Balancing Data, Privacy, and Business Learnings

Balancing Data, Privacy, and Business Learnings


Just over a month ago, Walmart’s U.S. CEO, John Furner, told Bloomberg News that the company was seeing a slowdown in grocery purchases from customers that take GLP-1 agonist medications. These drugs known as semaglutide, which are sold under brand names like Ozempic and Wegovy, require a prescription from a doctor and are becoming increasingly popular for patients using them to help with weight loss. In fact, just type any one of these brand names into a social or search and what you will encounter is how they counter appetite and promote weight loss versus their primary use in combating diabetes. The implications of this new prescription frenzy are obvious: customers who have less of a craving for food are unlikely to purchase food at the volume and pace they did prior to being on the weight loss drugs. This decrease in consumption will affect the bottom line of the company.

The question that some customers might be asking themselves is, “How exactly did Walmart get their hands on that data?” Walmart, like Target, and other large one-stop-shop retailers, have pharmacy facilities within their physical stores, making it easy for today’s consumer to pick up their groceries, new shoes for “little Jimmy”, and their medication in a single stop – in turn providing big retailers volumes of valuable insights into their shopping behaviors, nutritional and medicinal needs.

According to the article, Walmart is “studying sales patterns using anonymized data on shopper populations.” Walmart has an enormous amount of data on customers and customer audiences. Presumably, they are working with anonymized data generated by the pharmacy, Walmart, or both. Walmart itself said that they are comparing shoppers who pick up a prescription for weight loss medications at their pharmacies to shoppers with similar profiles who are not filling those prescriptions at Walmart. Within those two groups, they are looking at pattern recognition for comparison purposes and have already realized that the first group is buying less food.

Walmart clearly goes to quite a bit of trouble to protect important and sensitive details about customers – and once anonymized for the purposes of analysis, their discovery and learning begins. While admittedly lacking a bit on the details, the Bloomberg article raises questions about the use of prescription data and what that might mean for patient privacy. There is more that retailers can and should do beyond just anonymization to protect patient data and prevent its re-identification, which if you are reading this post, you are likely familiar with.

For large retailers like Walmart, combined commercial operations leveraging data strategically and in a privacy-preserving way can produce powerful results and even more powerful actionable outcomes. Strategically, retailers must balance the data, privacy, and learnings in an effort to achieve business goals. At the same time, they must allow for making informed, data-driven decisions that comply with governance and regulations. Too often, data privacy and utility are presented as a tradeoff – yet this does not have to be the case. By leveraging innovative privacy-enhancing technologies (PETs), Walmart could run analytics in a privacy-preserving manner on data from their many divisions, including pharmacies, Sam’s Club, online e-commerce, international operations, and more.

In order to maintain customer trust and align with regulations, privacy-enhancing computation enables companies to protect patient and customer data during analysis with cryptographic guarantees, without having to move the data or rely on anonymization. PETs include Secure Multiparty Computation (MPC) and Fully Homomorphic Encryption (FHE). As a leader in privacy-enhancing computation, Inpher empowers organizations to collaborate on sensitive data seamlessly and securely across teams and borders. To learn more about Inpher’s award-winning platform, please contact us.