People are much more than data, says award-winning SFU researcher

In a world mediated by digital technology, how does a community collectively make sense of, and reclaim agency over, the data being extracted from their daily lives?  
 
Digital platforms like social media, fitness trackers and online retailers have become part of everyday life. These technologies collect vast amounts of personal data that can be used to target advertising, shape prices and influence public opinion. As community members, consumers and citizens, people often have little choice about how their data is collected.

This process, known as datafication, turns everyday actions from shopping or exercising to seeking healthcare into data that can be collected, analyzed and used by governments and corporations.

Simon Fraser University (SFU) School of Communication professor Katherine Reilly researches and teaches about the role of data and information systems in development and social change. She studies the policies that govern how data is collected and used, and promotes data regulation that puts people’s needs and well-being ahead of corporate interests. 

Her recent book, Communing Data Literacy: Tools and Concepts for Social Engagement, examines how five communities across South America experience the collection and use of their personal data. It was co-authored with Esteban Morales, University of Groningen, and María Julia Morales González, University of the Republic, Uruguay. The publication received the 2026 Gertrude J. Robinson Book Prize from the Canadian Communication Association, which honors the best book published in Canadian communication studies. 

In the book, the authors explore a range of everyday situations where people encounter data collection. Case studies included older adults in Uruguay navigating the healthcare system, new immigrants in Chile accessing financial services, women in Peru seeking contraceptives, young people in Paraguay using pharmacy loyalty cards, and delivery drivers in Colombia navigating the Rappi platform, an app that offers food and retail delivery.

Together, the five cases demonstrate how datafication reorganizes people’s daily lives in ways that are deliberately hard to see. People experience datafication through seemingly small, personal decisions. But those decisions only become meaningful, and open to collective action, when viewed as part of a shared experience.

The authors propose that data literacy is not a skill individuals acquire, but something people build together through dialogue and shared experience as groundwork for a collective response to datafication.

Communing Data Literacy is available online at Queens-McGill Open Access book collection. 

We spoke to Professor Reilly about her book.

Your book examines five very different communities across South America. Did anything surprise you about how people used digital technology?

I was surprised by how keenly people want digitally enabled systems that improve their lives, and that they want to share their personal data if it means accessing essential services or creating systems that are better for everyone. They still have concerns about digital information systems, and want to be sure that these systems are secure and that participation doesn’t expose them to harm. But overall, participants saw datafication as a potential source of personal or community gains.

For example, our Chilean partners worked with recent migrants who had difficulty accessing virtual banking. Without a bank account, they had difficulty with daily activities like getting paid or renting an apartment. But they also worried about secure management of their personal data, because while digital banking helped them access basic needs, a data breach could result in them losing their savings. 

We were surprised by these findings because in the wake of the 2018 Cambridge Analytica scandal there was so much discussion about the risks of sharing personal data. But in everyday life, people tend to experience information systems not as datafication, but as daily routines, processes or services. When people become part of information systems, they are not thinking about the larger privacy concerns, or how the organization of these systems might reinforce or shift aspects of their social relations. They are just thinking about how to make their lives easier. 

Across the five case studies, what did you learn about how datafication shapes people’s everyday lives?

In this book, we shine a light on how digital systems organize us in relation to each other. These systems are designed to be pervasive, seamless, and integrated into our everyday activities in ways that cause as little friction as possible. They do offer us immediate hits of tangible value. However, it is often very difficult to understand the value propositions at work in these systems.

Also, people are part of many different databases, platforms and information systems. We participate in them as individuals, but they organize us into cohorts, each of which is designed to construct a particular flow of value that accumulates to the owner of the system in question. 

These systems can therefore have the effect of separating people from each other, as in the case of loyalty cards and pricing schemes. These systems are designed for individual use, and it is difficult to know who else is a member. This makes it difficult to join with others to demand changes in the value propositions of the program. 

You argue that personal data literacy is not simply an individual skill. What does it mean to approach data literacy as a collective practice?

Personal data literacy typically focuses on individual management of personal data. But from our point of view, it is better to learn about datafication through collective practices. This is because individual personal data only gains value when it becomes part of systems that organize daily practices into a large volume of flows. To become data literate, we need to understand the systems of datafied relationships that we take part in. 

I call this ‘communing’ data literacy, drawing on the Latin American concept of comunalidad.  Comunalidad means “working together in ways that produce a ‘we’”, and it highlights the importance of social processes that generate collective understanding. In total, I believe that literacy is something we produce through dialogue, and that it resides in social networks as a form of collective knowing.  

I think this approach is especially important for datafication. To the extent that datafication reshapes our social relations around the centralizing power of information systems, communing data literacy can perform a counterbalance by sparking conversations and human connections.  

What are your recommendations for communities that want to better understand and respond to datafication?

Communing Data Literacy contains a series of helpful guides for community or classroom work.  The book is available as an open access volume, and the guides contain all the information needed to organize a conversation or run a workshop.  

Since we finished the book, I have been thinking a lot about personal values. Here is a powerful activity for reflection on technology use. It works well for individuals, and can be an excellent jumping off point for dialogue within families, in classrooms, or in social networks:

• Track your digital activities for a day or two. 
• Identify your top 5-10 personal values. Online tools are available.
• Ask yourself: Do my digital activities align with my personal values? 
• Think about one specific thing that you might do differently to better align your personal values with your digital practices.
• Share findings amongst friends, family or community. Talk about challenges and how you might support each other.  

When you meet with communities and teach university students about data literacy, what is the most important thing you would like them to know about their data?

I always want people to recognize that, while datafication and digital systems organize us into information flows, we are not our data, or the value that can be extracted from it. Ultimately, data is just a trace or an inference—a construct designed to facilitate coordination and extraction. But people are so much more than their personal data, and we are certainly more than the value our data produces for others.

For more visit Professor Reilly’s faculty web page and Research Expertise Engine profile. 

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