Data as a New Medium of Design
By: Alexandra Schwartz
Tags: Big Data, Data Designer, Data Design, Data Science, Service Design

Businesses now view data as a raw material, exclaiming that ‘data is the new oil’, claimed to have first been said by Clive Humby, the mathematician who established the Tesco Clubcard loyalty programme (Marr, 2018), and King et al. (2017) referring to data as the “currency of the day”. With this forthcoming trend of commoditising data, designers have, now more than ever, started to use data as a tool to further inform their instincts and qualitative research about their users (Huang, 2016).

More and more design agencies have started to integrate data into the process of designing new services and products for their clients. With the increasing amounts of data, it has become crucial to develop methods to capitalise on big data. Topp (2016), a design and data lab from Sweden, however, has started using data beyond the traditional way of using it as a source to gather demographic insights or monitoring usage. Topp has started to use data as a design material that they believe opens up “creative and strategic paths, which are otherwise left untapped.” Topp runs a series of workshops that experiment with design-based ideation and sketching with data. Interestingly, they believe that imposing a human perspective and our ability to create narratives enables us to “read stories from the data”, which are meant to spark ideas and give designers a different perspective and point-of-view to work from. They don’t believe that the stories need to be statistically relevant and discuss the interpretation of data as fundamental in the use of data throughout the design process. On the one hand they argue that you don’t have to be a data scientist to be able to gain creative insights from data. However, on the other hand, they acknowledge that the ideas they generate by using data as an ideation tool “would of course be totally crazy talk if we were data scientists”.

The late Hans Rosling started his career as a professor in Global Health, yet became famous for his work as a statistician and his talks and presentations around the world about the power of statistics. He was featured in a documentary called “The Joy of Statistics”, which argued that statistics give people a perspective on the world that we cannot get in any other way. “Statistics tell us if the things we think and believe are actually true” (Rosling, 2010). According to Claeson (cited in Watts, 2017) “Hans had the unique quality of simplifying complexities and presenting them with humour, which made us listen, question, and have a more informed debate”. This quote illustrates how powerful data can be when communicated in a creative and approachable way to people less familiar with data.

Pamela Pavliscak (2016) is the founder of Change Sciences, a UX research and strategy firm, and her approach that is part ethnography, part psychology and part data science “translates future vision into tangible everyday possibilities” (Change Science, no date). She makes the case for algorithms as a new medium of design. She states that experience designers are becoming more reliant on designing with algorithms, and believes that design has become more about augmenting the human experience and creating a better emotional awareness through the use of algorithms.

Furthermore, data is becoming a new form of art, as the “boundaries between digital technology and ‘traditional’ creative practice dissolves” (Murray, 2014). Data is becoming a medium for art, with artists comparing it to paint or a canvas, and many believing that data should not be left to statisticians and the corporate world alone, highlighting the importance that data is playing across industries and disciplines (Murray, 2014). One such example is Information is Beautiful, an organisation that creates beautiful interactive data-visualisations and infographics, based on “patterns and connections across art, science, health and pop, which unveil the intricate, invisible and sometimes hilarious stories lurking in the data, information & knowledge surrounding us” (Information is Beautiful, no date).

Data visualisation from Information is Beautiful (2018)

With data no longer only informing business-critical decisions but feeding into art, design and even the creation of music, the rising of data as a new medium has drawn steady judgement by some critics, who believe that the use of data “tampers with creativity” and is best left to the business world (PromptCloud, 2017).

The role of ethics when designing with data

“Data by itself is at best meaningless, and at its worst, misleading” — Pardi, 2016

The digitisation of our world can undeniably be seen as a double-edged sword. While some highlight the benefits that digitisation and data have brought to our society (Sabbagh et al., 2012), others highlight the detrimental effects it has and will continue to have on society (Loebbecke, 2015). The advances that technology is making will continue to accelerate at a rapid pace (Treseler, 2015).

