| boris müller: Life Blog Projects Teaching & Research

Strategies for Design-Science Collaborations

27. May 2018

Bringing Design to Science — Part 2: the how

Calligraphy by Stefanie Weigele

Calligraphy by Stefanie Weigele

My last essay “Bringing Design to Science” was an attempt to describe the relationship of design and science. I would like to build on the essay and explore this relationship further.

Throughout the twentieth century, science has informed and inspired design. With the proliferation of digital technology, this relationship has changed. In many ways, design can now inform science. This fundamentally redefines the interconnection of design and science and it changes the role and responsibilities of both domains.

At the end of my last essay, I presented three projects from the interface programme and the Urban Complexity Lab at the University of Applied Sciences Potsdam. The projects demonstrate successful design-science collaborations — and I believe that many insights for these collaborations can be created from the projects.

However, I did not explicitly present strategies for initiating and conducting design-science collaborations. I explained the why — but not the how.

This essay is about the how.

Strategies for Design-Science Collaborations

In my experience, designers and scientists do not really like each other. There are plenty of clichés and preconceptions on both sides. Scientists view designers as frivolous nitwits who are only interested in superficial aesthetics and lifestyle products. Designers, on the other hand, believe that scientists have no taste, are incapable of explaining their ideas and are not interested in communicating their work. These clichés are neither true nor productive.

So while a closer collaboration between design and science seems like an obvious and desirable idea, there are obstacles for initiating projects that involve both sides.

I am clearly coming from the design side of this problem. So I cannot suggest how the scientific community could open up to the design world. But I can make suggestions for strategies that would enable the design community to initiate a closer collaboration with the scientific world. All strategies are somewhat obvious — but I believe it is useful to describe them at least briefly and present concrete ideas of how designers can initiate design-science collaborations.

Before we jump right into the strategies, one quick remark: when I speak about science, I explicitly include the humanities! This is probably because of my German background. In Germany, the distinction between the natural sciences and the humanities is less strong than in Anglo-Saxon countries. The terms “Naturwissenschaft” (science of nature) and “Geisteswissenschaft” (science of the mind) have the same root. They are both “Wissenschaften”.

But now — on to the strategies: engage, embed, co-create, collaborate, and agenda setting.


This is very obvious — but also very important. Especially if you are already in academia. Go out and meet colleagues from other disciplines, departments and universities.

However, this engagement should not be limited to personal contacts and research projects. Engaging other disciplines can and should also happen on the teaching level. Bringing colleagues from other disciplines to your classes and projects is sometimes a great challenge for the students — but it is also very productive. Asking students to address scientific problems and creating designs for them is an extremely valuable experience!

To push the engagement even further, contribute to a scientific community. Publishing a scientifically sound paper in an adjoining discipline is quite a challenge — but it is also a fantastic opportunity to engage with a wider audience. And it is a statement that design has indeed a relevance for science. In the Urban Complexity Lab, we are regularly publishing papers in computer science, visualisation, and digital humanities conferences and journals.

This strategy can already be useful for students. In our Lab, we usually have one or two student teams who get the opportunity to develop their own projects into fully fledged research endeavours. A good example for this is the project Shifted Maps by Heike Otten and Lennart Hildebrandt.

In this project, the two students have assumed the role of researchers who engaged with the academic field of visualisation — but the aesthetic, interactive and communicative quality of the implementation is an indication of their design skills.

Shifted Maps visualizes personal movement data as a network of map extracts showing visited places.

Shifted Maps visualizes personal movement data as a network of map extracts showing visited places.

So engaging with the scientific community is not only valuable for teachers and professors — but also for students. If the collaboration between science and design intensifies, I believe this will actually be a requirement for a successful design education.


Embedding designers in scientific teams is probably the most promising, the most exciting and the most challenging strategy. Scientific teams are already often interdisciplinary. Not out of idealism but out of necessity.

One reason for this is the computer. Today, computer systems are involved in most scientific research. Dealing with large amounts of data requires someone who understands algorithms and databases. This not only requires a computer scientist. When you work with data, images and interfaces — and if you intend to publish your work beyond the close academic circle — you need designers. And you don’t only need them as an external partner — you need them on your team.

Right now, it is fairly unusual to have designers on scientific teams. In my last essay, I have shortly presented the project “The Organ Generator”. It is a great example for the impact that an embedded designer can have in a scientific team. The specifics obviously depend on the research topic — data visualisation, interface design, dissemination strategies are areas where design is very important. These contributions are not only aimed at the communication and publication of findings. Data visualisation is highly relevant for the analysis and interpretation of data that is collected during the project. I know from experience that creating bespoke visualisations for scientific projects create new perspectives on data and support the scientists in finding methodological problems and opportunities.

“Bloodline Alpha 1” - the design of the software was part of the Organ Generator project.

“Bloodline Alpha 1” – the design of the software was part of the Organ Generator project.

I sincerely hope that embedding designers in scientific teams will be standard procedure in a few years. There are great opportunities for both design and science. But both disciplines need to adapt. Scientists and funding agencies need to understand the importance of this approach in order to enable it. And designers need to be able to address the complexity of scientific research. And — most importantly — as design teachers, we need to prepare our students for this kind of work.


Co-creation techniques are well established and are very well documented. However, there is one aspect of co-creation that is slightly overlooked. Co-creation not only means that one partner supports the other in expressing ideas about formations, visualisations and interfaces. It also means that both partners have to collaborate in order to enhance understanding and to have a debate as equals. Co-creation in an academic context is a knowledge transfer into two directions. Ideally, the result of a co-creation process are questions, observations, hypotheses, experiments and findings that are shared by all partners.

In this context, I cannot stress enough how important it is for designers to understand the science part of the project. It is not enough to treat data as numbers, visualisations as pictures or interfaces as templates. All design done in a scientific context needs to take into account the models, methods and insights of the project. In a scientific project, you need to understand the science. This sounds intimidating — but it is possible. And in all likelihood, you will have access to a really strong scientific team.

Designers in a research project are usually involved in the communication and dissemination activities. So they need to explain and interpret the questions, findings and methodologies of the project. They act as translators between the scientists and a wider audience. And explaining something is the best way to learn.


The best strategy for a successful design-science collaboration is to make valuable contributions to the overall project. This statement is a bit trite — but true nonetheless.

In order to do so, designers have to identify points in the scientific method where they have the greatest impact. I believe these points are currently found at the very end of the scientific process. The diagram below is a simplification — but it shows possible opportunities for design work.

A simple model of the the scientific method with possible applications for design.

A simple model of the the scientific method with possible applications for design.

The last point in this process — “engage with public” — is not strictly part of the scientific method (if “public” is understood as an audience beyond the academic peers). But I believe it is becoming more important and it will be a greater requirement for scientific projects in the future. As noted above, design plays a very important at this stage and it is at this point where most design-science collaborations will start.

