An Excerpt from Mindmasters

These days, it feels as though our lives are ruled by algorithms, and our personal data is available for anyone to see and manipulate. 

Growing up in a small village in Germany, computational social scientist Dr. Sandra Matz experienced an offline version of this visibility, feeling that very few aspects of her life could be private when everyone knew everyone. She relates this experience to her work in computational psychology in her new book, Mindmasters: The Data-Driven Science of Predicting and Changing Human Behavior. Matz shines a light on how our digital footprints have been used positively and negatively and how we can gain more control and power over our information.

In this excerpt from Chapter 10, Matz introduces the idea of data co-ops, a model through which larger groups of people can pool and manage their data responsibly and for the benefit—rather than exploitation—of its members.

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Whenever I’m back home, I go for a walk in the vineyards—no matter the weather. One time, I brought a friend visiting from Japan. We walked through my family’s vineyards, and I told him about my childhood adventures. He wanted to try our wine. I can imagine you might too.

I had to giggle a little. Our vineyards are far too small to produce our own wine. We neither had the equipment nor the expertise to do so. But, looking back, his question was a good one. He wanted to know, “What happens to the grapes?”

Selling the grapes to one of the big wineries in the area could have been a possibility. But we never harvested enough for this to be a real option. Even if we did, any deal would have greatly favored the winery. So instead of simply letting the grapes go to waste, most families in Vögisheim and the surrounding villages were part of a Winzergenossenschaften. Yes, a true beauty of a German word. The English translation is winemakers’ co-op.

After harvesting the grapes, we dropped them off at the co-op. The co-op would either turn them into wine or sell them to the wineries. Working with these co-ops had several advantages. First, combining the grapes increased the value of each individual harvest. Second, the co-op brought expertise, both because many of the members were winemakers and because we could pool our resources. The proceeds from the wine and grape sales allowed the co-op to buy advanced equipment, hire expert oenologists to improve the quality of the wine, and bring on marketing professionals to sell it. More than any one of us could have pulled off alone. Pooling our grapes made all of us better off.

The same is true of your personal data. Your individual data isn’t worth very much. It only becomes valuable when combined with the data of others. Think of medical research. Your medical record alone won’t tell us anything about the risk factors associated with a certain disease. We can only start exploring these factors once we have a sufficiently large pool of carriers (and non-carriers).

The same is true for your Facebook and Google data. The two companies only care about your data because they can connect and compare it to the data of millions of people. That’s what allows them to extract the insights third parties are willing to pay for.

But it’s not just the value of your data that increases when you pool it with others. Just as my family didn’t have the expertise to turn our grapes into wine, most of us don’t have the expertise to make good decisions regarding our data (see chapter 8). Left to our own devices, we simply don’t stand a chance. We have neither the expertise nor the time. Could my parents have figured out how to make wine? Probably, even though it might not have been very good. But were they eager to dedicate their whole lives to this? Hell no.

Just as the people in my village came together to reap the fruits of their labor, we need to come together in small communities of like-minded people to collectively manage our data and benefit from it. Like wine co-ops, data co-ops are member-owned organizations that pool and manage their members’ personal data to benefit the collective. However, unlike wine co-ops, data co-ops don’t require people to be in the same place—although they could. Instead, the members can be connected by a common goal and a shared strategy for leveraging their data to accomplish that goal.

Digital Data Villages

Let me give you an example of how a data co-op could work.

As I started writing this book, I got pregnant. A beautiful but also terrifying experience. You get advice from all directions. Do this. Do that. Most of the advice will at some point contradict prior advice you’ve gotten. Eating sushi might put the baby at risk. No, that’s not true. What you must look out for is caffeine. You have access to doctor check-ins every two to four weeks. But what you really want is a minute-by-minute update on how things are going, and the assurance that everything is fine. All this uncertainty drove me nuts.

Now, imagine expectant mothers from around the world sharing their genetic and biometric data, alongside information about their own health and the health of the baby. You could stop the guessing game, and instead base your decisions on actual data. To start with, you could build advanced predictive models to identify general risk factors. Some of them might be known already, but some of them might be new.

Not just that, the members of the co-op could receive personalized, dynamic predictions of their own risk factors and current pregnancy status. Or customized advice on how to cope with morning sickness (a very misleading branding for all-day misery) or the constant fatigue.

By tapping into different data sources, the model could form a holistic impression of the mother’s circumstances. Who is she (e.g., age, ethnicity, historical health records, levels of physical activity)? What’s her social context like (e.g., is she a single mom? Does she have a lot of support from other family members)? And what’s the potential impact of her environment (e.g., does she live in an urban area with high levels of air pollution)? Combining all these factors, is there anything our expectant mother should be worried about? And if so, what should she do? I would have signed up for this data co-op in a heartbeat.

You could think of many, many more examples. I’ve only listed a few here:

• Patients with rare diseases sharing their genetic information, medical history, and biometric data to improve our understanding of the disease and develop treatment options
• Professional or semiprofessional athletes trying to optimize their performance based on biometric feedback
• Women from underrepresented minorities pooling their genetic data to better understand the effectiveness of drugs that have been predominantly tested on white men
• Teachers pooling their classroom data and student performance to identify winning strategies for classroom engagement

What is common to all these examples is that the individuals involved voluntarily share a selection of their personal data with the co-op to help the entire co-op benefit from the group’s insights. Having access to my own genetic data is useless if I am trying to figure out how to improve my pregnancy experience or the health of my future child. But it could be extremely valuable when pooled with the genetic data of other expectant mothers.

Data co-ops turn the existing data model upside down. Instead of a few companies controlling and profiting from your data, you decide who to share your data with and you benefit from doing so. This works because data co-ops (and data trusts) are owned by their members and bear fiduciary responsibilities. They are legally obligated to act in the best interests of their members. And because co-ops are effectively governed by their members, anyone who joins the crew gains partial control over how the co-op is run. The system runs on collective rights and accountability, as opposed to exploitation and obfuscation.

This shift in the ownership and incentive model makes co-ops ideal champions for the privacy-preserving technologies I introduced in the previous chapter. Federated learning wasn’t developed specifically for data co-ops, but these organizations could be among its early adopters because they have a strong incentive to use such technology, unlike Facebook, which profits from accessing as much user data as possible. That’s its business model. Data co-ops are the exact opposite. They act on behalf of their members and are measured by how successful they are in amplifying the benefits and mitigating the risks. The specific goals of any given data co-op can vary. For example, some co-ops might focus on helping individuals monetize their data. Much like our wine co-op enhanced the returns on our grapes, data co-ops boost your bargaining power. With 20 million allies, the big players will suddenly have to take you seriously.

Reprinted by permission of Harvard Business Review Press. Adapted from MINDMASTERS: The Data Driven Science of Predicting and Changing Human Behavior by Sandra Matz. Copyright 2025 Sandra Matz. All rights reserved.