Introduction
In the digital age, the Internet has become an integral part of our daily lives. From online shopping, social media interaction to work and study, various social media are embedded in every scene of life. However, behind the convenience and personalized experiences of digital platforms have arisen serious concerns about privacy violations and data misuse. With the rapid development of technology and the rise of a data-driven economy, the collection, use and sharing of personal information has become more widespread than ever. The digital traces we leave in digital platforms may become the source of data collection, and these related contents are used for data collection, analysis and utilization through the Internet.
In the era of big data, datafication has become a core concept, and issues surrounding privacy and personal data security have expanded massively. It is becoming more and more common for personal data to be collected, analyzed and shared, and various activities and phenomena in daily life are converted into quantifiable data forms for easy storage, analysis and processing. As Van Dijck (2014) mentioned in the paper, “Datafication is the transformation of social actions into online quantitative data, allowing real-time tracking and predictive analysis”. People’s real life and virtual life are transformed into data existence states by various technologies. Especially under the joint action of data and algorithms, datafication also means that people are controlled and shaped by data. The individual therefore becomes an “object” that is quantified, calculated and controlled.
What is Datafication?
Datafication can be understood as the process of quantifying and converting various aspects of human life and activities into digital data, such as communication, relationships, health and fitness, transportation and mobility, democratic participation, leisure and consumption (Kennedy, 2018). With the proliferation of digital technologies, we generate large amounts of data every day and transform our daily activities and interactions into data. Collecting, analyzing and utilizing this data to gain valuable insights and make informed decisions is the process of datafication.
Figure 1: Examples of datafication in Amazon’s Customer-Centric Ecosystem (Source: Arek Skuza research and insight)
Datafication is widely used in business, the process by which companies collect, analyze and utilize data to drive business decisions, optimize operations, improve customer experience and increase revenue. Amazon uses algorithms to analyze users’ shopping history, browsing history, and purchasing behavior to provide personalized product recommendations. By using a data-driven approach, Amazon is able to significantly improve users’ shopping experience and cross-sell opportunities while gaining insights into consumer behavior, market trends and operational efficiencies. The personalized services provided by datafication can not only effectively improve customer satisfaction, but also drive sales and revenue. At the same time, by effectively utilizing the vast amount of data it collects, Amazon has greatly enhanced the customer experience and has maintained its dominance in the e-commerce industry. Although datafication is reshaping industries, changing the way we live and work, and bringing unprecedented convenience and efficiency, it also raises concerns about privacy protection, information diversity, and individual rights.
How We Are Affected and Shaped by Datafication
Datafication simplifies and quantifies things and relationships by converting complex real-world phenomena into digital forms of data. In a certain sense, datafication makes the relationships in the world more diverse because it reveals some relationships that were not obvious in the past. For example, through the analysis of social media data, researchers can explore social dynamics, the formation and change of public opinion, and the evolution of cultural trends—all types of data that are difficult to obtain with traditional research methods (Arsenault, 2017). However, datafication also makes the relationships in the world single. It strips out the rich attributes of the original relationships and turns various relationships into relationships that can be expressed and matched by data. In the process of digitizing interpersonal relationships and social phenomena, nuances and individual specificities are often difficult to quantify. “Friend” relationships on social media may be defined solely by whether or not we follow each other, which fails to reflect the depth, complexity, and diversity of real-life friendships. Datafication also tends to simplify communication patterns. On digital platforms, communication is often reduced to clicking “like,” “comment,” or “share.” While these interactive forms provide a means of rapid feedback, they often fail to adequately convey complex emotions and deep ideas. This simplification can deprive communications of emotional depth and contextual richness, limiting people’s ability to express and understand complex emotions and ideas. In addition, quantitative indicators such as the number of followers on social media and participation in online activities have become the main means of evaluating individual performance and social relationships. The number of followers a person has on social media may be seen as a sign of influence and social status, ignoring the individual’s true contributions, talents and qualities. This trend has brought about a simplified understanding of individual value and contribution, and may also lead to the distortion of social values, making people excessively pursue recognition based on quantitative indicators rather than real achievements and contributions. Entire social systems are increasingly data-driven. People’s work processes can be precisely controlled by data. The evaluation of people’s work effectiveness can be assessed based on data indicators. People’s quality of life is also “accurately” evaluated by various data.
Individual Rights
Instagram is a social media platform based on pictures and videos that allows users to express themselves and connect with others through interactive forms such as likes, comments, and shares. The visibility and influence of each post on Instagram can be measured through quantitative metrics such as likes, comments, saves and shares. These indicators not only reflect the popularity of the content, but also directly affect the probability of the content appearing in other users’ information streams.
(Vreeswijk, 2022)
Today, every dimension can be datified, from people’s portraits, bodies, locations, behaviors, emotions and psychology, various relationships to social evaluations. A large part of this data comes from the content of self-expression or the traces left by people in their digital existence (Lycett, 2013). Digital platforms (such as the Instagram example mentioned above) provide new spaces for expression and communication. However, in the process of datafication, the effect of self-expression is also quantified by the rules set by each platform, such as the number of reads, likes, and retweets. This data measures the quality of self-expression and becomes a quantitative measure of content’s popularity and influence, which will increasingly translate into real-life benefits. Therefore, in the process of datafication, people are also working hard for data that can reflect value and influence. Data has become an important guide in the digital age, and users’ self-expression will only increasingly tend to pursue these quantified success criteria.
