Author: Adaa Gupta
We rarely think of a click as a transaction. Yet every search, swipe, purchase, and online interaction leaves behind something that has become extraordinarily valuable: data.
There is growing truth in calling data the “new oil.” It has evolved from a byproduct of everyday life into one of the most valuable assets of the digital economy. Twenty years ago, much of its value remained locked within organisational databases and physical records. Today, data is collected, replicated, enriched, analysed, and monetized in milliseconds across interconnected digital systems. Users often pay for apps and services not with cash, but with their personal information and attention. Corporations then convert that attention into billions of dollars in advertising revenue.
In traditional currency transactions, people exchange cash for goods and services of roughly corresponding value. In the data-as-currency economy, however, the exchange is far less balanced. Individuals continuously generate information, yet receive little of the economic value created from it. Their data is captured, analysed, and often used to sell them more products through increasingly precise targeting. This imbalance raises an important question: if individuals create data through their everyday activities, who should benefit from the wealth that data generates?
Consider the world’s largest companies. A few decades ago, the most valuable businesses were largely built around physical products, infrastructure, and services. Today, data-driven platforms such as Google, Meta, Alibaba, and Tencent occupy an extraordinary position in the global economy. This reflects a fundamental shift in how markets perceive the value of information.
Unlike traditional currency, data is endlessly reusable. Cash leaves your wallet when you spend it. Data can be copied, combined, analysed, and sold repeatedly without being depleted. Its value can even increase as it is connected with other information.
Yet raw data is not inherently valuable. Its real worth emerges when it is contextualised, interpreted, and strategically used. Knowing that a consumer purchased a pair of shoes tells a business something. Understanding why they bought them, when they made the decision, and what that behaviour suggests about their future choices tells it much more.
Just as financial markets facilitate the movement of capital, data marketplaces facilitate the exchange of information. Businesses can list, purchase, sell, and exchange datasets, creating an economy around information itself. This market exists both within legal frameworks and, in darker corners of the internet, through illicit data trading.
Most people technically “agree” to data collection. Every click, like, search, and purchase generates another piece of information. Individually, these fragments appear insignificant; collectively, they can reveal remarkably detailed patterns of behaviour. Yet opting out is often inconvenient, privacy policies are exhausting to understand, and participation in the digital economy can feel almost unavoidable. Data extraction has become background noise, quietly helping organisations turn attention into profit.
Netflix provides a striking example. Traditional television networks often relied heavily on speculation and intuition when deciding what content audiences would watch. Netflix instead uses data from millions of viewers to understand preferences, predict demand, personalise recommendations, and influence what content gets produced. Data does not merely describe its customers; it helps shape the company’s decisions.
The same principle operates across the digital giants. Google, Amazon, Meta, and Apple continuously learn from the information generated through their ecosystems. Cross-platform behavioural targeting can connect our online actions with advertising profiles, allowing companies to predict what we may want before we consciously decide to buy it. A product we like on Instagram can contribute to a profile that influences the advertisements we encounter elsewhere. That is data’s power: it turns fragments of behaviour into predictions about future behaviour.
Artificial intelligence has pushed this transformation even further. Data is no longer simply a record of what happened; it has become the raw material for systems that predict what might happen next. AI can generate recommendations, identify patterns, and increasingly make or support decisions. Hospitals use medical data to identify early disease patterns, while retailers use predictive analytics to optimise supply chains and reduce waste. Across industries, data has become the fuel powering a new generation of economic decision-making.
We rarely see the value of what we give away because there is no receipt, no price tag, and no money visibly changing hands. Yet our data is being collected, analysed, and converted into economic value every second.
Money changes hands, but data leaves a trail. In an economy where attention can be measured, predicted, and sold, perhaps the most important question is no longer whether data has value. It is who gets to decide what that value belongs to.
