KEMs

Data for Exploration & Substantiation

Meaning & Awareness

Vision & Imagination

GOAL

Shaping change with data
Using data to explore (change) issues and provide inspiration and substantiation.

PHASE IN DEVELOPMENT PROCESS

Discover
Define
Develop
Deliver

APPLICATION

Using data in the design process and in the resulting designed products, systems or services.
Generate, analyze or validate data (in support of mission-driven innovation).
Collecting the right types of data to answer the right questions.
Using data to explore (diverge) within a design process.
Using data to make choices and reduce uncertainty within a design process (determine, substantiate, converge).

What does it entail?

What does this category entail?
KEMs within the category Data for Exploration and Substantiation help generate and apply insights from data within (complex) issues. The emphasis is on the specific context and the stories behind the data.

The category includes an overview of data-inspired to data-driven approaches. These can be applied in the design process and in the resulting designed products, systems or services. As a next step, these methods can be used to generate, analyze or validate data to support mission-driven innovation.

Why is that important?
Design issues and social transitions require continuous questioning and substantiation. During transitions, new information becomes available that can lead to a change of course. The extent to which the choices made influence society and individuals also requires continuous substantiation and refinement. Data, when used correctly, can provide a lot of support and offer support for these issues and processes.

When do you apply these KEMs?
For questions such as:

How do you use data in the design process and in the resulting designed products, systems or services?
How can you generate, analyze or validate data (to support mission-driven innovation)?
How do you collect the right types of data to answer the right questions?
How do you use data directly or indirectly to gradually search for more information and possibilities within a design process (explore, diverge)?
How do you use data directly or indirectly to make choices and reduce uncertainty within a design process (determine, substantiate, converge)?
How do you select and connect appropriate methods? Methods, when combined in a considered manner, can generate high-quality data and facilitate meaningful interventions.

What methods are there?

The Exploration and Substantiation Data field can be displayed in a space divided by the product and probe axes, and the exploration versus substantiation axis.

The methods on the left side of the figure provide a divergent movement in a design process, where more information and possibilities are gradually sought. The methods on the right provide a convergent movement, where data is used directly or indirectly to make choices and reduce uncertainty.
The left and right sides correspond to more respectively exploratory and substantiating design phases. For example, where Data-Driven Design is more determinative of product design choices, Data-Aware or Data-Inspired Design is more creative and exploratory. That is, design decisions should not only be made based on data, but also on data collection methods, and the right types of data should be collected to answer the right questions.
The top half of the figure shows methods that focus on data in relation to a product, either through a research product in Data-Centric Design or by iterating on data-driven products. The bottom half shows approaches that focus on Involving data in the design process, rather than in its outcomes. Here, probing and existing datasets are used to explore possibilities or inform design decisions. Figure 2 shows the Data-Enabled Design approach.

Human-Data Interaction
Human-Data Interaction (HDI) encompasses the principles of readability, negotiability, and agency. Readability focuses on making data understandable and accessible to users through visualizations and interfaces. Negotiability involves users in decision-making processes regarding data collection and use, allowing them to negotiate the terms and conditions. Agency emphasizes the control and autonomy of users over their personal data, allowing them to exercise control over accessibility and use. Haddadi et al. (2016) delve further into these concepts and discuss the importance of transparency, interactive consent processes, and user empowerment in human-data interactions. Related to HDI, a further distinction can be made between methods and tools to use data in creative ways.

Data Physicalization takes advantage of the tactile properties of materials, combining haptic and visual senses to promote playful and exploratory activities with data. Data physicalisation can facilitate a deeper sense of engagement through unfamiliar and attractive materials (Nissen et al., 2015, Desjardins et al. 2020), enabling the physical construction of visualisation (Huron et al., 2014), or through responsive tactility that senses and responds to user input.
Data Fiction, for example in the form of Epics (Desjardin & Biggs, 2021), is also an important approach to combine and contrast with data science-focused approaches. It can be a way to take an active perspective and foreground dialogues. Dourish and Gómez Cruz (2018) unravel and relate the datafication trend to datafiction, highlighting narratives as the common core of both approaches.
The element of time is central to all cases of data collection. This has led to an emerging line of research on the awareness and perception of this temporality as a core aspect of Data Interaction.
As the soma design approach is increasingly used in human-computer interaction, Somatic Data (Alfaras et al., 2020) a new way to creatively engage with biometric technology and the biodata it generates, namely interacting with the body's digitized response.

Data-Driven Design
The core idea behind Data-Driven Design is the use of user data and insights to inform and guide the design process, ultimately leading to improved user experiences (King et al., 2017). It also emphasizes the value of combining different data sources to gain a holistic understanding of user experiences.

