Data-driven circularity is the central intelligence of the circular economy, integrating and leveraging data across customer interactions, business ecosystems, connected products, and internal IT systems to guide strategic decisions and enhance resource efficiency. Incorporating technologies such as big data, business intelligence (BI), artificial intelligence (AI), machine learning (ML), predictive analytics, and generative AI creates a robust framework that enhances operational efficiencies, drives sustainability, and fosters economic growth. Big data tracks material flows, optimizing resource use and minimizing waste. BI transforms raw data into actionable insights, improving operations and recycling programs. AI and ML optimize resource allocation, improve product design, and facilitate predictive maintenance and demand forecasting. Predictive analytics anticipates trends, improving product design for repairability and recyclability. Generative AI aids in innovative product designs and simulations. Digital Product Passports enhance transparency and lifecycle management. Data-driven circularity helps comply with regulations like right to repair and Extended Producer Responsibility (EPR), supports strategic decision-making in redistribution, and extends product lifecycles. The chapter also outlines essential digital capabilities and provides a detailed capability map, emphasizing the integration of these technologies across various stages of the product lifecycle. Additionally, a comprehensive checklist guides manufacturers through implementing data-driven circular practices, ensuring structured adoption and effective outcomes. As part of the Sustainable Manufacturing Intelligence Framework (SMIF), data-driven circularity offers a comprehensive view of a manufacturer’s circular business models, guiding strategic decisions and operational adjustments to drive profitability and resource efficiency.