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Session 12

Rethinking Quality Framework

3 June 2026
16:30 – 18:00
ŠIBENIK III

Presentation title
Trusted data quality for evidence – Towards a new OECD Quality Assurance Framework
In recent years, the digitalisation of economies and societies has led to an explosion of data sources (geospatial data, citizen generated data, administrative and survey microdata, mobile positioning data, transaction data, etc.), coupled with increasing computing capacity to exploit them.

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The OECD is increasingly using different data sources for evidence in its analytical and quantitative work. Combining this data with data collected from its member countries provides the Organisation with the potential to generate new, timelier, and more granular evidence and information for citizens, analysts and policy makers. Along with these opportunities, evidence based on various data sources also entails important quality challenges that extend beyond the traditional quality criteria and require to reconsider the quality assurance framework and widen its scope. This paper presents some lessons from the OECD experience of developing a renewed quality assurance framework covering different data sources and aligned with the need to ensure that its data and statistics remain of high quality and continue to command public trust. The review of a sample of criteria included in frameworks, guidelines, and recommendations released by international organisations and national statistics offices, enabled the compilation of a list of quality criteria and the identification of groups by similarity. The definitions within each relevant group were analysed to revise the definitions of the quality criteria incorporated into the revision process for the OECD quality assurance framework. This paper presents this journey, the challenges and the main outcomes of this analysis and the revised definitions of quality criteria within the OECD quality assurance framework. It highlights the key role of metadata, the need to build a culture of continuous improvement, to develop quality metrics to measure progress over the data lifecycle, and to what extent artificial intelligence could support quality management. Although still in progress, this work already provides important insights into the quality of different data sources and highlights the need for the producers of official statistics to update their quality assurance framework and potentially other frameworks (data governance, ethics) in order to unlock the potential of these various data sources while maintaining user trust.

Main author / Presenter
Nora Bohossian
OECD

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Nora Bohossian works as a Statistician/Analyst in the Global Relations Unit of the OECD Statistics and Data Directorate. Prior to joining the OECD in 2023, she worked as a Senior Methodologist for Statistics Canada where, since 2006, she provided methodological services to a variety of economic and social statistics programmes in increasingly more responsible roles. Before working for the Government of Canada, she also worked as a business process analyst and a software developer in the private sector. Julien Dupont has been working in various areas at the OECD Statistics and Data Directorate for more than 25 years. Heavily involved in the coordination of the statistical reviews of candidate countries for accession to the OECD since 2007 and the development of the OECD Recommendation on Good Statistical Practice, the first OECD legal instrument on statistics, his current work involves heading up global relations within the Directorate, coordinating statistical reviews, coordinating the development of the new OECD quality assurance framework, and managing the OECD internal quality reviews. Julien is a member of several international expert groups, including the organizing committee of the Expert meeting on modernising statistical legislation, the UN Expert Group on National Quality Assurance Framework, and the Eurostat Working Group on Quality, and participated in the European conferences on quality in official statistics since 2016 .

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Presentation title
Impact on the statistical quality frameworks of the new legal basis for accessing PHD
The Data Act adopted in 2023 was the first legal act regulating the access to privately held data (PHD) for public needs, the so-called B2G data.

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However, it just included a free access for emergency situations closing the door for the use of these data for the regular production of official statistics. Regulation 223/2009 on European Statistics introduced in 2024 a procedure for accessing privately held data for the development and production of European official statistics on a sustainable basis and according to fair, clear, predictable and proportionate rules.

In addition, a new Digital Omnibus Regulation Proposal has been submitted by the Commission in November 2025 introducing common rules on data sharing, including the amendment of the Data Act and the Data Governance Act.

The new legal basis poses some questions with a clear impact on the statistical quality framework that are still to be solved, such as the request of PHD and the respect of Principle 9 on Non-excessive Burden on Respondents, the different consequences on the private data holders for not providing data and the principle 6 on Impartiality and Objectivity, the need to decide when the procedure is initiated by the Commission or the Member States and the principle 1bis on coordination and cooperation in the European Statistical System, the impact on principle 5 on statistical confidentiality and privacy of the voluntary use of a secure infrastructure set up by Eurostat…etc.

As the European Statistical System (ESS) is currently engaged in the implementation of the new legal framework, this represents an opportune moment to conduct a comprehensive analysis of all issues related to the impact of the new rules on the quality framework, as well as their short- and medium-term implications for the Member States and the ESS.

Main author / Presenter
Ana Cánovas Zapata
INE SPAIN

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Holds a Degree in Business Administration, a Degree in Law and a Masters’ Degree in Applied Statistics for the Public Sector. Joined the Public Administration as State Statistician in 2012. Working experience of 14 years at the National Statistics Institute of Spain, first at the Office of the President of INE and in the last 12 years devoted to the International Relations Unit of INE. Among other things, involved in the preparation of the participation of the Presidency of INE in international meetings and events, and in charge of the international statistical cooperation programme of INE. She was member of the Presidency Team at INE Spain during the Spanish Presidency of the European Council in the second semester of 2023, and was fully involved in the negotiations of the amended Regulation 223/2009.

