A series of pervasive, spontaneous phenomena emerge every day in connection with the use of “smart technologies”. Hienz  states that the way we understand and embrace the data movement at present will shape how it impacts all of our futures, our lives, economies, societies, and the choices we make. This author also believes that changes will be for the better.
However, the connected and ubiquitous nature of these systems, associated with the enormous amount of data generated by billions of people using them, at an incredibly fast pace, poses new challenges in the design of such systems and confronts us with societal challenges that require a novel way of thinking about innovation.
Gardien et al.  refer to this evolution as a process of systemic innovation in an ecosystem approach. By ecosystems they mean “interconnected products, services and solutions that grow and adapt with the user to bring new value and meaning”.
The Paradigm Framework developed by Brand and Rocchi  effectively describes the change in people’s concept of value and meaning in ecosystems. The framework depicts a fundamental shift from the industrial and experience paradigm (from delivering a single product to a specific user, to delivering targeted experiences for customer segmentations) to the so-called knowledge paradigm, connected to delivering meaningful experiences in ecosystems, whereby both consumers and experts have become the developers of the knowledge needed to create these meaningful experiences.
The knowledge paradigm implies a change in the development of technology; it requires sensitivity with respect to how people behave and make sense of their lives as well as adaptivity to changes. New kinds of pervasive sensor-based and embedded technologies used in ecosystems demand a very different understanding from traditional user-interface design activities. People need to make sense of the composition of different system elements at the logical level (what can be done, what can go together with what and for what purpose), the functional level (how to use it), the physical level (it must be possible to see what fits together and how to manipulate it), and at the experience level (what does this mean for me, what are the implications in the long term, and does the system respect my privacy and rights?).
Proactive social participation is essential to coping with this complexity. The design of products, services and technological systems is, in fact, inextricably and inevitably linked with society, and has very profound social consequences.
Our contribution to designing for social innovation in ecosystems relies on developing and experimenting with methodologies that bring people and communities at the core of ecosystems of interconnected products and services.
In what follows we will first review related works using massive data to drive the design process. Later we define our approach to data-enabled design in ecosystems by presenting the Experiential Design Landscapes (EDL) methodology, and two sample projects, Social Stairs and [Y]our Perspective.
In the conclusion, we will discuss the challenges and potential of data-enabled design in smart ecosystems.
2. Related Work
Data has attracted the enthusiasm and attention of researchers for its potential in several areas of applications. A new generation of products, systems and services aims at changing people’s behaviour, following and creating trends of quantified self , wearables  and Internet of Things (IoT) . These new elements are always connected and present in our everyday lives and rely on increasingly more intelligent interactions to make our lives better. This introduces the realm of Big Data to design, as these new product systems and services continuously generate data. Big Data also offers new opportunities for learning to designers, as it is possible to investigate and analyse large body of information about people, their habits, preferences and behaviours, and gain insights for new designs. Marr  describes the 5 V’s of Big Data, being Volume, Velocity, Variety, Veracity and Value. In design we see an emphasis on Variety, the diversity of data, and Value. Designers have to figure out new processes of research. They must learn how to deal with data, how to visualise them and make sense of them. They have to experiment with new approaches to observe people’s behaviour remotely and to envision new forms of participation for users who they will likely never meet, in order to create new value through their designs and the underlying data.
There are different challenges in what Bogers et al.  call data-enabled design. They aim at developing a data-enabled design framework for designing intelligent products, services and ecosystems targeting behaviour change. They see three necessary building blocks for developing such a framework: user involvement, methodologies for technology-mediated data collection from the field, and the use of interactive prototypes . Their work builds on the EDL approach we describe in this paper (see paragraph 3). Here, we take a closer look at these three challenges.
The first challenge relates to the involvement of users in the design process. Marti and Bannon  examine some of the difficulties one may encounter in performing user-centred design. They argue that while a user-centred perspective is required at all times in the design team, the forms of participation of users in the design process need to fit the context in the broadest sense (physical, sociocultural, emotional), and can vary significantly from that presented as the prototypical user-centred design approach.
Apparently, the turn to big data is opening up a new era in user-centred design, where people are systematically and remotely monitored, observed and profiled. To give an example, some researchers have taken advantage of the spread of mobile telephones to involve ordinary citizens as potential data gatherers, to collect every day a whole series of data accurately, all over the world, at a very low cost. An illustration of this approach can be found in the Citizen Science project of Paulos et al. [10,11], whereby mobile phones are used as ‘networked mobile personal measurement instruments’ e.g., to measure the quality of San Francisco air by deploying sensing systems (CO, NOx, O3, temperature, humidity data and GPS) on over half the fleet of street sweepers and collecting several months of data. The various field studies presented by Paulos et al , using different mobile devices as measurement instruments, show that even if there are no real-time sharing mechanisms or informed interface for people involved in the study, an amazing interest of citizens in making sense of data clearly emerges. Most participants reported that from the study they learned that there were unsafe levels of air quality in their city beyond what the government or news agency had reported to them before. Many of them complained about politicians for not informing them adequately and for not enacting a legislation to improve the air quality in the name of public health. The project also stimulated participants in discussing and interpreting the data they collected every day. They also shared strategies adopted when they had captured dangerously high readings, such as finding alternate routes to be less polluted.
