Session 20
Shaping the Statistical Future
4 June 2026
11:00 – 12:30
ŠIBENIK VI
Presentation title
From Principles to Practice: How the Revised Charter Shapes Switzerland’s Statistical Future
The Charter of Swiss Official Statistics, originally adopted in 2002 and amended in 2008 and 2012, has now undergone a comprehensive revision.
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While it was already broadly aligned with the Code of Practice for European Statistics (CoP) in substance, the revised Charter now also follows the CoP’s structure. This ensures that all statistical offices at the federal, cantonal, and local levels can use the Charter as a common reference framework—particularly those federal offices recognized as Other National Authorities, which must comply with the CoP under the Switzerland–EU statistics agreement and are subject to peer reviews.
Switzerland’s federal statistical system is highly diverse: around 80 statistical offices—half at the fed-eral level and half at the regional level—vary significantly in size, institutional setting, and scope of activities. The revised Charter explicitly takes this diversity into account. It thereby lays the foundation for a shared quality framework across all official statistics in Switzerland. In addition, all statistical offices will now benefit from a regular good-practice exchange on issues of professional ethics through the EvalCharta project.
The presentation will highlight experiences already gained through the EvalCharta project and discuss the Charter principles that help statistical services position themselves as centers of competence in data stewardship and data science.
These efforts are supported by an independent body, the Swiss Ethics Council for Official Statistics, which advises on ethical matters, promotes the Charter, and monitors adherence to its principles.
Markus Baumann
Federal Statistical Office FSO
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As deputy head of the staff division and head of the quality and legal section at the Federal Statistical Office (FSO) of Switzerland, Markus Baumann leads and supports a wide range of topics in official statistics. Among other roles, he also serves as the national coordinator for the peer reviews. A social scientist by training, he has been working at the FSO for more than 20 years in various functions.
Presentation title
Untangling the bottleneck: The NSO’s bold shift to smarter, decentralised statistical production
The National Statistics Office (NSO) of Malta has launched a multi annual modernisation programme designed to strengthen efficiency, enhance productivity, and elevate the quality and coherence of its statistical outputs.
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At the heart of this transformation is the development of a comprehensive quality management function and the establishment of a resilient, modern IT architecture that supports both technical robustness and business continuity. Central to this effort is the Statistical Production Service Architecture (SPSA), a unifying framework that integrates data and metadata standards inspired by the Generic Statistical Business Process Model (GSBPM). By addressing methodological and IT dimensions jointly rather than in isolation, the SPSA aims to streamline onboarding, promote the reuse of approved statistical tools and methods, and accelerate the adoption of modern, harmonised production practices across the organisation.
The initiative also introduces a new generation of statistical infrastructure, including tailored dashboards and a consolidated statistical database that brings together outputs in a coherent, accessible environment. This ecosystem provides the foundation for systematic process quality assessment, continuous improvement, and evidence based decision making. Beyond the technical redesign, the strategy represents a deliberate organisational shift: moving away from centralised bottlenecks toward a controlled, decentralised governance model. Under this approach, production directorates assume greater responsibility for execution while operating within a shared set of standards that safeguard methodological rigour and coherence. The result is a universal application of quality management - a model that empowers production teams, strengthens accountability, and ensures that innovation and quality advance hand in hand. The framework also reinforces staff empowerment by ensuring that human expertise is fully leveraged through continuous collaboration across functional units, fostering a culture where shared standards and collective capability drive excellence.
Together, these reforms position the NSO to meet evolving data demands with agility, reliability, and transparency, laying the groundwork for a modern statistical system that is both resilient and responsive to Malta’s national and European obligations.
Silvan Zammit
National Statistics Office (NSO), Malta
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Silvan Zammit has served the National Statistics Office of Malta for over 23 years and is currently Director for Data Resources, IT, and Methodology. His portfolio covers data collection, methodological design, the Census of Population and Housing, information systems, and IT security. A graduate in Mathematics, Statistics, and Operations Research from the University of Malta, he later pursued postgraduate studies in Actuarial Techniques. His experience spans census operations, survey methodology, and statistical infrastructure. Internationally, he has supported countries across Africa, Asia, and Eastern Europe on population and housing censuses, sampling strategies, and demographic work. For more than 13 years, he has also been a Senior Visiting Lecturer at the University of Malta, teaching Official and Business Statistics across multiple faculties. His global engagements, training delivery, and participation in technical forums reflect a strong commitment to advancing statistical literacy and connecting methodological rigour with practical application.
