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Session 4

Continuous Improvement of Quality Management Systems

3 June 2026
11:00 – 12:30
ŠIBENIK V

Presentation title
Process Excellence: Improving statistical quality through visibility of end-to-end processing
Improving the quality of official statistics demands a deep understanding of the end-to-end processes underpinning their production.

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The Office for National Statistics - the UK’s largest independent producer of official statistics - is undertaking an ambitious project to strengthen quality by systematically mapping, visualising, and continuously improving the production ecosystem. The aim is to create a transparent and consistent approach that identifies risks, inefficiencies, and opportunities for improvement across the statistical lifecycle.

The initiative was designed to address three core objectives: to enhance process transparency; embed quality assurance throughout production; and foster a culture of continuous improvement. By mapping processes end-to-end, we sought to make implicit knowledge explicit, enabling teams to see how activities connect and where vulnerabilities may arise. This approach supports alignment with wider quality initiatives, and provides a foundation for evidence-based decision making.

Our methodology combines process mapping and detailed workflow capture from subject-matter experts, supported by risk-based assessments. Bespoke visualisation tools were created to provide clear, accessible representations of complex processes. Insights could then be used to inform targeted interventions, including redesigning workflows, clarifying roles and responsibilities, and introducing performance monitoring.

Successful implementation has taken place for several key national statistics. Process visualisation was found to improve understanding and accountability across teams, while early intervention of quality risks reduced the likelihood of errors. Streamlined workflows delivered efficiency gains without compromising accuracy or coherence. Importantly, the initiative also constituted a cultural shift: embedding continuous improvement as a shared responsibility rather than a reactive exercise.

These findings highlight that improving statistical quality through deep understanding of production is not a one-off activity, but an ongoing commitment. By institutionalising this approach, national statistical institutes can strengthen resilience and adaptability, ensuring that innovation supports rather than mimics quality. Our presentation will offer practical insights and a replicable model for embedding process-based quality improvement within official statistics production.

Main author / Presenter
Sophie Edgar-Andrews, Sam Baker
Office for National Statistics

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Sophie Edgar-Andrews is a Deputy Director at the Office for National Statistics (ONS): the UK’s largest independent producer of official statistics. Her team is leading on the cross-office delivery of new ways of working and visual tools that will provide increased visibility of the end-to-end processes underpinning the production of core statistics. Sophie has a background in advanced analytics and was Head of Analytical Capability at the UK Health Security Agency prior to joining ONS in 2023. She has also led analytical teams through the COVID-19 pandemic as both a Consultant in Data Management and Analytics for the World Health Organization; and later as Head of Thematic Assessment at the Joint Biosecurity Centre. She has a PhD in Immunology and spent 5 years in academia undertaking research on HIV evolution. Sophie is is passionate about driving quality improvement holistically, through optimising infrastructure, systems, and practices. Sam Baker is a Senior Policy Advisor at the Office for National Statistics (ONS). She is part of the core team responsible for developing the Process Excellence toolkit to ensure it is fit for purpose and practicable at an organisational scale. Sam has a background in operational research and policy, and is currently on secondment to ONS from Statistics New Zealand, where she previously supported the New Zealand’s 2023 Census, developing field processes, contingency plans, and the knowledge base serving over 3000 staff. She has experience undertaking policy analysis across strategic, legislative, and operational domains, and also has an academic background in bioscience, having worked in multiple laboratory settings, where following robust processes with sound quality assurance was essential. Sam is eager to ensure Process Excellence is adopted with ease and enthusiasm, positioning production teams and their users at the centre of iteration decisions to ensure Process Excellence is a sustainable framework that maintains a fit for purpose status while producing tangible results.

CO-AUTHORS:

Presentation title
Innovation at Statistics Portugal: 20 years of Evolution
Over the past two decades, the global data ecosystem has undergone a radical transformation.