I suggest to work our way backwards. Once the value of design has become clear at the dissemination stage, the benefits for involving designers at several stages of the scientific process become more convincing.

With the right setup, design and science can collaborate as equal partners in a research project. However, there are a number of challenges associated with this strategy. Some of them are quite difficult as they are political.

The biggest problem here is that design is not yet fully recognised as an academic discipline by funding agencies. Most research programmes still focus on the established domains and do not recognise design as a research area. This is obviously problematic for the academic design community. For a design team, the only way to get a grant from a research funding agency is to associate itself with a larger scientific consortium.

This is not necessarily bad. Being part of a consortium creates lots of opportunities for engagement, co-creation and collaboration. And I appreciate that funding initiatives increasingly require research projects to address communication and dissemination activities as these are problems that call for an involvement of designers.

However, in this setup, designers will always be in a supporting role. And I believe that designers should also create their own research agenda. Which brings me to my last point…

Agenda Setting

Last but not least: if science and design are going to collaborate as equals, design needs to establish its own domain-specific research agenda. This is as important as it is difficult.

The main challenge is that the term design is not properly defined. It is highly context dependent and ranges from the styling of furniture to software development. It would be very difficult to define a general research agenda that represents all approaches, schools, sub-disciplines and manifestos of the design world.

However, within this diversity, it is possible to carve out a framework for design research questions that address specific fields of study and practice. For example, I have no problem to identify and to describe research questions in the area of data visualisation and interface design. It would go beyond the scope of this essay to describe these questions in detail — my point is simply that research activities are linked to specific well-defined questions — and not general problems.

So the question is: what is our intrinsic motivation as design researchers? What do we want to find out? What are the questions that are relevant for our discipline? What are our domain-specific strategies and methodologies for answering these questions?

While I believe that it is important to collaborate with scientists and to support other scientific research, designers should also pursue their own agenda. The role of design in a research project goes beyond providing services for other academic disciplines. And this is not a contradiction. Large, interdisciplinary research projects can provide a great framework for pursuing intrinsic design research questions.


I have said it before and I am saying it again: design should not be more scientific! Designers can claim a disciplinary autonomy and can refer to unique ways of knowledge production and research. The notion that a design-science collaboration would require design to be more objective and more methodological is misleading. This approach has failed in the past and would ultimately sabotage the very qualities that make design relevant for the scientific community.

It is actually the other way round. What makes design attractive for science are the specific approaches of professional design – the “designerly ways of knowing” as the design theorist Nigel Cross puts it.

As noted above, the relationship between design and science is not very well established and it is not trivial to implement the above strategies. But design and science can inform each other. New forms of collaboration can create new areas of expertise, new insights and new theories. There is much to be gained — and nothing to be lost.

This essay was also reviewed by my colleague Marian Dörk. Still going strong as a design-science collaboration!


Bringing Design to Science

11. November 2017

Science can benefit more from design than design from science.

Design and science have an uneasy relationship. Or, to be more more precise, design has an uneasy relationship with science. Science on the other hand has almost no relationship with design and tends to happily ignore the excitements and commotions of the design world.

Historically, design has been fairly unscientific. As the name suggests, the Arts and Crafts movement, which is one of the foundations of modern design, was closer to fine art than to science. Designers and artists are often taught in the same department at university and share a similar mindset. Throughout the 20th century, art has inspired design — and sometimes vice versa. For many, design was primarily a strive for elegant and expressive aesthetics for the products and objects that surround us in our everyday life.

So historically, design is much closer to fine art than to science.

But things are not that simple. While aesthetics were, and are, the focal point of design — science and engineering always played an important role in the history of design. Scientific discoveries led to new technologies, new materials and new social spaces. But it was engineering and design that made these technical innovations accessible and affordable to a wider public. So one could argue that design makes scientific progress visible and usable.

This process was not limited to the natural sciences. In 1925, Otto Neurath created the »Vienna Method of Pictorial Statistics« which in 1934 became ISOTYPE — the International System of Typographic Picture Education.Together with the designers Marie Reidemeister and Gerd Arntz, Neurath wanted to create a new visual language that sought to explain the complexity of the world in pictorial form. Their aim was to communicate social and scientific data in a form that was understandable by a wider public.

{A typical example of an ISOTYPE information graphic. Note the conveyor belt on the left!}

Otto Neurath was an ardent logical positivist. He believed in empirical observation and in creating rational foundations for philosophical discourse. He was famous for interrupting philosophical debates by shouting “metaphysical!” if an argument was not rooted in empirical observation. So he was certainly not someone who was interested in expressive aesthetics and an artistic interpretation of design. And yet, ISOTYPE is a very important milestone in the history of graphic design.

The great achievement of ISOTYPE was to communicate complex statistical data through a formalised visual design language that enabled the viewers to quickly understand the relationship of symbols and data. In his seminal book International Picture Language, Neurath states that visual languages cannot replace verbal languages — but he also demonstrates the power of signs and pictures when it comes to explaining a complex process or conveying statistical data. So ISOTYPE is one of the earliest attempts to use design in order to communicate scientific data — in this case mainly from the social sciences and from history.

This “rationalistic” approach to design was later also pursued by the HfG Ulm (Hochschule für Gestaltung Ulm / Ulm School of Design). In 1953, the HfG Ulm accepted the first students. Right from the beginning it was clear that it would not just deal with superficial aesthetics. Design had a social and political responsibility. And the only way to address this responsibility was by becoming more objective — and more scientific.

{Foundation course exercise at the HfG Ulm. Student: John Lottes; Instructor: Anthony Froshaug; 1958–59; Courtesy HfG-Archiv/Ulmer Museum}

An essential part of the design education in Ulm was the to create a theoretical foundation and a rational reason for design decisions. Furthermore, not only the approach to design problems was methodical but also the aims and objectives of the design process. According to the ideas and ideals of the HfG, design should participate in the social and intellectual progress. It is the object of academic debate whether the design approach at the HfG Ulm was “scientific” — but it is safe to say that it introduced a form of intellectual questioning into the design process that is still valid today.

The scientific community was never particularly interested in the design world. But in the second half of the 20th century, it was recognised that science had a communication problem. In 1985, the Royal Society published a highly influential report with the title The Public Understanding of Science. The report recognises the importance of communicating the results of scientific research to a wider public. While mentioning the term “design” just occasionally, the report has strong implications for the relationship between design and science.

The responsibilities for design are fairly obvious. Communicating the history, the process and the results of science to a wider public is an immense design challenge. In this context, design offers great powers and possibilities. Be it the creation of a museum exhibit, the design of an information graphic or the interactive simulation of an experiment, design can convey scientific insights in an intelligent, informative and delightful way. In order to achieve this, the designer has to work closely with scientists and communicators and convey the right message and the right amount of complexity.

In this sense, design interprets science and transforms the interpretation into a concrete artefact. This interpretation is very much about finding an appropriate textual, visual and interactive form for a scientific statement.