Other examples of new recent forms of participation in large amount of data gathering are known as Participatory Sensing (PS). This is a movement that observes and collects masses of highly detailed information that can then be used to improve quality of life in a great variety of different sectors, from environment to health and culture . This approach involves citizens in the monitoring and control of the environment they live in, in the broadest sense of the term, meaning both territory and sociocultural space.
Illustrious examples of PS include the Citizen Science project described above, but also embrace those developed by the University of California, Los Angeles, where students were involved in projects ranging from improvement of transportation to recycling, water monitoring, safety and health . However PS sees citizens primarily as “data gatherers” rather than partners of a sense-making and interpretation process of the generated data. The Timestreams Platform, developed by Jesse Blum et al. , tries to move more towards sense-making. This platform uses ubiquitous and pervasive computing for gathering and externalising a variety of data (e.g., CO2, temperature, humidity, images and videos) by artists, to boost a societal discussion about climate change. The Brazilian artist Ali created a record whose speed when played was controlled by the CO2 data collected on the Timestreams platform. Although citizens have a different role in this project, the gatherers of information are not the same persons as the ones interpreting the data.
So it seems that citizens can take up various roles in user-centred data-related design. As Sanders and Stappers  show in their map of UCD research (see Figure 1), most of the approaches to user-involvement can be mapped on a two-dimensional chart ranging from “users as subject and source of information” (user-centred design) to “users as participants and/or design partner” (participatory design, co-design, co-creation). The approaches share a fundamental issue in defining a suitable role for the users to deal with complex smart ecosystems.
An example of data-related user-centered design approach that is positioned on the left side of the figure is Cell Phone Intervention for You (CITY), where young adults are involved in user tests during which they receive behavioural interventions on their mobile phones to stimulate them to lose weight . The above-mentioned examples of PS and citizen science are more centred around the middle or moving to the right when users have a more equal and participatory role (Figure 1). In our own data-enabled design approach we also try to explore the right side of this figure.
The second challenge is related to techniques and methodologies for performing data-enabled design research. These methodologies range from screen-based approaches such as Virtual Ethnography  that adapts traditional observational methods to online fieldwork to the use of sensing technology worn by the users (see PS described above), and to approaches combining opinion-based and behaviour-based data [8,18].
This also creates challenges for design researchers. As described above, the roles of users are changing due to technological developments such as ubiquitous wireless networking, an explosive growth of Web 2.0 sites and connected IoT technology. That means that the amount of data generated by citizens as “amateur professionals” (instead of the previous passive consumer who was merely digesting professionally created content) is exploding and by far exceeding the quantity of professionally created content . Just imagine how much data the 600 million daily active users of Facebook, the 359 million active users of Google+ or the 300 million users of Twitter [20,21] could produce if they were involved in data-enabled design. On the one hand, technological developments such as machine learning can support the analytics of huge amounts of data to deal with the volume, variety and veracity in an automated and learning way. On the other hand, the users also play an important role in the data-enabled design process. In our opinion, the role of citizens should not merely focus on PS (participatory sensing), but on participatory sense-making, a term coined by de Jaegher and di Paolo  referring to the shared meaning-making process of people grounded in ongoing embodied and situated interactions in a shared action space. The data-enabled design process follows the translation from data to valuable information.
The third challenge is related to the use of interactive prototypes as a vehicle for experience and value probing, and a means of obtaining data of various kinds in an iterative way, starting early on in the design process. Prototypes can be used in many ways and forms, from the abstract to the sensorial. Prototyping creates insight and knowledge through the mechanism of reflection in and on action [23,24] and by offering a variety of experienceable prototypes through quick iterations. Designers enable people to have access to and express meaning in their everyday context, and through behavior-based data inform the designers about potential directions in which to go.
In line with these approaches, we see a value in data-enabled methods, since they offer the possibility to collect rich datasets of user’s behaviours, in particular when combined with participatory approaches where people can actually contribute to the design process.
In what follows we will describe the data-enabled design method EDL, which we have developed to design “smart” systems [25,26]. The description of the method is accompanied by the presentation of two sample projects, Social Stairs  and [Y]our Perspective that will highlight challenges and opportunities of data-enabled design.
The projects explore data-enabled design from two different perspectives.
In Social Stairs we collected multimodal data 24/7 on people’s changing or emergent behaviour in the use of stairs in a public building. The dataset was later used in participatory meetings with employees to envision social activities implying physical movement in the workplace.