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For Statistics Portugal (INE), this period represents a profound evolution, where innovation transitioned from a peripheral activity into a core, cross-cutting pillar. This journey has reshaped our processes, methodologies, and corporate culture, ensuring that official statistics remain a fundamental pillar of democracy in an era of rapid change. The National Data Infrastructure (IND) stands as the primary catalyst for this trajectory. More than a technological upgrade, the IND represents a holistic paradigm shift. By leveraging interoperability and the intensive use of administrative and other data, it has moved the NSI from traditional survey-based models toward a modern, integrated data-driven approach. This evolution has not only enhanced production efficiency but also enabled the creation of new, more granular statistical products. The current proliferation of data and the speed of information transmission—while posing significant challenges regarding misinformation—have provided Statistics Portugal with the opportunity to reinforce its role as a robust and credible information source. Through institutional examples and practical use cases, this presentation explores how strategic innovation and technical interoperability have been operationalized. We will demonstrate how this evolution provided the "rapid reaction" capability essential during global shocks, such as the COVID-19 pandemic, and how it continues to define a modern, resilient model for official statistics in the 21st century.

Main author / Presenter
Paulo Saraiva
Statistics Portugal

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**Paulo Saraiva** is **Data Management Director at Statistics Portugal**, where he leads the strategic coordination of data collection, administrative data integration, and the development of integrated data infrastructures for official statistics. He oversees the end to end management of survey, census, and administrative data, with a strong focus on quality, efficiency, and coherence across statistical processes. His work centres on the modernisation of statistical production, including the transition towards multi source statistics, the reduction of respondent burden, and the reuse of data for multiple purposes. He is actively involved in governance, interoperability, and ethical frameworks, ensuring innovation is aligned with legal requirements, data protection, and public trust. Paulo contributes to international statistical cooperation, notably within UNECE and HLG MOS, and brings an engineering background that supports a systems oriented and pragmatic approach to data management. Sofia Rodrigues, with a degree in management, is the Head of the Administrative and Business Data Unit at Statistics Portugal. Throughout her professional career, she has extensive experience in several statistical fields, including National Accounts; Planning; Business Registers and Business Statistics. Since 2019, she has been responsible for managing the team focused on collecting and analysing data from companies and institutions through online surveys, and administrative and other sources. Her work supports the production of official statistics and the provision of reliable and accessible data for scientic research. Portuguese representative of the European Inovation Network.

CO-AUTHOR:

Presentation title
Building a Culture of Quality in Official Statistics: Methodological and Technical Approaches at Croatian Bureau of Statistics
Quality in official statistics is a fundamental condition for credibility, public trust and evidence based decision making.

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Legally and methodologically, quality requires that national and European statistics are developed, produced and disseminated in accordance with harmonised standards, internationally comparable definitions and uniform methodological approaches. From the user perspective, quality means receiving clear, transparent and comprehensive information on the background, methodology and key quality dimensions of statistical outputs, enabling correct interpretation and appropriate use. The Croatian Bureau of Statistics (CBS) is firmly committed to ensuring quality statistical production for both national and European purposes. This commitment is grounded in the principles of the European Statistical System (ESS), the European Statistics Code of Practice and the ESS Quality Assurance Framework. Within this framework, quality is understood as a multidimensional concept that evolves through continuous interaction with users, systematic feedback mechanisms and the ongoing optimisation of statistical processes, methods and products. Building and maintaining a strong quality culture across the institution requires an integrated quality management system that encompasses all organisational levels. This includes strategic planning, operational coordination and the implementation of individual statistical processes aligned with the Generic Statistical Business Process Model (GSBPM). Such an approach promotes transparency, continuous improvement and active staff engagement, ensuring that quality principles are embedded in everyday statistical work. Quality reporting plays a central role in reinforcing this culture. CBS systematically prepares quality reports using harmonised ESMS, ESQRS and SIMS structures, supported by a dedicated quality reporting application. Reporting in accordance with the SIMS framework is detailed and seeks to provide a comprehensive and standardised description of statistical processes, methodologies, data sources, quality indicators and quality assurance measures. To support this work, CBS uses the POMI quality reporting system, which brings together quality reporting structures within a single environment and supports consistency, completeness and efficient management of quality metadata across statistical domains. In addition, CBS is engaged in the further development of its data and metadata exchange systems. The implementation of the latest version of SDMX (Statistical Data and Metadata eXchange) supports increased standardisation, automation and interoperability of statistical processes. By adopting updated SDMX standards, CBS seeks to improve the efficiency of data exchange with national and international partners and to support the consistent and transparent communication of quality-related metadata. Through coordinated efforts, CBS strengthens user trust, supports informed decision-making, enhances reliability and credibility of official statistics, and adapts to evolving requirements of statistics and quality standards.

