Abstract

Growing demand for statistics to support evidence-based policymaking and Sustainable Development Goal (SDG) monitoring has increased pressure on National Statistical Offices (NSOs) to produce data that are reliable, timely, and comparable across time and geographic units. Official statistics remain the primary source of nationally endorsed indicators for policy formulation, monitoring, and accountability. However, many NSOs operate in an increasingly complex data environment characterized by rising user expectations, expanding reporting requirements, and constraints in resources and data collection capacity. These challenges are particularly relevant in regional contexts, where comparability and consistency are essential for cross-country analysis and benchmarking.
This paper examines how quality assurance frameworks can be applied to strengthen the reliability and usability of official statistics while accommodating new data sources. Drawing on institutional practices from the Kenya National Bureau of Statistics (KNBS), the study assesses the operational use of internationally recognized quality standards, including the United Nations Fundamental Principles of Official Statistics and the International Monetary Fund’s Data Quality Assessment Framework. The analysis focuses on how these frameworks guide statistical production processes and support the systematic application of core quality dimensions such as accuracy, reliability, timeliness, coherence, accessibility, and comparability.
The paper adopts a process-based methodological approach, reviewing how quality assurance is embedded across stages of statistical production, from data collection and validation to dissemination and documentation. Particular attention is given to transparency mechanisms, including metadata standards and methodological documentation, which enable users to evaluate data limitations and apply statistics appropriately. These practices are essential in regional statistical systems, where harmonization of methods and clarity of documentation directly affect data comparability and analytical use.
In response to persistent data gaps following the adoption of the SDGs, the paper further examines the integration of Citizen-Generated Data (CGD) as a complementary source to traditional data such as censuses, surveys, and administrative records. CGD, produced by civil society organizations and community-based initiatives, has been applied in Kenya to generate localized indicators in areas where official data are infrequent or unavailable, including child education and early marriage. The study documents methodological steps used to incorporate CGD into the statistical system, including identification of priority data gaps, mapping of CGD producers, development of CGD-specific quality criteria, and implementation of validation and review procedures.
Results indicate that the application of clearly defined quality criteria and structured validation processes improves internal consistency, documentation, and fitness for use of CGD outputs. When aligned with existing quality assurance frameworks, CGD can supplement official statistics without compromising statistical standards. The analysis also highlights the importance of institutional learning and collaboration with non-state data producers in maintaining coherence within a mixed data environment.
The paper concludes that strengthening quality assurance and transparency remains central to sustaining trust in official statistics, particularly in regional contexts where comparability and harmonization are critical. Systematic evaluation of both traditional and alternative data sources enables NSOs to respond to expanding data demands while maintaining statistical consistency and reliability, supporting effective SDG monitoring and regional statistical cooperation.