Achieving universal energy access, as outlined in SDG 7, requires a comprehensive understanding of the energy landscape. However, progress has been consistently hindered by inadequate, unreliable, and inconsistent data. This study examines the development of electricity-access measurement, from early binary indicators to contemporary multidimensional frameworks, and evaluates the ongoing data issues that obstruct global planning and investment. For decades, access was assessed using a simple connected/unconnected statistic. Although easy to track, this binary method overlooked important variables like dependability, quality, and cost. The World Bank’s Multi-Tier Framework (MTF), which redefines power access across a range of service qualities, marked a significant shift. However, improvements in measurement have not resolved fundamental issues. Major differences between key agencies, notably the World Bank and the International Energy Agency (IEA), prevent the creation of a single, authoritative global baseline. At the same time, technical faults, metering problems, inadequate maintenance, and data gaps at the ground level reduce the accuracy of national statistics. This article explores how emerging technologies can help overcome these limitations. Satellite imaging and machine learning enable cost-effective macro-level assessments, while smart meters provide high-frequency, real-time usage data. When combined with citizen science activities, these methods can create multi-layered, self-validating data platforms for policymaking. Evidence from initiatives like the World Bank’s Mission 300 and Ethiopia’s Program-for-Results shows that detailed, reliable data are essential for planning targeted, effective interventions. Finally, technological advances must be accompanied by stronger political and financial commitments to close the global electricity access gap.