Data Science in the Design Process
In recent years the digitisation of our lives and environments has created a surge in digital data. We leave behind digital traces of our behaviour both online and even offline, through the rise of wearable technology and the presence of our mobile phones. Technology has enabled the storage of this data by companies, who have started to realise the value that this data can have for their products, services and the marketing thereof. According to Harvard Business Review, the role of data scientists has become “the sexiest job of the 21st century” (Davenport, 2012), and organisations from all industries are using data science to extract value from the large amounts of data (big data) they are collecting.
The need to use data science has also reached service design agencies, as more and more clients put pressure on their design agencies to incorporate the use of data into their ways of working. This study therefore aims to address to what extent service designers include data science in the design process. The approach of Research through Design is used in order to investigate how designers have been using data science, as traditionally the design process is deeply rooted in qualitative research. The research uncovers the broad challenges faced by designers working with data and how data can be used not only to complement qualitative insights, but explores data as a new medium of design. Furthermore, a framework and toolkit are suggested as proposed solutions, and the report illustrates how the prototypes were developed, tested and iterated. Next steps in the development of the proposed solutions are highlighted and finally, recommendations are made for further areas of practice-based academic research in this field.
Big data has become a buzzword that has captured the attention of businesses across all industries, governments and the media (Davenport & Patil, 2012). The collection and storage of data has become simpler and cheaper than ever before. Our lives have become digitized, from having conversations online, to sharing holiday photos, checking into flights and even dating online, where we leave behind traces of our activity every step of the way. We now create as much information about ourselves every two days as was created from early civilisation up to 2003: 5 exabytes every 48 hours (McGuier et al., 2013; Gobble, 2013). With the developments of the smart phone and wearable tracking devices, we now no longer produce data only when we are logged on to a computer at work; numbers are permeating our homes and personal lives. “Sleep, exercise, sex, food, mood, location, alertness, productivity, even spiritual well-being is being tracked and measured, shared and displayed” (Wolf, 2010).
Black Mirror is a British television “sci-fi anthology series [that] explores a twisted, high-tech near-future where humanity’s greatest innovations and darkest instincts collide” (Netflix, no date). Season two, episode one ‘Be right back’, written by Charlie Brooker and directed by Owen Harris, shows how people can be recreated, based solely on their online identity. A recent widow learns about a new online service, which re-creates her deceased husband through the use of algorithms and his social media and online history (see figure 2).
Only a little over a decade ago, the list of most valuable companies mainly consisted of manufacturing and production companies, such as General Electrics and Ford, yet today companies that generate value from information, such as Google, Amazon and Facebook, have become top of the leader board (Osman & Mines, 2015).
Data science and big data are closely interlinked, yet not identical. Big data relates to data capture, transfer, storage, archiving and analysis amongst other things, whereas data science is based on extracting information from data through algorithms, applied mathematics and statistics (Osman & Mines, 2015). Arguably, it is a foundation that companies such as Google and LinkedIn are built upon as well as having become increasingly popular in scientific research (Hey et al., 2010; Tolle et al. 2011).
Wolf (2010) describes an almost obsessive relationship to data, stating that “a fetish for numbers is the defining trait of the modern manager. Corporate executives facing down hostile shareholders load their pockets full of numbers”. Indeed, the business world has become accustomed to the use of data, as quantifiable data and numbers allow for easy comparison and testing. Furthermore, Alharti et al. (2017) argue that big data can support organisations delivering better and more personalised experiences and thus increase efficiency, profitability and is one of the most important elements in gaining competitive
advantage, as it allows businesses to be innovative in new ways (LaValle et al., 2011). However, McGuier et al. (2013) discovered that Chief Marketing Officers (CMOs) only make their decisions based on data analytics 29 percent of the time, highlighting the insignificance of data when not used properly. This might be due to the many barriers that organisations face in taking full advantage of the value that big data can present, such as “outdated IT infrastructure, the inherent complexity and messiness of big data, lack of data science skills within organizations, privacy concerns, and organizational cultures that are not conducive to data-driven operations or data-driven decision making” (Alharti et al., 2017, p. 286).
The growing amounts and richness of data is meaningless if it can’t be interpreted and the idea of hiring a dedicated resource to manipulate and extract meaning from data only emerged in 2001, when the term ‘data science’ was first used in a paper by Cleveland (2001; Pollack 2012). This led the way for many universities to promote the field of data science by opening data science institutions and centres to formally teach data science as a profession, such as the Centre for Doctoral training in Data Science at Edinburgh University, the Institute for Data Science at Berkley and the Centre for Data Science at New York University, to name a few. A little over a decade later, data science has become essential in every industry with endless authors referring to data as the “currency of the future” and comparing it to gold (Pollack, 2012; Johnson, 2012) and data scientists have become integral to the success of this new currency. Bakhshi & Mateos-Garcia (2014) define data scientists as “experts who use analytical techniques from statistics, computing and other disciplines to create value from new (‘big’) data.”