Main author / Presenter
Snježana Mikša
CBS

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Snježana Mikša is a Senior IT Technician at the Croatian Bureau of Statistics (CBS). She provides technical support for the POMI quality reporting application, helps transfer and update data, optimises system functionalities, and supports standardised reporting processes such as SIMS and ESQRS. Snježana also trains statisticians to use the system efficiently, contributing to reliable, consistent, and accessible quality metadata. Ivana Krušec is a Senior Advisor in the CBS Quality Department. She coordinates quality report preparation, monitors deadlines according to the Quality Reporting Calendar, and provides guidance to statisticians. Ivana fosters transparent and complete reporting, offers methodological support, and helps statisticians address challenges. Through close collaboration, they represent a team that listens to user needs, tackles challenges, and develops simpler, more efficient solutions, ensuring higher-quality, more user-friendly statistical outputs.

CO-AUTHOR:

Presentation title
Enhancing the Quality of Official Statistics through Technological Innovation: Framework, Challenges, and the Path to Sustainable Trust
Rapid technological innovation and growing demand on official statistics are accurate, timely, high-quality, and trustworthy.

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Beyond improving operational efficiency, a key institutional challenge lies in understanding how technology and innovation can be systematically leveraged to enhance the intrinsic quality of official statistics and strengthen sustainable public trust.

This paper addresses the following research question: how can National Statistical Offices )NSOs( effectively integrate technological innovation into their statistical production systems to improve statistical quality and reinforce institutional trust over the long term? The study is based on the hypothesis that the impact of technology on statistical quality and trust is not automatic, but mediated by organizational readiness, human capital, governance structures, and quality management frameworks.

A multi-level methodological approach is adopted. First, the study reviews contemporary quality frameworks for official statistics in the digital era. Second, it conducts a comparative analysis of international best practices in statistical digital transformation, drawing on experiences from the Netherlands, Canada, Singapore, and Portugal. Third, it develops an applied operational framework informed by the institutional experience of the Central Agency for Public Mobilization and Statistics (CAPMAS).

The main output of the study is the Digital Convergence Model, designed as both a diagnostic and implementation tool that aligns technological investments with quality objectives and trust-building mechanisms. Applied at CAPMAS, this model guided the adoption of cloud platforms and artificial intelligence, which reduced report production time from six weeks to ten days and improved data accuracy by 75%. Trust was further enhanced through the establishment of a Data Ethics Committee. The findings indicate that the adoption of technologies such as artificial intelligence, big data, and cloud computing can significantly improve data accuracy, production timeliness, and accessibility when embedded within coherent governance and quality management arrangements. However, the study also identifies key challenges, including system integration constraints, shortages in advanced digital skills, and cultural resistance related to algorithmic transparency and public trust.

The paper concludes that technology should be viewed as an enabling factor rather than a standalone solution. Its main contribution lies in offering a practical and transferable framework to support NSOs in assessing digital maturity, aligning innovation with official statistics quality frameworks, and placing trust at the core of digital transformation strategies.

Main author / Presenter
Mohammed Alaa Eldin Abdelrahman
Central Agency for Public Mobilization and Statistics (CAPMAS), Egypt

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Mohamed Alaa El-Din Abdel Rahman is a Senior Data Analyst at the Central Agency for Public Mobilization and Statistics (CAPMAS) in Egypt. He holds an MBA from the French University ESLSCA in Cairo and a Doctor of Business Administration (DBA) from Helwan University. His research focuses on studying the impact of technology and innovation on enhancing the competitiveness of data services, with applied case studies on CAPMAS. He is a member of the Young Statisticians Association and has extensive participation in international scientific conferences and advanced training courses organized by prestigious organizations such as JICA, Eurostat, and GIZ. His work aims to bridge academic research with practical application to improve the quality and reliability of statistical data at the national level.