Presentation title
Customizing International Principles for Abu Dhabi Statistical Business Model: Code of Practice 2025
The Abu Dhabi Statistical Code of Practice 2025 provides a substantive case study of how international statistical quality principles can be operationalized within a specific legal and institutional environment, while responding to rapid technological change.
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Issued by Statistics Centre Abu Dhabi (SCAD) under Law No. 5 of 2021, the Code establishes a single, binding reference framework for the entire Abu Dhabi statistical ecosystem, covering data providers, producers of official statistics, users, and government leadership. Version 4.0 represents a major revision driven by clear systemic pressures: the growing use of artificial intelligence and automation in statistical processes, the integration of administrative and multi-source data, heightened expectations around transparency and privacy, and the need for stronger ecosystem-wide coordination and assurance.
The Code is explicitly aligned with the UN Fundamental Principles of Official Statistics, the Eurostat Code of Practice, and UK practices. Core quality dimensions such as professional independence, methodological soundness, accuracy, relevance, and more are fully retained. The contribution of the Abu Dhabi Code lies not in redefining these principles, but in converting them into enforceable methodological, governance and assurance expectations tailored to a decentralized, technology-enabled statistical system. In particular, the Code strengthens requirements for controlled data integration, traceable and auditable AI-enabled processing, transparent revision and error-handling practices, and systematic quality assurance across the full value chain.
What makes the 2025 Code distinctive is its ecosystem-level design and legal enforceability. It explicitly defines accountability for Other Producers of Official Statistics operating under SCAD’s supervisory mandate and embeds quality governance, documentation, and reporting obligations as part of normal statistical practice. It also addresses innovation governance directly, requiring that the adoption of AI and new technologies remains methodologically transparent, quality-controlled, and subject to human accountability.
A strong example is the principle of real-world representation. Beyond asserting accuracy, the Code requires producers to quantify and disclose coverage, sampling, and non-sampling errors, alongside biases introduced through data integration and processing. This shifts methodological quality from an internal technical exercise to an explicitly reportable outcome, strengthening user trust and interpretability.
The Abu Dhabi Statistical Code of Practice 2025 demonstrates how global quality standards can be translated into a modern, operational and legally anchored framework that safeguards trust, strengthens comparability and transparency, and ensures responsible innovation in an increasingly automated statistical environment.
Aysha Almarzooqi
Statistics Center Abu Dhabi
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Aysha Almarzooqi is a Specialist in Statistical Quality Assurance at the Statistics Centre – Abu Dhabi, where she leads initiatives to strengthen statistical governance, quality assurance, and compliance with the Abu Dhabi Statistical Code of Practice. She has over four years of experience in quality management, policy development, and organizational excellence, including ISO 9001 implementation, innovation systems management, and cross-government process standardization. Her current work focuses on designing system-based quality evaluation tools, advancing statistical quality frameworks, and embedding sustainable quality practices across decentralized producers of official statistics. Aysha holds a Bachelor of Science in Finance from Zayed University.
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Presentation title
The LEG on Quality After 25 Years: Achievements, Challenges, and the Road Ahead
In June 2001, the Leadership Group (LEG) on Quality presented its final report, marking a major step in the development of a systematic approach to quality within the European Statistical System (ESS).
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The report covered a broad concept of quality in official statistics, extending beyond accuracy to include relevance, timeliness, coherence, accessibility, comparability, and user orientation. It introduced a comprehensive quality framework grounded in process thinking, customer orientation, continuous improvement, transparency, staff engagement and organizational responsibility, and formulated 22 recommendations aimed at strengthening quality across the ESS. Notably, the biennial Conference on Quality in Official Statistics was itself proposed as Recommendation 14 in the LEG report, illustrating the group’s long-term vision for sustaining a European quality community.
This paper looks back at the work of the LEG on Quality 25 years after the publication of its final report and reflects on its relevance in today’s statistical environment. It revisits the LEG’s key ideas and recommendations and considers how they have shaped quality practices within the ESS over the past quarter century. How far have we come in embedding quality principles into official statistics? Which recommendations have stood the test of time, and which areas still pose challenges?