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Presentation title
Strengthening National Statistics through Governance and Interoperability
In the modern statistical landscape, data governance and interoperability are the essential infrastructure upon which reliable, timely, and integrated insights are built.

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The Israeli Central Bureau of Statistics (ICBS) is currently transitioning from traditional data silo management toward a harmonized, interoperable ecosystem designed to serve as a strategic hub for national decision-making.

The ICBS Data Governance Framework

Central to our strategy is the Data Governance Council, comprising representatives from all departments. We are currently advancing a comprehensive Data Governance Framework that serves as an organizational blueprint across four critical pillars:

• Enterprise Data Model: Mapping our organizational data structure for consistency.

• Metadata: Standardizing components across all stages of the Generic Statistical Business Process Model (GSBPM).

• Data Quality: Mandating rigorous, automated checks throughout the production lifecycle.

• Data Security: Ensuring the highest standards of protection for national data assets.

Achieving Interoperability: The Master Code List

A primary challenge in modernizing official statistics is harmonizing diverse administrative data sources, where operational definitions often diverge from statistical requirements. To address this, the ICBS is spearheading the creation of a Government Data Lake.

One of the core aspects of this initiative is creating Master Code Lists—which are unified sets of standard classifications. Our implementation process of the Master code lists includes:

1. Business Collaboration: Aligning with government entities and international standards (e.g., SDMX).

2. Gap Mapping: Resolving discrepancies via specialized conversion lists.

3. Regulatory Approval: Appointing data owners and securing endorsement from the Governance Council and ICBS management.

4. Systemic Implementation: Embedding these lists into the GSBPM, from initial needs specification to final dissemination.

Impact and Results

To date, the ICBS has published 20 Master Code Lists on official governmental platforms, providing a "single source of truth" for ministries and the public. Furthermore, we have successfully linked our data lake assets to a centralized Metadata Layer. This Data Catalog, featuring 30 comprehensive metadata fields, ensures that our data assets are fully discoverable and actionable.

Through these initiatives, the ICBS ensures that Israel’s statistical outputs remain robust, transparent, and globally aligned.

Main author / Presenter
Muriel Shafir
Head of Senior Data Governance Department, Israeli Central Bureau of Statistics (ICBS)

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Muriel Shafir is the Head of Senior Data Governance Department in the Data Management and Data Lake Division in the Israeli Central Bureau of Statistics (ICBS). She holds a M.Sc. in Agricultural Economics and Management from the Hebrew University of Jerusalem. Since joining ICBS in 2007, she has held a variety of roles, including Head of the Metadata Division and Senior Coordinator for Economic and Quality of Life Indicators. In her current role she leads the modeling of ICBS data into the governmental data lake through analyzing data sources and converting source data to harmonized data. She also plays a critical role in defining metadata components and applying international standards such as the single integrated metadata structure (SIMS). As a member of the CBS Governance Council, she contributed significantly to the CBS Governance Framework and is involved in cross-unit collaboration and international learning in metadata management.

Presentation title
Ensuring the Quality of FAO Statistics: The Statistics and Data Quality Assurance Mechanisms
Producing high-quality statistics is a core function of the Food and Agriculture Organization of the United Nations (FAO), mandated since 1945 to collect, analyze, and disseminate information on food, agriculture, and related domains.

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Operating in a decentralized statistical system increasingly shaped by big data, geospatial technologies, and other non-traditional sources, FAO faces new challenges such as data privacy, intellectual property, and sustainability of data sources. To strategically respond to new and traditional challenges, FAO introduced in 2023 the Statistics and Data Quality Assurance Framework (SDQAF), replacing and expanding the framework adopted in 2014. The SDQAF sets out 16 principles covering institutional environment, statistical processes, and outputs to ensure FAO’s data remain relevant, accurate, coherent, and accessible. To complement the SDQAF, FAO has developed a suite of corporate Statistical Standards to harmonize practices across all phases of the data production process—from data collection and processing to data and metadata dissemination. These standards provide technical guidance, governance procedures, and practical tools, and are regularly updated to reflect evolving data ecosystems and best practices, ensuring consistency and responsiveness to emerging needs.

