When It Comes to the Collection of Personal Data, Less Is More

For decades, the tech industry has operated under a simple mantra: collect as much data about users and customers as possible. This approach seems reasonable: Data collection is cheap, access is easy, and the potential value for advanced insights and personalization – and ultimately higher profits – is well-established. But this traditional view is increasingly outdated—and potentially harmful.

In today’s digital ecosystem, the indiscriminate hoarding of personal data has become a liability, not an asset. Overcollection leads to data overload, making it harder to separate valuable signals from irrelevant noise. It also creates unnecessary risks, from regulatory scrutiny to reputational damage caused by data breaches. More importantly, technological advancements like federated learning now offer alternatives that can deliver personalization without centralizing sensitive information. The result? A win-win for both consumers and businesses.

More Data Isn’t Always Better

The prevailing belief among business professionals has long been that more data is inherently better, offering deeper and more holistic insights about consumers as well as greater accuracy and precision when it comes to predicting their future actions. Although this assumption has intuitive appeal, it isn’t always true. Too much data can, in fact, dilute actionable insights by flooding systems with low-value or irrelevant information, reducing both the efficiency and accuracy of predictive models. This problem gets compounded when companies lack a clear strategy for processing and leveraging the massive amounts of information they gather.

In addition, adopting a “less is more” approach to data collection can benefit businesses more directly. By collecting only what is essential, companies can streamline operations, reduce regulatory headaches, and build trust with their customers. Apple’s marketing campaigns around privacy—including its memorable tagline, “What happens on your iPhone stays on your iPhone”— for example, have solidified its reputation as a leader in user trust, differentiating the brand in a way that resonates with modern consumers.

More Data Means More Risk

Collecting large amounts of user data comes with the responsibility to protect them. With cyberattacks and data breaches on the rise worldwide, hoarding personal data poses both financial and reputational risks. High-profile incidents such as the 2017 Equifax hack and more recent breaches at companies like T-Mobile demonstrate that the stakes are high. According to IBM’s 2024 Cost of a Data Breach Report, the average cost of a breach has risen to $4.88 million – a 10% increase from 2023 and the highest in history. For businesses, the message is clear: the more data you collect, the bigger your target for cybercriminals.

Importantly, the costs of data breaches go beyond immediate settlements and fines. As research shows companies suffer long-term reputational damage after breaches, with many consumers choosing to take their business elsewhere. This risk is amplified in sectors such as healthcare and finance, where the stakes of mishandling data are particularly high. In addition, the operational burden of preventing – and in the worst case responding to – breaches can drain significant resources and distract from core business activities.

Leverage Data Without Collecting It

Traditionally, businesses were forced to trade off the security and reputational risks of collecting user data against its tangible benefits for personalization. But new technology can eliminate this tradeoff. 

Instead of pooling data on centralized servers, machine learning approaches like federated learning train algorithms directly on devices, ensuring that sensitive information never leaves users’ hands. Apple’s Siri, for example, or Google’s predictive text on Android devices leverage the computing power of your smartphone to train their models locally. 

Technologies like federated learning enable businesses to leverage insights about consumer preferences without having to collect – and subsequently protect – personal data. Although the transition to techniques like federated learning won’t happen overnight, their adoption is already expanding rapidly. In addition to companies like Google making much of their foundational research accessible through academic papers and open-source frameworks (e.g., TensorFlow Federated), there is a growing industry of consulting companies supporting the integration for SMBs that might lack access to internal expertise.

The Road Ahead

As regulatory landscapes evolve to impose heftier and heftier penalties on the mishandling of personal information, and consumers become increasingly concerned about their personal data being exploited, the incentives for minimizing data hoarding will only grow.

Technological advances such as federated learning offer a clear path forward. By embracing these innovations, companies can provide the same—if not better—levels of service while safeguarding user privacy. This shift requires a fundamental redesign of data strategies, but the benefits are undeniable: happier users and customers, reduced security and reputational risks, and a competitive edge.

The era of data hoarding is over. Companies that recognize the value of “less is more” will not only navigate the challenges of a privacy-conscious world but thrive in it.

Dr. Sandra Matz is a computational social scientist, and a professor at Columbia Business School, where she also serves as the Director of the Center for Advanced Technology and Human Performance. She is the author of Mindmasters: The Data-Driven Science of Predicting and Changing Human Behavior.