We will examine how evolving priorities—such as digitalization, data integration, and artificial intelligence—are reshaping the concept of quality in official statistics. What does “quality” mean in an era of big data and rapid technological change? How can the ESS continue to uphold trust and relevance while adapting to new technological conditions?
Looking forward, the paper considers current and emerging challenges, including increasing complexity of data sources, rising demands for timeliness, shrinking budgets and resources, pressures on respondent cooperation, and growing concerns about trust, independence, and the role of official statistics in democratic societies. In the face of increasingly difficult circumstances, how can public statistics continue to serve the common good? By reflecting on the legacy of the LEG on Quality and its impact over a quarter-century, this paper aims to stimulate discussion on the future of quality management in official statistics—building on strong foundations while embracing innovation.
Lyberg, L., M. Bergdahl, M. Blanc, M. Booleman, W. Grünewald, M. Haworth, L. Japec, T. Jones, T. Körner, H. Lindén, G. Lundholm, M. Madaleno, W. Radermacher, M. Signore, M. J. Zilhão, I. Tzougas, and R. van Brakel. (2001) Summary Report from the Leadership Group (LEG) on Quality. Luxembourg: Eurostat.
Lilli Japec
Statistics Sweden
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Dr. Lilli Japec has 30+ years of experience in statistical production, survey methodology, and quality management. She is currently Senior Scientific Advisor at Statistics Sweden where she has previously served as Director of Research and Development and Quality Director. She is Chair of the Board of Chief Editors of the Journal of Official Statistics and one of the editors of the special issue on future challenges and research needs for official statistics.
Dr. Japec is actively engaged in international innovation and foresight initiatives. She is a member of the UNECE Blue Skies Thinking Network and the European Innovation Network, leading a taskforce on horizon scanning.
Her academic work includes numerous publications on survey quality. She is co-editor of Advances in Telephone Survey Methodology (2008) and Big Data Meets Survey Science (2021). In 2021, she received the AAPOR Warren J. Mitofsky Innovators Award for collaborative contributions to innovation.
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Presentation title
CSO Data Strategy - Harnessing data as a strategic national resource
Official statistics are operating in an increasingly complex and fast-evolving data environment characterised by growing data volumes, new data sources, rising user expectations, and rapid technological change.
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In response to these challenges the Central Statistics Office (CSO), Ireland has recently published a Data Strategy aimed at ensuring that official statistics remain relevant, trusted, and fit for purpose in the years ahead.
Central to the CSO Data Strategy is the recognition that quality and relevance must evolve together. While traditional surveys and censuses remain foundational, the strategy places increased emphasis on the responsible use of administrative data, data integration, and emerging data sources to enhance timeliness, granularity, and analytical value. This transition is underpinned by strong governance, legal safeguards, and a quality-by-design approach that embeds statistical standards and metadata throughout the data lifecycle.
The strategy highlights the critical role of data standards and interoperability in maintaining coherence across a diversified data ecosystem. By promoting common definitions, classifications, and identifiers, the CSO seeks to reduce fragmentation, support data reuse, and lower respondent burden, while improving comparability and transparency for users. These measures are essential to sustaining confidence in official statistics as data sources and methods become more complex.
Aligned with the conference theme of quality in a changing landscape, the CSO Data Strategy also addresses organisational capability, skills, and culture. It emphasises collaboration across the public service, innovation in statistical methods, and clear communication with users to ensure statistics continue to meet evolving policy and societal needs. Together, these elements position official statistics as a stable, supportive anchor within a rapidly changing national data landscape.
Ken Moore
Central Statistics Office
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Ken Moore is a career statistician having worked in the Central Statistics Office, Ireland for almost 30 years. During this time he has worked in Earnings Statistics, Transport statistics and for the last 10 years in the area of Quality Management. Ken heads up the Quality Management, Support and Assurance division in CSO and is responsible for the strategic management of quality in both the CSO and across the Irish Statistical system as well as leading on the Standards pillar of the National Data Infrastructure. He enjoys collaborating with colleagues in the CSO and the national statistical system to improve the quality of their methodologies, processes and outputs.
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