This paper examines how FAO operationalizes the SDQAF through a strengthened governance structure and systematic activities related to quality assurance. Central to this architecture, led by the Chief Statistician and supported by the FAO Data Coordination Group, is the establishment of the Data Quality Unit (DQU) within the FAO Statistics Division in 2024. The DQU plays a pivotal role in maintaining and developing corporate statistical standards, and in implementing procedures used to assess the quality of existing statistical activities and data platforms, which include self-assessments through the Quality Assessment and Planning Survey (QAPS), in-depth quality assessments on selected statistical activities, and peer reviews to validate outputs prior to dissemination. These processes are complemented by user consultations and standard-based assessments, ensuring compliance with SDQAF principles and guiding continuous improvement. Together, these tools provide a structured approach to monitoring quality at both process and output levels, enabling FAO to identify gaps and priorities, generate actionable recommendations and improvement plans, inform FAO’s statistical workplan, and embed quality assurance principles throughout FAO’s decentralized statistical system.

Hence, the quality assurance mechanism translates quality principles into operational practice, fosters a culture of quality and continuous improvement, and ensures that FAO’s statistics remain trusted, relevant, and fit for evidence-based decision-making.

Main author / Presenter
Clara Aida Khalil
Food and Agriculture Organization of the United Nations

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Ms Clara Aida Khalil is the Team Leader of the Data Quality Unit of FAO’s Statistics Division. In her role, she coordinates the development and revision of FAO Statistical Standards, and the implementation of quality assessments on FAO statistical and data processes and outputs.

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Presentation title
Integrating Process Model Modernisation and Internal Quality Reviews: A Strategic Approach to Quality Culture at SURS
Following the recommendations of the 2023 Peer Review, the Statistical Office of the Republic of Slovenia (SURS) launched a comprehensive project to modernise its process model in line with the Generic Statistical Business Process Model (GSBPM) version 5.2 and to establish an internal quality review framework.

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The project involves all organisational units, as the modernisation affects core statistical processes, supporting documentation, and information systems used across SURS.

The updated process model strengthens alignment with international standards, facilitates digital transformation (including the integration of artificial intelligence and process automation), and enhances modularity and process repeatability. Key changes include the introduction of the new Build phase, the integration of overarching processes (quality management, metadata management, and information security), and updates of essential documents, such as the Guidelines for Quality Assurance and the Process Inventory for Statistical Surveys. These updates provide the basis for the harmonised and systematic implementation of statistical processes.

The internal quality review system is based on the updated process model and the principles of the European Statistics Code of Practice. It incorporates the development of a structured self-assessment questionnaire to enable systematic monitoring of compliance, identification of improvement areas, and the strengthening of a quality-oriented organisational culture.

The project fosters collaboration across sections, promoting a shared understanding of processes and supporting continuous organisational learning. This unified approach strengthens institutional capacity, ensures compliance with European and international standards, and creates a foundation for future innovations in statistical production, including advanced automation and AI-driven solutions.

Main author / Presenter
Mojca Eremita
Statistical Office of the Republic of Slovenia (SURS)

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Mojca Eremita is Head of the Metadata and Quality Section at the Statistical Office of the Republic of Slovenia (SURS), where she leads strategic initiatives, metadata management, and quality assurance. She has eight years of professional experience at SURS, including six years in the Agriculture, Forestry, Fisheries, and Hunting Statistics Section before assuming her current leadership role. Her section is responsible for planning statistical surveys and processes, managing statistical documentation, reviewing internal instructions, and supporting quality reporting and methodological explanations. Her current focus is on modernising the SURS process model in line with GSBPM 5.2 and embedding internal quality reviews to foster a unified quality culture across the organisation.

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Presentation title
From Assurance to Culture: the Evolving Role of ASPIRE at Statistics Sweden
Without a well-established quality culture, the risk of producing data or statistics that are not fit for purpose or are flawed will increase.

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This can ultimately undermine trust and confidence in official statistics and the national statistical system. A defining feature of a quality culture is the shared commitment within a statistical office to focus on users’ needs and deliver statistics of appropriate quality. It is also characterized by a common understanding of key quality-related concepts, shared values and working methods, as well as the systematic use of opportunities for improvement. Furthermore, regular quality reviews and reporting are essential to raise awareness of quality assurance and strengthen the quality culture within an organization.

Depending on the context, quality reviews can serve different purposes:

• to directly stimulate quality improvements,

• to promote a quality culture, and/or

• to enhance external confidence in statistics.

Statistics Sweden has used ASPIRE (A System for Product Improvement, Review and Evaluation) since 2011 for regular and thorough reviews of the accuracy of key statistical outputs aided by external experts (see ES CoP principle 4, indicator 4.4). Initially, ASPIRE was a key component of the agency’s Quality Assurance Framework to restore external confidence in statistics after a period of recurring errors in published data 2008-2010. More recently, as confidence has grown, its role has shifted toward fostering a quality culture.

In this paper, we explore the positive effects of using ASPIRE to date, as well as the challenges we have faced and continue to address. Among other factors such as effective communication and regular follow-up, the commitment of leadership remains crucial to achieving the intended outcomes.

Main author / Presenter
Heather Bergdahl, Sabri Danesh
Statistics Sweden

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To be submitted later.

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