However, it is important to note that this process cannot be a one-way-road. There are also responsibilities of science in this process. While the report The Public Understanding of Science states that “it is clearly a part of each scientist’s professional responsibility to promote the public understanding of science” it also recognises that “within the scientific community there is still often a stigma associated with being involved in the media”. Thirty years later this is still true. Every designer who has ever worked with scientist knows that creating science communication is indeed a tricky business.

We have to recognise that the scientific progress has reached a level of depth and complexity that it is hard to explain and to communicate every aspect.String theory is simply not trivial. Climate research deals with lots of uncertainties. Furthermore, science is highly specialised. There are a multitude of scientific disciplines and sub-disciplines. And even neighbouring disciplines sometimes do not understand each other. But we also have to recognise that in order to achieve a better public understanding of science, the scientific community needs to engage more with writers and designers.

If science wants to play a more active role in public debates, popular culture and universal education, it needs to adapt and gain a greater awareness of design strategies. Public understanding of science is a collaborative effort. And in this effort, design plays an important role.

So in the past, the relationship between science and design was dominated by attempts to make design more scientific and to employ design to make science more understandable.

This is all good and well. But I think design can do more. I believe design can make contributions to the scientific progress itself. Design can and should be part of science. Instead of bringing science to design, I would suggest to bring design to science.

Design — and especially interaction design — has many qualities, strategies and methodologies that can make substantial contributions to the scientific progress. This statement is probably surprising — if not irritating — to many people as “design” is still associated with things like marketing, advertising, superficial aesthetics, luxury and commercialism. While this association is not entirely wrong, it completely ignores the aspects of design that are extremely valuable for science: innovation, user-centredness, deep aesthetics, problem solving, contextual awareness. And as I have pointed out before, even the intuitive aspects of the design process are not irrational.

Instead of explaining these aspects in all detail, it is probably better to demonstrate the power and possibilities of design in the scientific context by illustrating this statement with concrete examples. At the Interface Design programme in Potsdam, we have been collaborating with scientists for many years. With our designs, we contributed to scientific projects and created new qualities in the scientific work.

The following design projects address and demonstrate the power and possibilities of design in science.

Design in the natural sciences

The Organ Generator — Computer Aided Biology Design

By Roman Grasy

In his MA thesis, Roman Grasy collaborated with a team of researchers that are exploring the possibilities of 3D bio-printing. He identified a number of research questions and problems that could be solved by design. As he was fully integrated into the team, he worked very closely with the researchers and created a number of relevant contributions for the project.

Bio-printing is a fairly new technology. It allows for the additive manufacturing of living tissue and is currently used to create individually designed mini-organs. Further developments of this technology aim at printing fully functional organs for medical transplantations. The printer used in this project was the Cellbricks Cellmaker, which uses stereolithography and specific bio-inks to print complex mini-organs. It has a resolution of up to 10μm.

{Software prototype of “Bloodline Alpha 1” beneath the Cellbricks 3D bioprinting machine.}

The starting point for the thesis was the design of a modelling software interface for the 3D bio-printer. While this was already a challenging topic and advanced into the application prototype “Bloodline Alpha 1”, the thesis quickly developed into a more complex investigation. Roman addressed many questions relating to the shape of the organoid data bodies. He applied generative design principles to the vascular aspects of the organoid bodies and created a parametric system that enabled him and the research team to create a large number of different models that are all based on the same generative principles. This setup formed a flexible but controlled environment for further experiments.

{Impressions of generative designed vessel systems.}

The setup was reflected in the interface and interaction design of the main software application for the modelling of the organoid data bodies — or “organ bricks”.

Based on the insights generated in the development of the system and the software interface, Roman was able to take the design questions a step further. He addressed the speculative question of how printed organs could possibly look and work like if they exist outside the human body.

Our organs are a repetition of patterns on complex organic surfaces within three dimensions. These patterns are based on the arrangement of functional units within the dense space structures of the human body.

The “Organ Generator” demonstrates the relevance of including designers in a scientific research team. The scope of the project and the quality of the results are strongly influenced by the design contributions. In this project, design is not just about reflecting scientific insights and optimising a software interface. Design explores the inherent questions and possibilities of the project. This way, design influences science — and vice versa.

Design in the humanities

VIKUS Viewer

By Katrin Glinka, Christopher Pietsch and Prof. Dr. Marian Dörk

In the last few years, a number of cultural institutions have digitised their collections. In many cases the media databases, in which the collections are stored, contain detailed and highly refined content. But the databases often lack adequate interfaces for working with the digitised material. There are not enough tools for exploring, visualising, organising and understanding cultural collections that support academics in their work.

The aim of the research project VIKUS is to investigate the role of data visualisation and graphical user interfaces in the exploration and examination of digitised cultural collections. Our team of researchers design, develop and evaluate interactive systems that support scientists and academics who work with cultural collections.

One of the outcomes of the VIKUS project is the interactive visualisation VIKUS Viewer in the implementation of »Past Visions«.

{Visualisation of historical drawings by Frederick William IV}

The visualisation is based on a digital collection of drawings by Frederick William IV of Prussia (1795–1861). The drawings reflect his personal ideas on art and architecture as well as literary influences or contemporary events like wars and revolutions. The database contains 1492 high-resolution images of the drawings and sketches and corresponding metadata.

The interactive visualisation provides the users with a number of ways to organise, explore and contextualise the images. It is realised as a dynamic canvas on which the drawings are arranged by year or similarity. Tools for interactive filtering and for zooming make the visualisation highly flexible and very powerful as the users can seamlessly move from high-level overviews to clusters and close-ups.

This setup allows the cultural scientists to see and explore the collection along temporal and thematic aspects while not abstracting the individual drawings into aggregated shapes. The interactive visualisation reveals both temporal and topical structures in the collection and provides a way to examine the high-resolution scans of the individual drawings.

The VIKUS Viewer provides cultural scientists with an innovative, valuable and efficient way to work with digital cultural collections. It is a well-designed tool for scientific research and generates new insights that would otherwise be lost or invisible.

Just as engineering provides scientists with new technological tools for measurement, recording and analysis, design provides science with new conceptual tools for the exploration and evaluation of data.

Design in Climate Impact Research

A Brief History of CO2 Emissions

By Julian Braun, Dr. Elmar Kriegler, Prof. Boris Müller et al.

Climate change is one of the most dramatic challenges of the 21st century. It is particularly insidious as it is a gradual process that takes place over many years. Climate change is only widely discussed when catastrophes like hurricanes or massive floodings occur. But it is a serious threat to global stability and it needs to be understood and acted upon.

As my colleague Elmar Kriegler from the Potsdam Institute for Climate Impact Research (PIK) explains: “Climate change is already occurring today (with around 1 degree warming since pre-industrial times) and even under the scenario of a successful implementation of the Paris Agreement will continue until mid-century (adding an extra half a degree of warming or a bit more until 2050). So some damages are here to stay, e.g. intensifying storms, heat waves and droughts, increasing sea levels, bleaching coral reefs, but the goal is to avoid the worst.”

