Demystifying the black box: innovating Big Data through Service Design Activism [part 1]

Over the past six months, I have been working actively across tech, design and social innovation sectors to explore the role of design and designers in uncovering the ‘black boxed’ culture of data-enabled service productions. Here, I lay out the challenges we are seeing in today’s tech landscape — its failure for transparency, inclusion and fairness — and propose ‘Service Design Activism’ as a way to democratise digital service innovation practice.

This is part one of the series ‘Demystifying the black box: innovating Big Data through Service Design Activism.’ Stay tuned for part two!

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Where ‘data’ exists in our everyday life (see bottom for image sources)

Data in everyday life

We have all witnessed the power of data: Facebook’s Cambridge Analytica data breach, Russian interference of Trump campaign and China’s social credit system are just a few of the many tech scandals that have left a profound impact on people’s lives. It is almost difficult to predict what the next big scandal is — yet here we are, inseparable from our digital devices, exposing, sharing and communicating our personal stories in an online pool saturated with data.

Figures estimate that 2.5 quintillion bytes of data are created every day, amounting to roughly 7000 tweets, 40000 Google enquiries and 2 million emails sent every second. As a transmittable computer information, data is now entering all walks of human lives, from policies and government, art and design, cities, homes, education, work and well-being. Now with its distributed and connected assemblages, data takes on many forms and implications, suggesting that data is never just the data. We haven’t come a long way since the first appearance of this word — in the 1640’s — when its plural ‘datum’ meant ‘something given.’ Yet, what originated as a broad term meaning ‘something given,’ the discourse of data – including its cultural and social backlash – has now shifted to carry a highly technology-centric connotation.

Weaponisation of data

Algorithmic bias in the Criminal Justice System (Image source)

In the US, recidivism risk algorithms are used to assess how likely a person will commit a crime again. The intention is to ‘replace subjective judgment with objective measurement’ by displacing human error and bias from complex decision-making. However, American Civil Liberties Union found out that in fact, black men were imposed 20% longer sentences than white men for convicting similar crimes in the federal system. In another case, studies have shown that for the same charges, prosecutors were more likely to pass death penalties for black people than for white people.

As long as human designers and the cultural, social and political discourses which shape their subjectivity exist behind every data algorithms, there also exist manufactured injustices and inequalities. What we see in this case is data ‘captured and recuperated by existing concentrations of power’ generated at a particular place at a particular moment in time, by identifiable individuals and institutions with a particular ideology. They are problematic because they present themselves as the universal truth, when in fact, they only yield ‘a partial picture of the world.’ The fundamental issue is also that ‘people tend to trust results that look scientific,’ claims mathematician, Cathy O’Neil, who calls this phenomenon, ‘the weaponisation of an algorithm.’ To put it another way: every aspect of our lives – our bodies and systems – are governed by these omnipresent and invisible forces.

Positioning data in service innovation

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IDC FutureScape: Worldwide IT Industry 2018 Top 10 Predictions (Image source)

IDC predicts that by 2021, 50% of global GDP will be digitised. As shown in the image above, within this digital transformation space, 90% of large IT enterprises are predicted to generate revenue from data-as-a-service by 2020; this includes the sale of raw data created through metrics, insights and recommendations, which, altogether, is a 50% increase from 2017. Given that data is intertwined with many other digital technologies, it is evident that data will continue to hold a vital economic value in many years to come.

With this rapid demand for service digitisation, businesses are now challenged to manifest the ‘hidden’ machine-to-machine interactions to ‘expand its focus beyond the “service experience” to include these technology- connected design activities.’

The rising use of data are also met with the emergence of various new actors. Below are lists of stakeholders proposed by Prendiville, Gwilt and Mitchell for creating services using data:

  • Data providers
  • Data collectors
  • Data specialists
  • Service providers
  • Managers and innovation planners
  • Service designers
  • Data consumers

Management of these new actors will mean a new opportunity for service innovators to advance the use of Big Data from a mere add-on function to a catalyst for establishing a completely new service models. So how to go about managing this complexity? In the next section, I will delve into the design strategies of Big Data, focusing primarily on the two trending approaches that are dominating today’s design scene: human-centred design and systems design. Using practical examples from design and innovation consultancies, I will explore how Big Data and emerging technologies are used in the current design and innovation landscape — and why it matters. Stay tuned!

Top image sources

Black Mirror (no date) IMDb. Available at: (Accessed: March 18, 2018).

Calabresi, M. (2017) “Inside Russia’s Social Media War on America,” Times, 18 March. Available at: March 18, 2018).

e-Estonia (2017). Available at: (Accessed: November 1, 2017). Fjord (no date) Fjord. The Design & Innovation consultancy. Available at: (Accessed: November 1, 2018).

Grassegger, H. and Krogerus, M. (2017) “The Data That Turned the World Upside Down,” Motherboard, 28 January. Available at: mg9vvn/how-our-likes-helped-trump-win (Accessed: November 29, 2017).

Home Page of EU GDPR (no date) EU GDPR Portal. Available at: (Accessed: November 18, 2018).

Hong, K. (2017) “Big data meets Big Brother as China moves to rate its citizens,” Wired, 21 October. Available at: score-privacy-invasion (Accessed: October 21, 2017).

Lupi, G. (2017) “How we can find ourselves in data,” TED. Available at: https:// (Accessed: November 1, 2017).

O’Neil, C. (2016) Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. London: Penguin, 2017.

Wong, J. C. (2017) “Uber concealed massive hack that exposed data of 57m users and drivers,” The Guardian, 22 November. Available at: technology/2017/nov/21/uber-data-hack-cyber-attack (Accessed: March 18, 2018).

A designer + researcher + strategist with a background in graphic design and cultural studies, Megumi Koyama is a recent graduate of Central Saint Martins MA Innovation Management course interested in design for civic engagement and empowerment. She is currently based in London, UK seeking opportunities in design for social good/tech for good. Contact koyamamegu[at] for opportunities and collaborations!


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A designer + researcher + strategist. Graduate of CSM MA Innovation Management // #designforgood #techforgood

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