You’ve probably seen the “Data Science Hierarchy of Needs” (pictured below) before. Inspired by Maslow, it shows the tooling you would use at different levels in data science—from logging and user-generated content at the bottom, to AI and deep learning at the very top. While hierarchies like this one can serve as helpful guides, at Shopify, we don’t think it always captures the whole picture. For one, it emphasizes particular tools over finding the best solution to a given problem. Plus, it can have a tendency of prioritizing more “advanced” solutions, when a simple one would do. The Data Science Hierarchy of Needs That’s why we’ve chosen to take a different approach. We’ve created our own Data Science Hierarchy of Needs to reflect the various ways we as a data team create impact, not only for Shopify, but also for our merchants and their customers. In our version, each level of the hierarchy represents a different way we deliver value—not better or worse, just different. Our philosophy is much more tool-agnostic, and it emphasizes trying simple solutions before jumping to more advanced ones. This enables us to make an impact faster, then iterate with more complex solutions, if necessary. We see the pinnacle of data science not as machine learning or AI, but in the impact that we’re able to have, no matter the technology we use. Above all, we focus on helping Shopify and our merchants make great decisions, no matter how we get there. Below, we’ll walk you through our Data Science Hierarchy of Needs and show you how our tool-agnostic philosophy was the key to navigating the unprecedented COVID-19 pandemic for Shopify and our merchants. Tackling The Pandemic With Data During the pandemic, we depended on our data to give us a clear lens into what was happening, how our merchants were coping, and what we could do to support them. Our COVID-19 impact analysis—a project we launched to understand the impact of the
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