Greenhouse gas emissions are one of the driving forces behind climate change. Together with the PIK, the Urban Complexity Lab of the University of Applied Sciences Potsdam designed and developed a short movie on CO2 emissions and on global warming. In our film “A Brief History of CO2 Emissions”, we visualize the geographic distribution and the historic dimension of carbon dioxide emissions.

We literally wanted to show where and when CO2 was emitted in the last 250 years — and might be emitted in the coming 80 years if no climate action is taken. By visualizing the global distribution and the local amount of cumulated CO2, we were able to create a strong image that demonstrates very clearly the dominant CO2 emitting regions and time spans. The format of a short film gave us also the opportunity to provide context and tell a story. So the data is not only visualized but also part of a narration. We believe that this combination of facts and storytelling is a great format for informing a wide audience about the causes and effects of climate change.

Our aim was not only to raise awareness for climate change but to relate factsand data. This was only possible by collaborating with climate scientists. The team from PIK choose the most recent and authoritative data sources on the topic. One important source was data from the Carbon Dioxide Information Analysis Center. It provides the longest time series of gridded (i.e. spatially distributed) CO2 emissions from fossil fuel combustion and cement manufacturing.

While our short film is essentially science communication and a good example for “the public understanding of science”, the collaboration with the researchers from the Potsdam Institute for Climate Impact research proved to be insightful and inspiring for both sides. It triggered the SENSES project that will address issues of visualising global climate change scenarios on a larger scale.


Science is one of the most important foundations of our modern world. This is not only true for the natural sciences but also for the humanities. Originally, design stood between the arts and engineering. But with the rise of digital technology, it has become more relevant for a wider range of topics and issues. We have reached a point where design — as a discipline — can contribute to the scientific progress and has indeed become part of science.

One of the most important contributions is certainly visualisation in its broadest sense. Designers are image-makers. And today, images play a very powerful role in science communication and in scientific work itself. If your scientific work involves images of any kind, you are dealing with design questions. Treating these questions with scientific integrity requires designers.

But beyond image-making, design can also play an important role in scientific work. Designers are trained as problem solvers and (co-)creators. As the project “The Organ Generator” has shown, designers can generate valuable contributions within a research project.

So science should engage with design — and vice versa. In order to make this happen, both communities need to open up more and engage each other. The scientific community should understand “design” not only as an aide in the sense of “public understanding of sciences” but also as a contributor to the scientific work itself. But the same is also true for design. If design is going to take on a greater responsibility in the world, it needs to engage more with scientific disciplines and bring the qualities that are inherent in design to the sciences.

This will leave design not unchanged. It will challenge the way we teach and practice design today. We will need new spaces where design and science can meet on equal terms. We will need new forms of research funding that requires the inclusion of the designers. We need an awareness for the potential of excellence in the science-design collaboration.

I strongly believe that designers as communicators, problem-solvers, image-makers, creators and co-creators can make relevant and valuable contributions to the scientific progress.

This essay was reviewed by my colleague Marian Dörk. He is a trained computer scientist – and I am a trained interaction designer. By jointly running the Urban Complexity Lab, we are a living example of a successful science–design collaboration!

“Bringing Design to Science” was originally published on Medium on 22 October 2017.

Picture, Depiction and Deception – Why Data Visualisations are Cultural Images

17. August 2017

Data visualisations are usually created by computers – but they are not technical images. Every visualisations is an interpretation of data and as such a cultural image.

This essay is the sequel to a blog posting that I wrote more than two years ago. The subject is still relevant so it makes sense to re-visit the text and elaborate on the issue. My central point is that a lot of the current debate on data visualisation is not differentiated enough and is based on an antiquated idea of an “image”. So let’s start at the same point as last time:

In the excellent book Design for Information by Isabel Meirelles, I came across a quote from Ben Shneiderman:

“Like Galileo’s telescope (1564–1642), Hooke’s microscope (1635–1703), or Roentgen’s x-rays (1845–1923), new information analysis tools are creating visualizations of never before seen structures. Jupiter’s moon, plant cells, and skeletons of living creatures were all revealed by previous technologies. Today, new network science concepts and analysis tools are making isolated groups, influential participants, and community structures visible in ways never before possible.”

This is a great quote and I very much like the vision behind it. It is obvious that data visualisation is an essential strategy to deal with large sets of data. Turning abstract data into a concrete images transforms data into something we can perceive and relate to. Data visualisation translates the unimaginable into a tangible and sometimes interactive image. So the power of visualisation is something I strongly believe in and I totally agree with.

However, I think Ben Shneiderman – and many others in the datavis community – misses an essential point about visualisations. He draws analogies with technical devices. The comparison with telescopes, x-ray and microscopes implies that data visualisation is mainly a technical problem – a problem of magnification and resolution. Optical devices magnify small or distant objects. Lenses and mirrors direct and focus light waves and are thus revealing existing physical structures like plant cells or galaxies. The assumption is that these devices generate technical images that are objective and show aspects of reality.

So the quote by Ben Shneiderman suggests that data visualisations are technical images. It suggests that the visual representation of complex data structures can be directly derived from the data itself. It suggests that by using visual encodings as data-lenses, you can create an objective image of the data.

I believe that these assumptions are questionable and misleading.

There is a strong argument in media theory for treating all images as symbolic. This includes technical images that were generated by a device. They are also a representation of an entity and need to be decoded and interpreted. So even technical images are not a copy of reality.

Which brings me to my main argument. I believe that that data visualisations are not technical but cultural images. As we will see, this distinction is a gradual one – but it is important to note that data visualisations simply cannot be understood as an objective excerpt of reality.

For the further discussion, I would like to refer to Vilém Flusser. He was a highly influential media theorist and philosopher and he dedicated a lot of his publications to the role and significance of photography. In his book “Towards a Philosophy of Photography”, he develops a theoretical framework for understanding the relationship between the photographic image, the apparatus and the photographer. In particular, he questions the objectivity of the photographic image and the apparent control of the photographer in regard to the image-making. But his writings are not limited to photography – he explicitly refers to “technical images” that also include computer-generated visuals.

I believe Flusser’s book holds a number of relevant lessons for the debate on data visualisation that is happening right now. In this context, the parallels between the photographic image and data visualisations are intriguing. Many of the theories, concepts and ideas that Flusser developed for technical images can be applied directly to data visualisation. The following quotes are all taken from his book “Towards a Philosophy of Photography”.

