One advantage of living in the age of big data is a diminishing need to ask customers explicitly for feedback. A variety of methods for unobtrusive learning from customers have emerged thanks to digitalization (vs. digitization). For example, customers now write reviews every day about products and services without being asked to do so. The behavior of customers can be captured by tracking their website visits. Sensors are now so cheap that a retailer can put sensors all over the floor in its stores and track the physical movements of customers. Compared to the tools and technologies used in an Amazon Go store, such a data collection initiative can be considered a small step today. Motion sensors for store shelves, neural network-powered cameras, and wireless beacons can easily be added as complements. From a managerial perspective, the phenomenon is more than a shift from a push mindset to a pull mindset. Leveraging it fully requires careful planning and execution. This is probably why “data centric” companies are capturing more value from unobtrusive methods while most retailers still struggle to learn from the reviews on their own product pages. Capturing most of the value also requires a systematic effort, rather than ad hoc attempts, ideally starting from product development into the full product life cycle. For example, when launched in 2004, Yelp required asking friends for recommendations. Users could not write reviews without being explicitly asked for. Yelp switched to the current model four months after the launch, based on the data on how early users behaved at the site. This is a short intro to a series of posts on “data centricity,” a concept I have been developing. Comments and feedback are welcome in any form.