Assuring Quality in Agricultural Statistics while Accelerating Innovation: FAO's Statistics and Data Quality Assurance Framework and Mechanism
Conference
10th International Conference on Agricultural Statistics
Format: CPS Paper - ICAS 2026
Keywords: #officialstatistics, data quality, quality assurance
Abstract
In today’s world, data and statistics are the backbone of decision-making, guiding the design of sound policies and targeted interventions, and enabling governments and the international community to act on evidence towards the achievement of development targets and goals. Building on FAO’s core mandate to collect, analyze, interpret and disseminate information on nutrition, food and agriculture, the Organization has modernized its statistical quality assurance framework and governance infrastructure to keep pace with a rapidly evolving data ecosystem. This paper presents the evolution and implementation mechanisms of the FAO Statistics and Data Quality Assurance Framework (SDQAF), designed to safeguard statistical integrity in the complex landscape of agricultural statistics. Building on the first Statistics Quality Assurance Framework of 2014, the SDQAF extends FAO’s quality principles to emerging data domains, addressing issues such as the use of non-traditional data sources, data confidentiality and the protection of intellectual property. The framework is structured around 16 core principles covering the enabling institutional environment, the implemented statistical processes and the produced statistical outputs, and is reinforced by robust governance and a dedicated Data Quality Unit. Through statistical standards, capacity development, quality assessments, user consultations and clearance procedures, the SDQAF operationalizes these quality principles across FAO’s decentralized statistical system, ensuring that innovation strengthens rather than undermines trust in official statistics on food and agriculture.