A/B testing is a method of comparing two different versions of a design to see which one performs better. This allows designers to make evidence-based decisions instead of relying solely on intuition or assumptions. Data-Driven Design also involves working closely with other teams, such as data analysts and researchers, to integrate quantitative and qualitative data.
Another form of Data-Driven Design is the use of Digital Twins, both in industrial application and in social awareness and participatory decision-making. Digital twins are data-driven and offer their users a strong interaction with the data.

Another perspective on Data-Driven Design emphasizes the integration of data analytics and design processes to understand user behavior by collecting and analyzing data (Van Steenbergen et al., 2019). By examining user interactions and preferences, designers can gain valuable insights into how a design is used and the effectiveness of different elements in the design. This data-driven approach enables designers to make informed decisions about design improvements and optimizations. By continuously evaluating and refining a design based on user data, designers can create more user-centered and tailored experiences. This iterative process allows for continuous learning and improvement, allowing a design to continue to meet the changing needs of users. Integrating different types of data, such as quantitative and qualitative data, helps to gain a comprehensive understanding of the user experience. By combining data from surveys, interviews, and user feedback with quantitative data from analytics tools, designers can better understand the user’s motivations, emotions, and needs, which can support design decisions.

Data-Informed Design
Data-Informed Design is an approach that uses data to support convergence, but does not yet drive decision-making. This approach supports the way designers and design researchers think about the problem and context (King, 2017). It is grounded in iterative data prototyping and interventions, using data to indicate the many paths of the solution space.

This is how it is Entangled Ethnography defined as a general practice that brings together users, objects, and machine learning to support design processes.
Real Time Contextual Inquiry uses data streams to provoke discussion and create material for in-depth analysis (Gorkovenko et al., 2019).
Data-Driven Ethnography uses data from interactions to build rich contexts (Anderson et al., 2009).
Data Probes, building on cultural and technological research, are prototypes that help us question and immerse people in the possibilities offered by data and observe and learn from their responses (Bourgeois et al. 2014a).

Data-Centric Design Data-Centric Design is an emerging mindset that exploits opportunities in the many data trails that emerge from existing products, services and infrastructure. In contrast to the data-driven approach, data is reused from an operational and deterministic process to an exploratory and divergent design process. It revolves around three principles that build bridges to open science and data feminism:

Circularity focuses on minimizing additional data collection and instead encourages data reuse and repurposing by accessing and using existing data through mechanisms that adequately inform and consent the people represented in the data.
Participation focuses on actively partnering with people to jointly give meaning and enrichment to their data.
Reflexivity focuses on promoting people's reflection on their data and designers' reflexivity on their design process.

The Designerly Data Donation (Gomez Ortega et al., 2022) and its tangible representation DataSlip (Gomez Ortega, 2024) is a platform that enables designers to call for participation in the form of data and active engagement in participatory meaning-making. It uses the Data-Centric Design mindset, seeks alternative ways of participation and demonstrates how data can be a tool for contextual inquiry and a path to understanding what responsible data use can mean in practice.
Telemetry-Informed Design (Zhang et al., 2016) shows how naturalistic 360-degree videos can be leveraged in the design process by repurposing camera sensors.
Participatory Data Analysis (Bourgeois et al., 2014b) an example of active collaboration with people represented in the data to reflect on the meaning of data and ways to enrich it

Data-Enabled Design
Data-Enabled Design (DED) is a new approach that aims to innovate by using reliable, contextual insights and designing adaptive systems that meet individual user needs (Van Kollenburg & Bogers, 2019; Funk et al., 2024). Data-Enabled Design considers data as an active component in the design process, obtained from the environment and real end users through creative methods. The goal is to create complex products and services embedded in intelligent ecosystems, which understand users within their context and adapt based on data collection and processing. The Data-Enabled Design process consists of six steps:

1. situate prototypes in everyday life;
2. data collection
3. analysis to gain insights
4. design synthesis
5. Using data as creative design material
6. Remotely modify prototypes.

These steps ensure that the developed solutions are data-driven and capable of learning and adapting to user needs within an intelligent ecosystem. The Data-Enabled Design process facilitates two types of explorations: research-driven contextual exploration and solution-driven informed exploration. In this way, Data-Enabled Design enables flexibility and adaptability within the design process. In summary, Data-Enabled Design uses data as a creative design material to support the development of adaptive systems that are aligned with user needs and contextual insights (see Figure 3).

Through a Iterative Data Enabled Design Loop enables designers to continuously interact with users, collect and analyze data, and refine their solutions to create personalized and effective design outcomes. This approach bridges the gap between creative, Data-Inspired or Data-Centric explorations and Data-Driven design processes. This enables designers to create innovative and user-centric products and services within intelligent ecosystems.

Data for Exploration & Substantiation in practice