With the increasing amounts of data, the analysis of the data has become more complicated than ever before and the solution for many organisations has become to hire data scientists and expect them to wield their magic wand to fundamentally transform their businesses. However, in practice this is not that simple (Wettersten & Malmgren, 2013). Many companies are trying to understand how to position data teams within their business. At snack company Graze, the data team reports directly to the Chief Executive Officer, Anthony Fletcher. He states that this wasn’t a typical approach, but they wanted to create the right culture around data within their organisation from the start. Data scientists are responsible for delivering actionable insights to stakeholders within the business and need to be made accessible for departments such as sales, marketing and finance (Davis, 2016). Nowadays, the team works across the business, which on the one hand is attractive for data scientists, as they get to work across varying types of problems and on the other hand it “democratises” the data throughout the entire workforce.
The ever-increasing amount of data that is being produced at rapid rates has outpaced the ability to manipulate, analyse and interpret the data. Thus, data scientists have become a rare and much-needed talent commodity in the business world. Parsons (quoted in Davis, 2016) proclaimed that “data scientists are the rocket scientists of the digital world and the role of the Chief Data Scientist (CDS) is emerging” and even the esteemed Harvard Business Review (Davenport, 2012) referred to the role of data scientists as “The Sexiest Job of the 21st Century”.
Treseler (2015) makes the point that using scientific methods to test new things in medicine, engineering and other safety-critical systems is common practice, yet using this in the design of consumer products and websites is a relatively recent phenomenon. Looking at the number of articles published about data in relation to business, technology and marketing, it becomes apparent that data is the topic du jour, Treseler (2015) argues the case for taking “data science” beyond the roles of statisticians and enabling designers to make data science part of their skillset.
There is a popular thought that designers make their decisions based on instincts and creative intuition, which can lead some to believe that design is never grounded in data and cannot be considered an empirical discipline. Thus, design stands in opposition to data science (King et al., 2017). Rightly so, design of products and services is grounded in building empathy with its users and creating “artful” experiences, through an explorative and creative process. Unlike the epistemological processes of “science”, “design is emotional. […] Design cannot be rationalised and constrained” (King et al., 2017, p. xi). Some professionals (DigiCult, no date; Esslinger, 2017) worry about the potential constraints that data could impose on a designer’s intuition, experience and creativity.
On the opposite end of the spectrum and from an extreme point-of-view, data science is seen as an indisputable truth, creating a much sought-after certainty for business leaders. King et al. (2017) describe an extreme view of gathering data from millions of users believed to answer all design questions and as such, that data can replace design. In fact, Denham (2018) even hails data scientists as the UX designers of the future.
Waechter (2016), placing design and data science on opposite ends of the spectrum, believes that designers and data scientists “often don’t speak the same language, let alone share a common understanding of the desired user experience”. This notion leads to the question whether or not data science and design can live together in synergy and create a reciprocal relationship of give and take? Wettersten & Malmgren (2018) seem to be exactly of that conviction. Wettersten, a design director and Malmgren, a data scientist, both started working together closely when IDEO and Datascope Analytics amalgamated their teams (Wettersten & Malmgren, 2013). They refer to using data science in their famous human- centred design process as ‘human-centred data science’, which they state results from interdisciplinary teams and suggest that rather than one person becoming both a designer and data scientist, practitioners should work together and learn from each other.
On the one hand, it is argued that data scientists can learn to build more empathy with the research subjects when they participate in design research. Furthermore, design methods, such as visualisations and sketches, can help data scientists see patterns in data during the analysis phase (Osman & Mines, 2015). On the other hand, designers can also learn a lot from data science, such as using metrics to align with business goals, and rather than having to rely on ‘gut instinct’, they can use data and metrics to test and evaluate their assumptions and hypotheses (Huang, 2016).
The need for any field to become more “data-driven” is a need that is increasingly familiar to people (King et al., 2017). Organisations are relying more strongly on data to aid their decision making, including decisions about design and user experience. Although the term “data-driven design” is something that has become popular in the literature, King et al. (2017) discuss three different ways to think about data and how it is used within the design process. They discuss the familiar terms data-driven and data-informed and have additionally coined the term data-aware.
The following outlines the definition of those three terms:
Ngai (2016) believes in the benefits of complementing qualitative insights with quantifiable data. He explains that for many companies, design and data are interconnected, as there is a constant stream of data that monitors hundreds of metrics and iterations. He states that “while design instincts are still valuable, data and analytics can help you hone your product understanding and ensure your decisions satisfy stakeholders”.
Due to the increased amount of readily available data, organisations have come under pressure to utilise their data sets. This pressure is fed down to the agencies that are hired by organisations and designers are expected to support the qualitative insights they collect with a quantified analysis of the client’s data. Hertto (quoted in Likkanen, 2017), a quantitative research specialist, criticises the fact that too many projects are under pressure to gather quantitative data without purpose and end up with “data that is non-actionable from a design point of view”. This is supported by an argument by Esslinger (2017), who criticises that data cannot easily support every design decision. He effectively argues that using data based on past behaviour to shape future product development is a pitfall for many. The example he uses is the case of Motorola, when the company rejected a proposal for a touchscreen smartphone, because market data concluded that consumers wanted to buy phones that were similar to Nokia at the time. Evidently, the designer’s insight was superior to the data-based insight on what should be created (Likkanen, 2017).