From Technical Images to Cultural Images
Flusser’s definition of the technical image is pretty straightforward: “the technical image is an image produced by apparatuses”. But he directly points to one of the most relevant and culturally important aspects of technical images – the fact that they are often mistaken for the reality they depict. “They [technical images] appear to be on the same level of reality as their significance”. This is a fundamental insight that not only relates to artistic, professional or hobbyist photography, but also to scientific images. Images are always symbolic. Or – as Flusser puts it: “This lack of criticism of technical images is potentially dangerous at a time when technical images are in the process of displacing texts – dangerous for the reason that the ‘objectivity’ of technical images is an illusion. For they are – like all images – not only symbolic but represent even more abstract complexes of symbols than traditional images.”

Important for our debate is also Flusser’s distinction between technical images and traditional images: “With traditional images, by contrast, the symbolic character is clearly evident because, in their case, human beings (for example, painters) place themselves between the images and their significance. Painters work out the symbols of the image ‘in their heads’ so as to transfer them by means of the paintbrush to the surface.”

This distinction is important insofar as it introduces two kinds of image-making. The technical image is produced by an apparatus, the traditional image is crafted by a person. This distinction has nothing to do with qualities. This is not about whether a traditional image is better than a technical image. But the distinction has a lot to do with the way these two different kind of images are perceived. A traditional image is perceived as real in itself – a technical image is perceived as a window on reality. But obviously the technical image is also (and primarily) real in itself.

It is clear that the traditional image is a cultural image. But following the above line of thought, it becomes apparent that technical images are also cultural images. The technical conditions of a optical apparatus do not make an image objective. The use of technology for image generation does not remove the layers of meaning and significance of the image itself. An image is an image. Even if it is produced by a machine.

I think at this point it becomes quite clear where I am heading. Data visualisations and photography share a number of similar attributes. They are both technical images that seem to be an objective representation of reality. But on closer examination it becomes clear that both are essentially cultural images. And they should be treated, interpreted and decoded as such.

The statement that data visualisation creates cultural images is important in two different ways.

First, it is important for the interpretation of data visualisations. As we have seen, they are more than a technical representation of data and facts. Data visualisations are cultural artefacts and need to be interpreted accordingly. And as all other cultural images, there are different ways to read and interpret them. So instead of simply talking about “insights” I would argue for a hermeneutical approach when dealing with visualisations. Visualisations should not only be interpreted on the grounds of their technicality but also in terms of their culturality.

Second, it is important for the creation of visualisations. I am very much aware of a certain suspicion regarding well-designed visualisations. I often encounter the prejudice that aesthetics is just getting in the way of proper data representation and that design just obfuscates objectivity. I strongly believe that this is not true – simply because the objectivity of an image is an illusion. So instead of negating the role of design in the creation of data visualisations, we should embrace and improve it.

I know that many colleagues in our community are uncomfortable with the notion of human interference with data visualisation. If designers “place themselves between the images and their significance” as Flusser puts it, the colleagues fear that the objectivity gets lost. But it is important to point out there was no objectivity in the first place. Actually, the lack of objectivity goes even deeper. As Johanna Drucker has pointed out in her book Graphesis: “Data are capta, taken not given, constructed as an interpretation of the phenomenal world, not inherent in it”. So the collection and generation of data is already culturally biased. And if we stay in the analogy between photography and data visualisation, one could point out the data visualisers do not only use an apparatus – they actively create new ones.

All this does not mean that data visualisation is random, artistic and meaningless. Quite the opposite. Just as photography, visualisations can make strong statements about reality. However, we need to be aware of the fact that both the data and the visualisation are constructions. A visualisation is an artefact in itself and not just a neutral conduit for data. It helps us to relate to the data – but it obviously shapes the way we perceive it. Just like with a photo, we should not only recognise what is depicted – we need to recognize the picture itself.

Photography and data visualisations can explain, enlighten and entertain. Both represent facts, stories and events. As such, both are meaningful and useful. And both photos and data visualisations are essentially cultural images.

This essay was reviewed by Moritz Stefaner. Being a “Truth and Beauty Operator”, he had to a lot to say about the topic. Special thanks for asking “what do you mean with ‘technical image’?”. It was originally published on Medium on 30. March 2017 under the original title: “Picture, Depiction and Deception”.

Design in Four Revolutions

21. February 2017
The progression of design is inextricably linked to the industrial revolutions. Interface- and Interaction design are the design disciplines of the third industrial revolution.

In order to understand interface and interaction design, it is useful to look at the history of design – and at the history of industrial revolutions. Every single one of these revolutions had its characteristic technologies that changed social, economic and environmental conditions. And each revolution had its specific design.

Industrial production and design are fundamentally connected. New technologies allow for new ways of production. Industrial products are not crafted but designed and mass-produced. A design is a template, the production is the implementation of the design.

This connection is not necessarily obvious. The Arts and Crafts movement was highly influential for the emergence of design as a professional discipline – but it was essentially anti-industrial although it advocated economic and social reform. In the middle of the nineteenth century, the working conditions in most factories were hellish and many workers lived in squalor. Furthermore, many of the industrial products had a poor quality compared to those created by a craftsman. As an answer to the poor quality, the Arts and Crafts movement emphasised traditional craft methods.

But some members of the Arts and Crafts movement – notably Henry Cole – realised that industrially manufactured goods also had the potential for creating durable and aesthetic products for the masses. Not a designer himself, Cole campaigned for improving the standards of industrial production and industrial design. He understood that by employing good and thoughtful designs it was possible to industrially mass produce goods of a high quality. So in his understanding, the designer was no longer a craftsman but someone who created templates and planned the production process while the machine and the factory worker implemented the design.

Industrial manufacturing and design are deeply related. So in a strict sense: if you create a unique chair, you are a craftsman – but not a designer.

I will come back to industrial manufacturing later. My main point right now is to illustrate the inextricable link between design and industry. As industrial revolutions have changed the working conditions, production process and product development, the design questions changed accordingly. So let’s have a look at a bit of history.

The zeroth industrial revolution

The printing press is usually not considered in the canon of industrial revolutions. And yet – it allowed the mass production of books, posters and pamphlets. It was definitely a revolution in terms of mass communication and it certainly was a catalyst for social change. Without printing, the Reformation would not have had the same impact.

Johannes Gutenberg is widely considered to be the inventor of modern printing. He introduced moveable type to Europe and invented a number of important methods and devices for printing. But more important for our debate, Johannes Gutenberg was the first designer. He was the first graphic designer – and the first type designer.

When I mention the “printing press”, it is important to point out that I mean a whole manufacturing process – and not simply the press itself. In order to truly revolutionise printing, the process had to become fast, flexible, robust – and cheap. It involved a number of different devices and techniques that are easily overlooked. And at the heart or the printing process was an inconspicuous invention that allowed for the mass manufacturing of letters: the hand mould.

The hand mould was used for casting moveable type. Each letter was cut in metal and then used to create a matrix. This matrix would be held in the lower part of the mould. Molten metal was then poured into the hand mould, casting the letter. Using this method, Gutenberg was able to quickly create large numbers of letters that were all copies of the designed template.

