Big data is big business—designed to drive scale, find efficiencies, and unlock growth. But it was not built for individual use. Despite its reach, big data often misses the mark when delivering meaningful value to consumers.  

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Data collection without getting any real benefit is constant. We live with it but probably aren't happy about it. You search for something once—a recipe, a supplement, or a health condition—and suddenly, your media streams are flooded with ads. This data collection feels invasive, not insightful, and delivered without context, relevance, or care for your real needs.

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It’s a problem for personalized nutrition, too. There is no shortage of inputs: biomarkers, behavior, wearables, and purchasing habits. What’s missing is translating this information into products and programs that can help a person make better choices to address their needs. Part of the challenge is data structure—systems that facilitate using different data sources in meaningful ways that address individual needs, support everyday decisions, and deliver measurable value.  

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In our recent publication in Critical Reviews in Food Science and Nutrition, developed in collaboration with the Personalized Nutrition Initiative at the University of Illinois, we explore what it will take to shift from siloed data to integrated systems to capture data and deliver beneficial outcomes.  

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From Data Points to Personalized Experiences

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Whether it’s a biomarker assessment, a food tracker entry, or a grocery store purchase, every data point provides a piece of the individual’s story. When these inputs are designed only to be used by the company and live in disconnected systems, their value declines. Data must be integrated and interpreted with a purpose of supporting the whole person.

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We proposed a framework for integrating three core data domains essential to effective, scalable personalized nutrition:

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Together, these domains offer a more complete, dynamic view of the individual—not just what they need to eat, for example, but how, when, and where they can make changes that align with their daily lives. Data becomes truly valuable when these systems are designed to work together, adapt over time, respond to real-life circumstances, and help people act in the moment.  

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International Momentum Toward Adaptive, People-Centered Systems

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Our perspective aligns with a recent framework proposed by Linseisen et al. published in Advances in Nutrition called Adaptive Personalized Nutrition Advice Systems (APNAS). This model emphasizes “what should be achieved” and “how to bring about change” when it matters. They consider biomedical/health characteristics, static and dynamic behavior signatures, including goals, barriers, and preferences, and the food environment, including access and exposure.

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These frameworks highlight a shared shift across regions: PN must move beyond static data and toward real-time support that adapts.

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What Does This Mean for Innovation?

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It starts with thinking about how the data you collect can create value for your customers or consumers. Suppose your goal is not only collecting information on habits but also thinking about how the data you collect can help your consumers solve their problems. In that case, you can increase engagement and enhance customer lifetime value. To accomplish this, it is also essential to think about how the data you collect can be structured to work with other data sources your consumer is using or collecting. Supporting interoperability of different data sets provides more data points to deliver personalized advice. When aligned data creates value for the individual, it will create value for your business too.

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It’s time to ask:

• How can your data solve your consumer's personalized nutrition and health problems?  

• Are there opportunities to partner to provide more complete solutions?

• Is your data structured to work with other data sources to support decision-making?

• Are you transparent in how your data collected and will be used? Will it provide benefits to the user while maintaining privacy?

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Personalized nutrition means meeting people where they are. But to do that at scale, we must design systems around the problems we aim to solve, not just the data we can collect. When problem-solving drives system design, it becomes easier to identify the right data, structure it effectively, and deliver insights that matter. The key is systems that connect across data types, sectors, and use cases, creating value for all.

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