Data capitalism – personal data as currency?

Objective:

Learners analyze the concept of data capitalism, identify the economic value of personal data, and evaluate the key opportunities and risks of data trading.

Contents and methods:

The worksheet deals with data capitalism and the role of personal data as a “currency.” Using the example of the fictional character Nala, the types of data shared and their economic interest for companies are examined. The economic value of data, the role of data brokers, and the use of profiles by companies are discussed. A sorting exercise traces the flow of data from release to corporate use. Subsequently, argumentative texts highlight the perspectives of consumer representatives, business representatives, and small business owners on the opportunities and risks of data trading in order to summarize the most important arguments in a concluding table and write a reasoned statement.

Skills:

Analysis of data flow and economic interdependencies in data capitalism.
Assessment of the opportunities and risks of trading personal data from different perspectives.
Reasoned statement on a socially relevant economic topic.
Summary and structuring of complex factual texts.

Target group:

Grade 9 and above

52 other teachers use this template

Target group and level

Grade 9 and above

Subjects

EconomicsPoliticsEthics

Preview

Data capitalism – personal data as currency?

Introduction

“Data is the new oil”: EU politician Meglena Kuneva coined this phrase in 2009 to describe the importance of data for the global economy.

But is this comparison accurate? Oil was the fuel for industrial progress, as data may now be. However, while oil is consumed, data can be copied and used infinitely without diminishing. Companies such as Google, Meta (Facebook), and Amazon see our digital footprint—every click, every search, every location—as an inexhaustible raw material. They collect this data, refine it with artificial intelligence (AI), and use it to create enormous wealth.

The crucial question is: if data is the new oil, are you the oil supplier?

Today, economists talk about data capitalism. It is based on the idea that your personal information is not only a raw material, but also your currency. Every time you use a free service, you pay not with money, but with your data. But is this exchange fair? Who determines the price of your digital privacy? And what happens if this “raw material” falls into the wrong hands? You can find out more about this in this worksheet.

📝 Read the text and learn about Nala's day. Then complete the tasks.

Nala

Nala's day is meticulously planned with the aid of her smartwatch and digital apps. She begins her morning by reviewing her sleep analysis and logs her breakfast into a nutrition app. Feeling a bit tired, she notes it in her digital cycle calendar. On her commute, she searches for symptoms of iron deficiency and easily orders supplements online. Her evening jog is tracked by her fitness device, recording her heart rate and route, which she shares proudly on Instagram. Before bed, she engages in an online health forum, discussing her occasional headaches. It's impressive how technology simplifies tracking her health and wellness.

📌Let's take a closer look at who might be interested in Nala's data, how much it could be worth, and what exactly happens to her data. Read the text to find out.

The Economic Value of Nala's Health Data

Nala has willingly shared a range of personal health information through her daily activities, involving her smartwatch, nutrition app, online searches, and social media. This data encompasses her sleep patterns, dietary habits, fitness activities, and health inquiries, all forming a detailed digital health profile. The economic value of such data is derived from its potential to be monetized, offering significant insights into consumer behavior and health trends. Health data, particularly when aggregated, becomes a powerful tool for businesses seeking to tailor products and services to specific demographics. For instance, data from Nala’s activities might be valued only a few dollars individually. However, when combined with thousands of similar profiles, it creates a comprehensive dataset that can command hundreds of thousands of dollars in the market.

Corporations across various industries have a vested interest in acquiring health data due to its ability to drive personalized marketing and product development. Data brokers are key players in this ecosystem, specializing in collecting, refining, and selling packaged datasets to pharmaceutical companies, insurance firms, and consumer goods industries. Pharmaceutical companies utilize this data to enhance their marketing strategies, targeting drug promotions to specific consumer groups. Insurance firms use these insights to refine their actuarial models, determining risk and setting premiums more accurately. Consumer goods companies, particularly those in the health and wellness sectors, leverage this information to develop tailored products and services, increasing their market appeal.

The journey of Nala’s data from her smartphone to corporate entities involves complex data brokerage processes. Initially, the applications and devices she uses collect and store her data, often with her consent. These data points are then aggregated, anonymized, and sold to data brokers, who refine and package them into comprehensive datasets. Data brokers analyze and enhance these datasets, often using machine learning and artificial intelligence to extract deeper insights. Once refined, the datasets are sold to interested companies, enabling them to align their offerings more closely with consumer needs and trends. This data-driven approach not only boosts corporate efficiency but also fosters innovation in product and service development, providing a competitive edge in the market.

In essence, the economic value of Nala's health data lies in its ability to inform and transform business strategies, turning personal information into a lucrative asset in the digital economy.

📝 Put the sections in the correct order.

📌 Read the texts to learn more about the opportunities and risks of trading personal data.

Mr. Chen

As a policy advisor for a leading consumer rights organization, my primary concern is the escalating power imbalance in the digital health market. The concept of 'data capitalism' is not merely an economic theory; it is actively reshaping individual destinies by turning personal health into a tradable commodity. This creates severe information asymmetry, where corporations possess a far deeper understanding of an individual's future health risks than the individuals themselves. AI models can infer sensitive conditions from seemingly non-medical data, leading to algorithmic discrimination that is nearly impossible for a consumer to challenge. This systematic risk profiling erodes the principle of social solidarity, leading to a society where an individual's future is determined not by their actions, but by the predictive shadow cast by their data.

Ms. García

Speaking as the Head of Innovation at a major health-tech corporation, I see the flow of health data not as a threat, but as the most promising catalyst for medical and economic progress in our lifetime. Aggregated, anonymized datasets fuel innovation, allowing us to accelerate research and development for new therapies and diagnostic tools. By analyzing population-level data, we can identify health trends and develop predictive models for early disease detection. This personalization creates a significant competitive advantage while delivering tangible benefits to consumers. We invest heavily in robust anonymization and cybersecurity to ensure that economic advancement and individual privacy coexist.

Ms. Patel

As the founder of a health-tech startup, I witness firsthand how the current dynamics of data capitalism are not fostering a competitive market but are instead paving the way for data monopolies. Large corporations have amassed vast data silos, giving them an almost unassailable advantage. Access to proprietary datasets is the critical input for training effective AI models. This creates insurmountable barriers to entry for smaller innovators like us, who lack the capital and data volume to compete. This market concentration leads to welfare loss, where long-term innovation stagnates, consumer choice diminishes, and the economic benefits are captured by a select few, with the costs borne by consumers.

📝 Complete the following table by listing the key opportunities and risks associated with data trading. Additional task: Rank all of the arguments listed in both categories (opportunities and risks) according to their importance. Start with the most important argument in each category.

✅ Sample table

📝 Complete the following table by listing the key opportunities and risks associated with data trading. Additional task: Then rank all of the arguments listed in both categories (opportunities and risks) according to their importance. Start with the most important argument in each category.