[Y]our Perspective takes a different approach to data-enabled design. The social platform stimulates people in expressing a value proposition related to the place they cross in a specific moment. The system sends notifications related to the ongoing public debate and the place, and people can respond to this with their point of view. All comments are geo-localised and shared in the community, including the municipality. People can set new pools as well. Data are interpreted and discussed in public Open Space Technology meetings .
3. Experiential Design Landscapes
The Experiential Design Landscapes (EDL) method is a design research method aimed at designing for and with people in their natural environment, to find ways to support them in structurally changing their behaviour on a local scale, and to address global societal issues in the long run. EDLs are environments, be it physical or virtual, that are part of society (e.g., designated areas in cities, sports parks, virtual platforms, etc.) in which a design research team meets people in their everyday lives. EDLs can be seen as a specific type of Living Lab or Field Lab. For the exact difference, please refer to the paper by Peeters et al. . The EDL method is society-aware, design-enabled and inspired. The EDL method is based on 4 process steps of envisioning, designing interventions, acquiring data, and analysing and validating this data  (see Figure 2).
Moreover, the entire development process shifts via several stages from first explorations to value propositions that can be upscaled to a market-ready product or system, based on the growth plan as proposed by Ross et al.  through phases of incubation, nursery, adoption and product launch (Figure 3).
Open, disruptive, and intelligent propositions—which we call Experiential probes (EP)—can easily be created; introduced and tailored in the EDL. These EPs are open, sensor-enhanced, networked products-service systems that enable citizens to develop new and emerging behaviour. In a parallel manner they enable detailed analysis of the emerging data patterns by researchers and designers as a source of inspiration for the development of future systems; products and services. The environments and designed propositions are instrumented with smart sensors solutions to analyse changing behaviour and new emerging patterns. Through analysis, insight is gained on the behaviour of people and the influence of the design on the interaction and on society. EP can best be described as a physicalisation of “open scripts and intentionality” that aims to play an active role in the relationship between humans and their world . They are multi-stable ; i.e., context and relationship dependent. Depending on the relationship people have or build up with these artefacts or the context in which they are used, the probes can have different interpretations; intentionalities and identities.
The multistable interpretations, intentionalities and identities of the EP are not a result found after product launch, but can be explored, steered, challenged and radically changed during the design research process, based on people’s behaviour and the design research team’s vision of its future society. This creates a new process of design in which the design research team creates a dialogue through design with people in the EDL.
3.1. Social Stairs
Due to several causes, society is moving toward a sedentary and inactive lifestyle, with resulting consequences for the health and well-being of its citizens . One of these causes is the way our daily work has changed from physical to cognitive tasks, mostly performed in a stationary state. Work occupies a large part of our day and thus is of great influence on our lifestyle and health. Van der Ploeg et al.  showed that sitting for more than eleven hours has such an effect on our health that exercising will not redress the damage caused by sitting this long. With the abovementioned in mind, we describe the first EDL, called Social Stairs, a design proposition for an intelligent staircase, installed in both the main buildings of the Eindhoven University of Technology and KPN, a Dutch landline and mobile telecommunications company.
On two flights of stairs, each stair detects footsteps and triggers sound accordingly. The stairs make sounds when a person walks up or down (and thus interacts with) the stairs. These interactions allow the design research team to offer a new stair-walking experience and to explore opportunities to get people using the stairs more frequently and differently. The Social Stairs aims at disrupting the usual way of transit between floors in the building. Over the course of time, different EP, i.e., various types of stair-walking experiences and accompanying soundscape designs, were developed based on this inspiration and vision of walking and playing in the Social Stairs EDL. These EP contained multiple layers of complexity and offered different sound experiences ranging from musical and rhythmical sounds to surprising sounds, echoes and even complete compositions. People could jointly explore and deepen the interaction with the Social Stairs (Figure 4). Based on the behaviour and interaction of the people on/with the Social Stairs, each EP would react differently.
The Social Stairs EDL has been installed and carried out in the two separate locations where both EDLs ran for more than 6 months (Figure 5). The initial version of the Social Stairs was a result of an educational master course, where students created the first explorative probe of an instrumented and interactive stairs in the Eindhoven University of Technology main building. The first quick and low fidelity probe consisted of applying bubble wrap to a staircase. The observed resulting emerging behaviour led to new cycles of the EDL, where each stair was provided with a sensor. Two iterations have been installed between the first and second floor, on one side of the staircase, giving people the option between the Social Stairs and a normal staircase. The second iteration was built for a duration of six months, during which 4 main different EP were developed, tested, experienced and iterated. The probes differed for the sounds they created, ranging from “bloomy” echoing soundscapes to staccato drum sounds. The design research team found the different probes to result in different kinds of emerging behaviour [13
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