For people living with type 1 diabetes (T1D), home glucose monitoring has evolved from occasional qualitative urine tests to frequently sampled continuous data providing hundreds of data points per day to inform optimal self-management. Continuous glucose monitoring technologies have a robust evidence base derived from randomized controlled trials (RCTs) over the last 20 years, and are now implemented in routine clinical practice, reflecting their clinical and cost effectiveness. However, while randomized studies are the gold standard, they can be slow to set-up, unrepresentative and do not provide data for efficacy in large, unselected populations. Real-world data can be responsive to rapid product cycles in technologies, provide a large, representative population, and have a lower regulatory burden. In this podcast we discuss the advantages and pitfalls of using real-world data to assess the efficacy of continuous glucose sensing technologies in people with T1D, with reference to examples of real-world data for real-time and intermittently scanned continuous glucose monitoring. Large datasets confirm the RCT data for real-time technologies and additionally provide data for work absenteeism and hospital admissions, as well as showing the impact of advanced technology features that can be difficult to assess in randomized studies. Real-world data for intermittently scanned monitoring also confirm the randomized controlled trial data, provide additional insights not shown in controlled study environments and highlight the importance of health equality. A mature real-world dataset for automated insulin delivery systems is now available and the future of glucose sensing is also discussed.