextualising phase. While it is not mandatory to involve these people, their help will open up more possibilities for designers to work with data.















Mark