In addition, mobile phones, wearable devices, etc. map people’s real-life behaviors and status into data. Not only what people do on digital platforms will be datified, but what people say and do in real life may also be recorded and analyzed in the form of data. An important aspect of the datafication of people is the datafication of the body. However, the datafication of the body is often completed in a dismantled manner. Faces, expressions, body movements, voices, and fingerprints are all turned into “components” due to various different needs. Different service providers take what they need from these components. Human components also enter various data pipelines and are analyzed and processed, or recombined with other people’s components. With this componentization, individual autonomy and dignity are undermined, and individual rights may be ignored.
Personal Privacy
(Samarasinghe, 2021)
As wearable devices gradually give consumer electronics more meaning. It’s not just limited to fitness tracking, but also about preventive healthcare, continuous monitoring, and empowering patients to take control of their health through personalized data-driven decisions. The original intention of using wearable devices and sensor technology to collect personal data in different aspects of people’s daily lives is based on medical treatment, health management, sports and fitness and other goals (Kalinin, 2024). However, as the scope of people’s utilization continues to expand, self-quantified data has begun to become a new means of self-performance on social platforms. For example, the number of steps walked, running trajectory map, daily calories, etc., these all silently present the lifestyle, living status and pursuits of the owner behind the data. With the advent of various datafication methods, some concepts that originally seemed abstract have become concrete and precise, becoming a continuous datafication process. For example, people’s emotions can be recorded into continuous waveforms through EEG recordings. The shift of people’s gaze can be converted into heat maps by eye trackers, and the inner secrets people want to hide can be easily explored by data. In addition, platforms usually obtain the right to collect data in the form of terms of use. Once personal data enters each platform, it is no longer under the control of the individual. Users also have very limited understanding of the specific uses of data, resulting in users losing their autonomy over personal information. Both the storage period of data and the application method of data can only be controlled by the platform. People’s privacy is also legitimately “used” in the name of data (Quach et al., 2022). Sometimes, if they cannot complete the datafication process required by network platforms or social systems, people may not even be able to obtain the corresponding qualifications. Datafication has become a wall, isolating some people from corresponding services or social resources.
Interpersonal Relationships
Datafication also quantifies the relationship between people. This phenomenon is particularly evident on social media platforms, where every aspect of personal interactions, social networks, and relationships is transformed into measurable and analyzable data. Digital interaction provides people with a quick and intuitive way to understand and evaluate interpersonal relationships, but it also simplifies complex interpersonal interactions, which may lead to misunderstandings about the depth and authenticity of relationships. The number of likes and comments on social platforms quantifies and makes the degree of relationship public. From the perspective of the feedback giver, likes can quickly achieve the purpose of showing goodwill without putting in too much effort, so it has become a convenient means of maintaining relationships. The number of likes, comments, shares, and follows on social media has become a measure of interpersonal intimacy and social influence. While social media platforms provide a convenient means of staying connected, relationships maintained through digital interactions may lack the depth and authenticity of face-to-face interactions. Users rely too much on digital interactions and may overlook the importance of in-depth communication and face-to-face gatherings. Therefore, under the guidance of data, the way of establishing and maintaining interpersonal relationships has gradually become simplistic, superficial, and utilitarian.
Conclusion
As a distinctive feature of modern society, datafication provides social systems with unprecedented control and optimization capabilities, promoting the optimal allocation of resources and the personalization of services. However, datafication also brings new challenges, such as the infringement of personal privacy, the erosion of personal rights, and the neglect of non-quantitative value. Although it is necessary to seek quantitative methods under the trend of datafication, it is also necessary to develop and maintain a more comprehensive and pluralistic system to ensure a comprehensive and unbiased understanding of individuals and social relationships. This includes valuing and protecting personal privacy, respecting the non-quantitative value of individuals, and promoting public awareness and critical thinking skills about the use of data.
References
Arsenault, A. H. (2017). The datafication of media: Big data and the media industries. International Journal of Media & Cultural Politics, 13(1-2), 7-24.
Kalinin, K. (2024). Wearable Technology in Healthcare: The Future of Medical Devices. Topflight. Available from: https://topflightapps.com/ideas/wearable-technology-in-healthcare/
Kennedy, H. (2018). Living with Data: Aligning Data Studies and Data Activism Through a Focus on Everyday Experiences of Datafication. Krisis : Journal for Contemporary Philosophy, 2018 (1). pp. 18-30. ISSN 0168-275X
Lycett, M. (2013). ‘Datafication’: making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), 381–386. https://doi.org/10.1057/ejis.2013.10
Quach, S., Thaichon, P., Martin, K.D. et al. (2022). Digital technologies: tensions in privacy and data. J. of the Acad. Mark. Sci. 50, 1299–1323. https://doi.org/10.1007/s11747-022-00845-y
Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & society, 12(2), 197-208.
Images
Arek Skuza Research. (2023). The Role of Machine Learning and AI in Amazon’s Data Monetization Approach [Image]. https://arekskuza.com/the-innovation-blog/the-role-of-ml-and-ai-in-amazons-data-monetization-approach.
Kalinin, K. (2024). Wearable Technology in Healthcare: The Future of Medical Devices. [Image]. Topflight. https://topflightapps.com/ideas/wearable-technology-in-healthcare.
Samarasinghe, Y. (2021). Health and Wearables. [Image]. Champ Blog. https://champsoftblog.com/?p=2978.
Vreeswijk, S. (2022). How to Get an Instagram App for Desktop (Mac or PC). [Image]. Shift. https://shift.com/blog/apps-hub/how-to-get-an-instagram-app-for-desktop.
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