At Shopify, we recognize the positive impact data-informed decisions have on the growth of a business. But we also recognize that data exploration is gated to those without a data science or coding background. To make it easier for our merchants to inform their decisions with data, we built an accessible, commerce-focused querying language. We call it ShopifyQL. ShopifyQL enables Shopify Plus merchants to explore their data with powerful features like easy to learn syntax, one-step data visualization, built-in period comparisons, and commerce-specific date functions. I’ll discuss how ShopifyQL makes data exploration more accessible, then dive into the commerce-specific features we built into the language, and walk you through some query examples. Why We Built ShopifyQL As data scientists, engineers, and developers, we know that data is a key factor in business decisions across all industries. This is especially true for businesses that have achieved product market fit, where optimization decisions are more frequent. Now, commerce is a broad industry and the application of data is deeply personal to the context of an individual business, which is why we know it’s important that our merchants be able to explore their data in an accessible way. Standard dashboards offer a good solution for monitoring key metrics, while interactive reports with drill-down options allow deeper dives into understanding how those key metrics move. However, reports and dashboards help merchants understand what happened, but not why it happened. Often, merchants require custom data exploration to understand the why of a problem, or to investigate how different parts of the business were impacted by a set of decisions. For this, they turn to their data teams (if they have them) and the underlying data. Historically, our Shopify Plus merchants with data teams have employed a centralized approach in which data teams support multiple teams across the business. This strategy helps them maximize their data capability and consistently prioritizes data stakeholders in the business. Unfortunately, this leaves teams in constant
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