Bakhshi & Mateos-Garcia (2014) highlight the fact that most people believe that data science work is the exact opposite of being ‘creative’, with many considering it to be routine, predictable and even boring. Digicult (no date) states that one of the reasons designers have difficulty working with data is that they see their work as a form of art and they are worried that their “creativity and intuition [will be] replaced by data and facts”. Pardi (2017) argues that within the creative process, data should be used to inform us of the facts, which can serve as the basis for asking questions and experimenting with the ‘adjacent possible’, in order to discover insights and potential, which the raw data is not able to provide. He argues creativity is an exploration of possible outcomes, yet it can only be a small percentage of the possible, as our memories, biases and the perspectives which we draw upon limit our imagination.
This is where both diversity of experiences and backgrounds in a team become invaluable, as well as the use of data that can inspire different perspectives, new thoughts and more importantly raise more questions that might not have been considered as a possibility.
If you are interested in this topic, you can read the other articles in this series on the below links:
Bakhshi, H. and Mateos-Garcia, J. (2014) Data science work is creative work (Part 1). Avaialable from: https://www.nesta.org.uk/blog/data-science-work-is-creative-work-part-1/ (Accessed: 7 October 2018).
Cleveland, W. (2001), ‘Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics’, International Statistical Review, 69 (1), pp. 21–26.
Davenport, T. and Patil, J. (2012) ‘Data Scientist: The Sexiest Job of the 21st Century’, Harvard Business Review, 90, pp. 70–76.
Davis, H. (2016) The role of a data scientist and why we need them. Available from: https://www.raconteur.net/technology/the-role-of-a-data-scientist-and-why-we-need-them (Accessed: 13 October 2018).
Denham, L. (2018) Data Scientists Are The Next UX Designers. Available from: https://channels.theinnovationenterprise.com/articles/data-scientists-are-the-next-ux- designers (Accessed: 6 October 2018).
DigiCult (no date) 3 things you need to know about data-driven design. Available from: http://digicult.it/digimag/3-things-need-know-data-driven-design/ (Accessed: 8 October 2018).
Esslinger, H. (2017) The Big Data Challenge. Available from: https://www.frogmut.com/2017/05/08/big-data-challenge/ (Accessed: 9 October 2018).
Huang, R. (2016) How designers can use data to create amazing work. Available at: https://www.invisionapp.com/inside-design/how-designers-can-use-data/ (Accessed: 10 October 2018).
Johnson, J. (2012), ‘Big data + big analytics = big opportunity’, Financial Executive, 28 (6), pp. 50–53.
King, R., Churchill, E. and Tan, C. (2017), Designing with Data. Sebastopol, California: O’Reilly Media Inc.
Likkanen, L. (2016) Tools for Data-Driven Design of Web Services. Available from: https://www.linkedin.com/pulse/tools-data-driven-design-web-services-lassi-a- liikkanen?trk=mp-reader-card (Accessed: 9 October 2018).
McGuire, T., Meyer, C. and Stone, D. (2013) The Data-Driven Life. Available from: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-data- driven-life (Accessed: 12 October 2018).
Netflix (no date) Black Mirror. Available from: https://www.netflix.com/gb/title/70264888 (Accessed: 15 November 2018).
Ngai, J (2016) Designing with data — interpreting and analysing data as a designer. Available from: https://uxdesign.cc/designing-with-data-ed721ffa008e (Accessed: 9 October 2018).
Osman, A. & Mines, K. (2015) ‘Data Science as a New Frontier for Design’, International Conference on Engineering Design. Milan, Italy, July 2015.
Pardi, B. (2017) If You Want to Be Creative, Don’t Be Data Driven. Available from: https://medium.com/microsoft-design/if-you-want-to-be-creative-dont-be-data-driven- 55db74078eda (Accessed: 4 October 2018).
Pollack, N. (2012) The exabyte revolution: how Kaggle is turning data scientists into rock stars. Available from: https://www.wired.co.uk/article/the-exabyte-revolution (Accessed: 12 October 2018).
Tolle, K., Tansley, S. and Hey, A. (2011), ‘The Fourth Paradigm: Data-Intensive Scientific Discovery’, Proceedings of the IEEE, 99 (8), pp. 1334–1337.
Wettersten, J. and Malmgren, D. (2018) What Happens When Data Scientists and Designers Work Together. Available from: https://hbr.org/2018/03/what-happens-when- data-scientists-and-designers-work-together (Accessed: 5 October 2018).
Wolf, G. (2010) The Data-Driven Life. Available from: https://www.nytimes.com/2010/05/02/magazine/02self-measurement-t.html (Accessed: 12 October 2018).
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