This process is an early precursor of industrial manufacturing. Gutenberg designed a product – the letters – and then mass-produced them. Furthermore, he designed the letters as basic elements and then used them to design the layout of a page. The modularity of moveable type enabled him and other printers to plan the layout of a page in a highly flexible and iterative manner, thus creating the disciplines of typography and graphic design.

The design of the letters was strongly influenced by the calligraphy of the time. It is based on the Textura Quadrata that was one of the leading book hands for bibles. Gutenberg formalised the Textura and optimised it for printing. Instead of coming up with a completely new design, his typeface is clearly a simulation of calligraphy. The aesthetic simulation of “old” technology in a revolutionary product is (by the way) a typical pattern in design history.

Although the printing press is not an “official” industrial revolution, it already introduced design problems and it demonstrates how fundamentally design and industrial production are linked.

The first industrial revolution

The first “proper” industrial revolution was triggered by the invention of the steam engine. Steam not only powered trains – it created to possibility to deploy large machines with enormous capabilities. These machines produced everyday merchandise (not just letters) in large quantities at low prices.

New manufacturing methods emerged that also created new design possibilities. A prime example for this is the no. 14 chair by Thonet. It was introduced in 1859 and became one of the best-selling chairs ever made. Its design is based on a new manufacturing process called steam-bending. The wood was heated with steam, bent into the required shape and then dried. All the tasks could be completed by unskilled workers – a craftsman was no longer required. The product was planned by designers and engineers and then manufactured by machines and unskilled workers.

So instead of conceiving and constructing each product individually, the planning and the manufacturing of a product were clearly separated. A new discipline – industrial design – emerged.

Even for us today, the no. 14 chair is an ingenious design. But in 1859, the chair was revolutionary. It was novel, elegant and comfortable – but also cheap, lightweight and durable. Furthermore, it could be disassembled and easily reassembled – making it possible to ship and sell the no. 14 chair in the entire word.

The no. 14 chair is another great example for the fact that design and industrial production are deeply related. The design of the chair would not have been possible without the means of production. But the production method itself would not necessarily have led to the design. This is still valid today. As new forms of production emerge, new chairs will be designed that facilitate the new technologies.

The second industrial revolution

The second industrial revolution introduced electricity. So with the second industrial revolution, a new type of product entered the households: the electric appliance. Different kinds of energy generation (lamps, stoves, ovens, etc.) were suddenly replaced with electricity and completely new types of products were introduced (vacuum cleaners, washing machines, radios, etc.). These new machines had to be controlled and they brought a new complexity with them. So electricity created the user.

In many ways, the electric appliance was a strange product. It was neither tool nor kitchenware. In order to introduce them to the households, a new visual language was required – and product design was born.

The first designs for these new products followed the tried-and-tested pattern. They basically looked exactly like “old” technology. A good example for this is the electric water kettle by AEG. It was designed by Peter Behrens in 1909.

The electric water kettle is very beautiful – but it still looks like a kettle to put on a stove. New technology is dressed up as old. It is not quite clear if this was intentional – but it made the introduction of the electric appliances much easier. The message was clear: “This water kettle looks and works just like the one you already know”. Even the completely new types of products pretended to be old technology. Vacuum cleaners tried to look like brooms, washing machines like laundry tubs and radios like a cupboard.

However, over time product design moved away from quoting traditional forms and tried to find a visual language that would not attempt to hide the new – but to emphasise it. After the First World War, it was relatively easy to break away from traditions and to develop new, modern aesthetics that embraced technology and tried to show the machinery as it is.

This new approach – often called “functionalism” – was conceptually oriented towards a utilitarian design. The idea was that the aesthetics of a product should be derived from its function. Consequently, the use of ornaments and “styling” should be abstained as they were irrelevant for the functionality of the product. So instead of finding a design that hides the technology, the design should be derived from the technology. The workings of a machine or the construction of a building should be visible and should define the overall form.

Conceptually, this approach was laudable. Instead of pretending that an electric kettle is “old technology”, designers were trying to find an aesthetic that reflected the qualities of the new technology.

In reality, however, this approach did not always work out. The main problem was that designers and architects mainly focused on the aesthetics of a product and not on its everyday use. They confused the workings of a machine with its functionality. Instead of designing for use, designers celebrated the machinery. In the end, technology became just a new form of ornament.

Even if it did not work out – the design of the second industrial revolution tried to address the aesthetics of technology based on the technology itself – and not on something pre-existing. Technology became part of the aesthetic discourse.

The third industrial revolution

The computer – in all its forms and networked states – is at the core of the third industrial revolution. And the design of the third industrial revolution is interface- and interaction design.

The previous industrial revolutions tried to replicate existing products but make the manufacturing cheaper, more efficient and aimed at large volumes. This is as true for Gutenberg’s printing press as for the Thonet chair. And it is partially true of the digital revolution: many digital products and services are faster and more efficient iterations of analogue technology. Word processing software is the digital iteration of a typewriter.

Furthermore, an important design strategy of the previous industrial revolutions is also valid in the era of computerisation: making technology accessible by referring to well-known, “old” technologies. Just as the electric water kettle by Peter Behrens pretended to be “old” technology, the desktop metaphor pretends to be based on items and elements of pre-existing work environments.

But the third industrial revolution is not simply another iteration of technological innovation. There is something genuinely new to it. I believe that the third industrial revolution is at its heart a design revolution. This statement might surprise some readers – especially those with a tech background – but let me explain.

There are fundamental differences between the third industrial revolution and its predecessors.

The first difference relates to the objective of the industrialisation. As we have seen before, the first industrial revolutions aimed at simulating manual labour through machines. Steam power and later electric motors replaced manual labour and human strength. The manufacturing process was broken down into clearly describable units that were either performed by machines or by workers at the assembly line. So the objective of the first industrial revolutions was the mechanisation of manual labour.

In the digital revolution, this is different. The object of the digital revolution is not to simulate the human hand but the human mind. As Frieder Nake put it: “computers are the mechanisation of intellectual labour” – or “Maschinisierung der Kopfarbeit” in the German original. Software is the automatisation of thought.

The second difference relates to the first one: the computer is not a single-purpose-machine. This sounds trivial – but it has far reaching consequences. In the analogue industrial age, machines were build and optimised for a specific task – bending wood, punching metal sheets or printing a newspaper.

The computer, on the other hand, is a universal machine. The hardware itself – in the strict sense of the CPU and the memory – has no specific use case. Even if we add hardware interfaces to this configuration, the computer becomes more specific – but it remains a universal machine. Input devices like keyboards, mice and touch screens as well as output devices like monitors, speakers and printers form a standard setup for most computers. This configuration limits the scope of possible applications, but overall it is still a highly unspecific system.

It is the software that specifies and defines the application. And within the limitations of the hardware, the software can be anything. Software itself is ethereal. It only becomes corporeal through interfaces. And transforming the ethereal into the corporeal is a design task.

