The Concept of ‘Data Humanism’ in Response to Georgia Lupi’s Works

A CRITICAL ANALYSIS OF AND RESPONSE OF GIORGIA LUPI
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A critical analysis of and response to the Work and Position of Giorgia Lupi
1 Introduction
This essay critically assesses the concept of ‘data humanism’ in response to Lupi’s works (Lupi, 2017). While Lupi believes that we, as data visualization practitioners, should focus on making data more real and tangible, my perspective of data humanism is that as long as the information is understandable, which means that one can draw patterns or relationships between different variables, then it can be regarded as humanist. As visualization practitioners, we should focus on transforming data into something that is as relatable to the audience as possible, allowing them to interpret concepts, trends, and relationships without straining.
2 Critical Analysis
1 The Artist
Giorgia Lupi is an information designer and a partner at Pentagram in New York. She is an Italian by profession and is famous for her humanistic approach to data. She challenges the impersonality of data and designs and engages in visual narratives that link up numbers to create a visual perspective of what they stand for in terms of telling stories, talking about people, and sharing ideas. She has built rich visually driven experiences in data for various clients including Google, Bill & Melinda Gates foundation, the United Nations, IBM, and Starbucks among others (“Pentagram”). Her perspective on the humanization of data leads to the question of whether indeed data can be human. She tries to make data more tangible, rather than abstract since she believes that such an approach would make it more understandable. This approach makes it important to assess whether indeed data can be converted into a tangible form for it to be understood. In this essay, I believe that even abstract data can be humanized as long as one can relate it to real life situations.
2 Artist’s Perspective
Every activity in the modern world, including the economy and our social lives revolves around data (Mills, 2018). Consequently, unlike Lupi’s assertion of making all data tangible as a way of humanizing it, some of these information represented by data cannot be represented in a tangible manner, but can still be structured in a way that is easier to understand, which means that data can still be humanized but in a different way. Big Data is no longer a mysterious incident reserved for the dystopian future. Instead, it has become a commodity and a special feature in the modern times, prominently used in accounting for different things including currency, goods, and services. For example, the amount of goods sold, the valuation of an economy, and strength of a currency are all expressed in form of data. Today, it is virtually possible to categorize everything in terms of data. For example, we can learn about ourselves by determining the number of times we undertake a given activity, such as checking our phones, dressing, and even receiving compliments, and representing this information in either charts or other ways of data visualization (Lange, 2019). This process of converting the information into concepts that are relatable entails data humanism. Therefore, based on the existing evidence, I concur that data can indeed be humanized. Contrary to the general perception that data is impersonal, boring, and clinical, Lupi’s works adds life to data and disapproves this perspective (Lupi, 2017).
3 Artist’s Works
Lupi’s primary objective was always to transform the abstract data into real world experiences. She was determined to make the data more understandable by seeking to present a picture of what the information means, by describing specific aspects of data in terms that bring out a clearer picture of what the statements mean (Lange, 2019). For example, in the figure below, Lupi presents an alternative or opposing view of some of the terms used in data to make it more understandable to the ordinary person. The image below shows many other terms that Lupi used to render data less abstract.
Other terms common in data usage, such as descriptive, predictive, and conventions might be confusing to the reader. In this regard, she introduces the antonyms to make them more understandable.
3 Influence on Future Design and Practice
4 Modern Application of Data Humanization
Data visualization is the modern trend in interpretive plans, through which many artists, politicians, economists, philosophers, and several other professionals communicate to their target audience (Boehnert, 2016). Politics, the economy, the weather, and every other thing is currently expressed in terms of data (Boehnert, 2016). People have become acclimatized to regard the seriousness of an issue based on the data. For example, the seriousness of a pandemic, such as the COVID-19 disease is constantly expressed based on the number of people affected. For example, a visit to the World Health Organizaation website majorly shows the number of people affected in different countries around the world. The growth of an economy or its decline is expressed in terms of data. People’s minds and bodies have been trained to naturally adapt to the new reality of both physical and informational structures. Consequently, it is easier to make people understand the magnitude of a problem by visually representing its impact using data visualization techniques. Currently, the most effective way of reaching people and making them appreciate the magnitude of an event is by presenting facts to them. In the political world, politicians have realized that it is more practical to explain to the people how they will improve the economy by quoting the numbers. The politicians will also criticize their opponents by quoting numbers because of the understanding of the impact of such figures on the people.
5 Comparative Analysis
Lupi refutes the common perception that marketers have often held that the use of some pictograms and numbers have an innate power to simplify complexity (Lupi, 2017). This statement only goes further to show how complex the information that data tries to simplify is. Lupi might be wrong because the pictograms and graphs that she terms as simple is often as a result of extensive analysis that aims at simplifying the information presented (In & Lee, 2017). For example, quantitative analysis is a complex process that seeks to establish the magnitude of the relationship between variables using data. In most cases, such information is quite complex and multifaceted and the purpose of representing such details using graphs is to try and simplify the complex data.
In her criticism of data, Lupi asserts that while graphs and pie charts create an understanding of the complex systems in some cases, they only provide several volumes of unreadable 3D pie charts and some translucent user interfaces full of widgets (Lupi, 2017a). According to her, while the neutral visual language of these graphics suggest authority, they often mislead the people as they are more likely to be misread. In her data humanization efforts, Lupi and Posavec visualized their characteristics as carefully drawn flowers, asterisks, and whorls (“Giorgia Lupi and Stefanie Posavec's Life Data Visualizations”). Her perspective of this form of data representation is the ability to touch and feel the information, rather than simply ...