Murray (2014) argues that big data cannot guarantee an objective truth, as the strengths of data, its ubiquity and malleability, are also its weakness, with growing concerns over intrusion of privacy and security. He calls out the “flaws in the vision of social positivists who talk of data as some kind of panacea for the world’s ills”.

Zwitter (2014) argues that big data has created the need for ethics to be rethought, particularly in relation to individual moral responsibility, as individuals lose the ability to understand its potential and make informed decisions. Social network data, health data generated by wearable devices, emails and data from phone applications all have the potential to be misused in unethical ways. Individuals are not familiar with the unintentional consequences of their digital footprint. “Existing privacy protections focused on managing personally identifying information are not enough when secondary uses of big data sets can reverse-engineer past, present, and even future breaches of privacy, confidentiality, and identity” (Richards & King, 2014). The number of breaches in data security have been frequently featured in the news and media. One of the more prominent examples is when Cambridge Analytica illegally accessed millions of Facebook profiles and managed to collect vast amounts of personal data. They were able to understand and even attempt to alter people’s political views, potentially having affected the most recent US presidential election (Halpern, 2018).

Pavliscak (2016) draws attention to the rise of emotion-sensing apps and devices and encourages using data and creating algorithms based on this data in a minimalist way. Halpern (2018) also recognises the dangers of this sensitive data, as it lays the foundations for psychographic manipulation, “by appealing directly to their emotions, using increasingly segmented and sub-grouped personality type designation and precisely targeted messaging based on those designations.”

In recent years, one of the most prominent organisations that has come under scrutiny for how data is collected and used is a US government agency — the National Security Agency, also known as the NSA. Supporters defend its invasive collection of phone and internet records on the basis that ‘only’ metadata is collected — who you call, when you call and for how long — arguing that as the actual content of the conversations is not recorded, the threat to privacy is inconsequential. This is an argument that is misleading at best. Probably one of the most controversial statements that has been made in relation to the ethical use of data and how metadata nowadays “tells you everything about somebody’s life”, was by General Michael Hayden, former director of the NSA (quoted in Cole, 2014) stating “we kill people based on metadata”. This shocking statement is a testament to the extent to which data can violate not only ethical standards, it also carries far greater risks to humanity. When a set of data is relied upon for its (meta as) knowledge to inform life or death decisions, omitting the role of the human also omits the value in empathy, values, emotional intelligence and humanity.

The shortfalls of data interpretation

Howard (quoted in Pollack, 2012) says that “data doesn’t leave any room for bullshit. […] People will argue, but you can show them the data. It doesn’t lie.” However, Topp (2017) argues that without human interpretation, data is just a description of a reality.

“There is no inherent value in any piece of data because all information is meaningless in itself. Why? Because information doesn’t tell you what to do.”

— Beau Lotto (quoted in Pardi, 2017)

Pardi (2017) makes an interesting observation about the dangers of relying on data alone, as a designer, engineer or any role that creates things. He states that data is only information that does not represent objective reality. He makes a compelling case for the fact that data is “highly contextualised by the individual doing the processing”, as different people will interpret data in different ways and their context has a strong impact on that interpretation. The example he uses is as follows.

Read the text aloud:

w at a e y u ea ing n w?

If you read “what are you reading now?”, you did what many people would read with the same “data”, despite the sentence not containing a single English word. Your brain filled the blanks due to your relationship to the English language and as you were primed to “Read the text aloud”, which influenced the outcome. Yet if you were snacking on something, hungry or sitting in a restaurant, you might have read “what are you eating now?”.

The point is that the role of the data scientist is exactly that — making sense of data through interpretation and turning the analysis into actionable insights for the rest of the organisation. This example shows that data can never be an objective truth as it relies on the subjective interpretation of one or many individuals, all seeing the data from their perspective and context. Similar to King et al. (2017), Pardi (2017) implies that being data- driven, with the emphasis on “driven”, is not a good approach when wanting to work creatively with data. However, he does believe that data can support creative and innovative outcomes. The facts should inform designers, so that they can experiment with future possibilities to discover insights that the raw data cannot provide.