To put it in a different way: on a sensual level, software only exists in the form of an interface. If you want to experience software and if you want to interact with it, you need an interface. And this interface is always the result of a design process. Design gives software a gestalt.

So interface- and interaction design not only makes the software accessible – it constitutes our idea and our understanding of a computer. Without an interface, the computer would not be present in our world.  This sounds esoteric – but it is not. Imagine a future without electricity. If you wanted to understand how a computer works, you could maybe figure out the relationship of the hardware components. But it would be absolutely impossible to understand how a computer was used.

But software is not only ethereal – it is also incredibly flexible and can simulate any kind of machine. This is a fundamental property of the computer that was already proposed by Alan Turing and then later on extended by the teams around Douglas Engelbart, Alan Kay and Steve Jobs. In his essay “Alan Kay’s Universal Media Machine”, Lev Manovich points out: “It was only Kay and his generation that extended the idea of simulation to media – thus turning Universal Turing Machine into a Universal Media Machine, so to speak.”

Software offers us an amazing optionality. It is possible to create any kind of software-machine within the hardware-machine that we call computer. Compared with analogue machines, designers and developers have an immense freedom for experiments, invention and creativity. Furthermore, the interface and the software itself have an intricate relationship that goes both ways. The interface is not just a representation of an abstract system. The interface also defines and demands how software is organised and what functionalities are required. As Johanna Drucker puts it in her book Graphesis: “We look at interface as a thing, a representation of computational processes that make it convenient for us to interact with what is ‘really’ happening. But the interface is a mediating structure that supports behaviours and tasks. It is a space between human users and procedures that happen according to complicated protocols.”

The desktop metaphor was not successful because it solved a technical problem but because it solved a usability problem. And it is important to point out that many other design solutions would have been possible. It proved to be an successful solution – but the user interface of the Xerox Star was not determined by the technology. As it is software, it could have been designed in a completely different way. Again – we sometimes tend to forget that out there are innumerable possible solutions that were not realised.

The extraordinary thing about software interfaces is that they are completely fictional, fabricated and imaginary. If you take an analogue camera apart, you can see how it works. It’s complicated, but you can figure out the relationship between the cogs, wheels and lenses. The controls are directly and physically linked to the workings within the machine. So the user interface of an analogue camera is determined by its mechanics. This is very different with computers. If you take a computer apart, you cannot figure out how the software was used and operated. There is no inherent and binding relationship between the hardware and the software interface.  On a computer, the technology does not determine the software interface.

We have agreed to interact with software in a certain way – via programming languages, command line interfaces or graphical user interfaces. But not out of a technical necessity but simply because it seems to work. So the software user interface is more of a social convention than a technical requirement.

All these observations lead to my statement from above: the third industrial revolution is at its heart a design revolution.

The design of an analogue machine can be derived from the technology. But software interface design has no technical form it can derive from. Due to its ethereal nature, software can only be revealed and experienced through design. Furthermore, due to the fact that the computer is a universal machine, the relationship between the software-machine and the software-interface is completely arbitrary. The only limiting component are the human mind and the human hand.

All this gives interface- and interaction design a relevance and a potency that goes way beyond the form-giving of the previous industrial revolutions.

Final words

The industrial revolutions are not sequential, clearly defined events. They are models that allow us to talk about social, economical and environmental change. Furthermore, none of these revolutions are over. We still print books, produce furniture, manufacture appliances and develop software, hardware and services.

But digital technology – in all its forms – is the dominant revolution of our time. And it is not only an industrial one. The digital revolution has great implications for almost every human being and for our planet. We are still right in the middle of it and its impacts and consequences are still not yet fully understood. Over the next few years, the relevance of digital technology will increase – and so will the importance of interface- and interaction design.

I believe as designers, we should be aware of our professional role and our responsibilities. Not only in a pragmatic sense within our team and our company – but also in the context of the history of technology.

This essay was reviewed by my colleague Prof. Dr. Jan Distelmeyer. He is professor for the history and theory of technical media. I got tons of valuable feedback from him and we discussed so many authors that in this case I will add a list of references for further reading. Some of the references are only available in German – apologies for that. If you speak German, I highly recommend Jan’s brand new book “Machtzeichen” – it was just released this January.  


In Defence of Intuition

13. January 2017
As design becomes more methodological and scientific, it is important to acknowledge that the core ability of designers is intuition.

In the last few years, design — and especially interaction design — has become more methodological. There are methods for all parts of the design process. For inspiration, ideation, interpretation, sketching, composition, building, evaluation, prototyping and implementation. Students like methods because they are fairly easy to learn and provide confidence. Clients like methods because they make the design process understandable and accountable. Furthermore, as design research is becoming more and more recognised in other academic disciplines, designers need to adapt to certain ways of writing and thinking about their work. If you want to publish the design of a new visualisation in an academic context, you need to adjust to the scientific expectations of the HCI community.

So it seems that design is not only becoming more methodological but also more scientific. This is not surprising. Design as a discipline has moved from “product beautification” to being a central part of product development. It has incorporated methodologies from human computer interaction, sociology, anthropology — but also from advertising and management. And with the rise of “Design Thinking”, creative methodologies were introduced to a wider range of professional disciplines.

In this essay, I don’t want to criticise design methodologies. (I’ll save that for later.) But against the backdrop of a structured and methodological design process I believe it is important to remind our community that there is one fundamental aspect to design that cannot be formalised in a methodology. And that is intuition.

Intuition is a difficult term. It is not a subject in design school. It’s nothing you talk about. Many designers get a bit queasy when asked about intuition. It is often associated with a impulsive, irrational decisions and aesthetic extravaganza. This is completely missing the point.

Intuition has been the subject of philosophical and psychological study. But its definition varies depending on the discipline and the context. So I would like to explore what intuition means for design.

Generally speaking, intuition is the ability to reach conclusions and make decisions without conscious reasoning. This is something every professional designer does on a daily basis. We know how to make social, conceptual and aesthetic decisions based on our intuition. We know how to achieve goals, solve problems and create effects. We know when something is right.

This sounds quite esoteric. But it’s not. Intuition is an essential and elemental ability of designers.

Intuition has a prominent role in nebulous situations. It allows us to act and to decide even if we just have little information and are dealing with unforeseen events. It enables us to handle ill-defined problems. And as most design projects are — by definition — open, vague, unclear and sometimes chaotic, intuition plays a prominent role in the process of finding the right design.

If you are working in a very strict operation and if you are not dealing with any unforeseen problems, you don’t need intuition. On the assembly line, you need manual skills — but you won’t use your intuition that much. As the setting for design work is quite different from working on the assembly line, intuition has a much more prominent role.

There are many clichés about intuition. In order to to understand what intuition is, I would like to talk about what intuition is not:

Intuition is not instinct

Instincts are deeply rooted in our biological self. They are behaviour patterns that are not learned or acquired. Instinctive actions are carried out in response to a clearly defined stimulus. Instinctive behaviour is characteristic in all members of a species.

Design sometimes tries to evoke instinctive behaviour through a certain visual language. This can be useful for marketing and advertising purposes. But triggering instinctive behaviour in the audience has nothing to do with intuition. These are two completely different concepts.

Intuition is not irrational

In my opinion, there is a tremendous difference between irrational and non-rational behaviour. Irrational is acting against better knowledge. Non-rational behaviour is — in the worst case — random and chaotic. Intuition can be non-rational — but it is not irrational.

Compared to the natural sciences, the design world does not offer a strict system for evaluating the quality of an outcome. But design is an extremely context-dependent process. So there are are a number of possible criteria for assessing the outcome of a design process. Is a design useful? Is it technically feasible? Is it robust? Is it understood and liked by its audience or by its users? Is it successful on the market? Is it socially, economically and environmentally responsible? Is the client happy? Does it win awards? And what is the feedback from fellow designers? Depending on the specific design, more criteria can be defined.

Decisions that are based on intuition should not be obscure. I strongly believe that it is important to talk about and evaluate them. For this, we have to use adequate language that reflects the process and qualities of the decision. A simple “I kind of like it” is not enough.

Designers make decisions based on intuition. The decision itself may not be based on a strict rational derivation. But this does not mean that intuitive design is detached from scrutiny. Intuitive design decisions can be discussed, tested and evaluated.

Intuition is not unscientific

I am in no position to discuss the role of intuition in the sciences. But I do believe that intuition is underrepresented in epistemology.

The natural sciences have a great conceptual framework and a great toolkit for testing a hypothesis. The scientific method allows for rigorous testing of new theories. Systematic observation, experiments with reproducible results and critical peer reviews make it possible to evaluate a new hypothesis.

But how do scientists come up with a new hypothesis in the first place? Not every scientific idea is derived from rational arguments and analytical reasoning. There are a lot of examples for scientists who came up with a completely unfounded new theory. Furthermore, a lot of scientific problems are systematic — such as two proven theories that contradict each other. In these controversies, intuition plays a powerful role.

Even in mathematics — the strictest of all sciences — intuition is recognised as a way to solve a problem. In the early twentieth century, the dutch mathematician Luitzen Egbertus Jan Brouwer developed a mathematical-philosophical theory called “Intuitionism”. Brouwer believed that intuition and time are fundamental to mathematics — and that both cannot be formalised.

Intuition has its role in the sciences — and many scientists understand the importance of intuition. In a conversation with Alexander Moszkowski, Albert Einstein famously said that “the really valuable factor is intuition”. And René Descartes noted: “The two operations of our understanding, intuition and deduction, on which alone we have said we must rely in the acquisition of knowledge.”

Intuition is not “intuitive”

I am always a bit sceptical when someone describes an interface as being “intuitive”. Intuition is a purely human quality. An object or a system simply cannot be intuitive. The sentence “this software can be used intuitively” actually means that a piece of software can be understood and used by someone based on his or her intuition. Human beings — not software — are intuitive.

But does intuition help us to understand and use software interfaces? This is a surprisingly tricky question. As mentioned above, intuition is helpful when you are dealing with an unclear situation, are encountering something unforeseen or are handling an ill-defined problem. So — consequently — you only need intuition if you are dealing with a bad interface. Good interfaces are actually those where you don’t need intuition in order to complete a task.

The above statement may sound a bit surprising. But if a software interface is used effortlessly it is simply because it is predictable, clearly defined and based on interactions that we have learned before. We don’t need our intuition to use a book. We might need intuition in order to understand and interpret the text. But the handling of a regular book does not require intuition.

In reality however, software systems are extremely complex and even good user interface designers cannot anticipate every possible condition. So users are dealing with unforeseen events and unpredictable situations and dialogues. In these cases, intuition can help the users to solve the problem. And good interface design can support the users in training their intuition. But I am sure this is not what is meant with the label “intuitive software”.

Designing an usable, understandable, elegant, efficient and delightful software interface requires intuition. Using it should not.

Intuition is not talent — it can be taught

I strongly oppose the notion that intuition is a nature-given characteristic that some people are born with. Everyone has a disposition for intuition. And — most importantly — intuition can be trained, honed and cultivated. This training is an important part of design education. Students are confronted with ill-formed briefings, create designs and then get feedback on their process.

More importantly, intuition is not limited to the design world. Most jobs that require some sort of decision-making are involved with intuition. Doctors, politicians, teachers — they all have their own domain-specific intuition. Some jobs require a higher degree of intuition then others. And I believe that education should reflect this.

But as most teaching is based on the instruction of systematic knowledge, this can be quite a challenge. How can you teach something if there is no right and wrong — only good or poor solutions? And — as a teacher — how can you convey your feedback in a way that is not superficial and opinionated?

I strongly suggest to take a look a how singing is taught on a professional level. It is really, really amazing to observe professional opera singers teach young, aspiring talents. Just watch this clip where Joyce DiDonato is teaching a Master Class at Carnegie Hall in 2016. It is intriguing how she physically and verbally critiques and reflects on both artistic expression and technique.

Intuition can be trained, criticised and developed. Being a design educator, I feel very passionately about this point. In design education, we need to be more aware of teaching intuition.

This part of the design education is very similar to teaching fine arts and music. As a teacher, you need to work very closely with your students and give them direct feedback on their work. Furthermore, you have to develop an appropriate language that reflects the subtleties of our discipline. Just like in art and in music, words have their own meaning and their own domain-specific context: strong, cold, balanced, discreet, contrast, noise, power, clarity, order, chaos, guidance, support, attention, (I could go on for a while) can be used to describe intuitive concepts that go beyond the direct meaning of each word. We need to cultivate this language and foster a practice of teaching intuition.


In this essay, I tried to demonstrate and discuss the importance of intuition in design. I believe we should give intuition the recognition it deserves and bring it back into the centre of the design process and design education.

Mainstream interaction design currently has a tendency to formalise the design process. This is not surprising as our discipline is industry oriented and aimed at designing concrete outcomes. Digital products are becoming more and more complicated. And the quality of the interaction design is becoming more and more important for the success of the product. So demanding a more formalised design process is understandable.

However, I believe it is good to remember our community that not everything can be formalised. We have to live with uncertainty in the design process. And we should remind ourselves that our greatest capital is the ability to make creative, intelligent and successful decisions in unclear, contradictory and complicated circumstances. This ability is called intuition.

Intuition does not necessarily lead to good design — but good design is always based on intuition.

This essay was discussed with and reviewed by my esteemed next-door-colleague Prof. Dr. Frank Heidmann. Many thanks for the great discussion and the feedback! It was originally published on Medium on 6. January 2017 under the original title: “In Defence of Intuition”.

« Older postings |

RSS Feed | Twitter @borism | Impressum und Datenschutz