Session 2

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Title of session: Quality management and model implementation II

Chair: Claudia Junker

Room: S2 Wawel

Time: 17:00 - 18:30

Date: 27 June

Session 2 - papers & presentations

Presenting AuthorAbstract
Hans Viggo Sæbø
Title: <<< Beyond Code of Practice – New quality challenges in official statistics >>>
Globalisation, technology, competition and changes in the political agenda have led to a demand for new statistics, but also provided new possibilities in terms of new data sources and partnerships. However, these developments have challenged statistical institutes’ compliance with principles and values described in quality frameworks for official statistics. Such frameworks constitute the backbone of quality management in statistical institutes. The European Statistics Code of Practice is an example developed particularly for the production of European statistics, but there are also similar frameworks for official statistics developed by UN, OECD and regional statistical cooperation bodies. They are all inspired by and build on the UN Fundamental Principles, and have developed gradually or revised to address new challenges. However, having a quality framework is not enough, compliance is, in practice, difficult since there often are trade-offs between such principles. Official statistics must be trustworthy, but at the same time relevant and timely, and this may challenge the independence of a statistical institution. Utilisation of new data sources implies challenges for accuracy and reliability, and meeting competition with partnership may harm equal treatment and confidentiality principles. The paper considers these quality challenges, how they are handled in the existing quality frameworks and could be met by the statistical institutes. Statistical professionalism is a key word in this context. Reflecting on new quality challenges may guide the way forward, on creating and maintaining a culture for continuous improvement in European and national statistics. The starting point of the paper is international, but examples are mainly from Statistics Norway. However, these are believed to be representative for several statistical institutes.
Michael Colledge
Title: <<< Experiences in Developing Statistical Quality Frameworks >>>
Over the last 30 years or so a statistical quality framework has become accepted as an essential part of the infrastructure of a statistical office. It provides a systematic mechanism for ongoing identification and resolution of quality problems, and for maximizing the interactions between office staff. It is a basis for creating and maintaining a quality culture within the office and is a valuable source of reference material for training. It makes transparent the processes by which quality is assured and reinforces the image of the office as a credible provider of good quality statistics. It facilitates exchange of ideas on quality management with other national and international producers of statistics. With this in mind, the UN Statistical Division published a template and guidelines for a national quality assurance framework, which were endorsed by the UN Statistical Commission, and many statistical offices have installed a quality framework. So, what have been the consequences? The paper outlines the approaches and lessons learned in developing statistical quality policies, frameworks, and guidelines in six developing and developed countries and in three international organisations. It describes the common features of, and differences in, the various approaches. It summarises what the impacts have been, what seems to have worked, and what has not. The paper concludes with a discussion of the relationships between quality management, methodology development, metadata management and risk management.
Ana Cristina Martins Bruno
Title: <<< Quality and Risk Management at Brazilian Institute of Geography and Statistics: a short report of a work in progress >>>
The paper aims to present and discuss the risk management model integrated to the quality management model under construction at the Brazilian Institute of Geography and Statistics (IBGE). Quality management and risk management are strategic issues, part of the modernization program linked to the strategic institutional plan 2017-2027. In Brazil, risk management has been strongly recommended by the Federal Court of Accounts (TCU), which has developed a model for assessing organizational maturity in risk management based on best international practices such as COSO, ISO 31000 and British Orange Book. The model proposed by the TCU will be used for risk-based external audits and will evaluate management in four dimensions: capacity, translated in terms of leadership, policies and strategies; preparation of people in risk management; application of these capabilities to processes and partnerships; and evaluation of results and performance of the mission. The model will be adopted by IBGE as a self evaluation reference to identify realistics targets for improvements and produce action plans for developing or enhancing risk capabilities. The work has been carried out by the Governance, Conformity and Risks Committee together with the Quality Committee, that is responsible for the implementation of quality policy and process management, based on generic models GSBPM (Generic Statistical Business Process Model) and GAMSO (Generic Activity Model for Statistical Organizations). Although the project is in the initial phase and has not yet generated effective demonstrable results, it is intended to present the model as a Position Paper, portraying a short report of work in progress under the brazilian Institute.
Silvia Losco
Title: <<< Strategies and approaches for managing risks in the official statistics production: ISTAT experience in the modernization programme. >>>
Today institutional organizations are facing pressing and emerging challenges going to meet the speed of technology, the demands for change, necessary to ensure efficiency and competitiveness. Change processes are carriers of risks and opportunities because they are able to create value. Processes must be on time and flexible but it is necessary increase attention to the risk analysis, both at strategic and operational level, to assure the achievement of the goals. Italian national Institute of Statistics (ISTAT), with the modernization program (2016), has adopted a complete program of change with the aim of evolving the statistical production system from traditional survey models based on the direct acquisition of data from citizens and companies towards a model that uses statistical registers. It is an ambitious program that aims to overcome the "silos" vertical processes of traditional statistical production with a high level of risk. The model provides the creation of an integrated system of registers, of a single logical infrastructure of data deriving from administrative sources, from new innovative sources (Big Data) and powered by continuous data flows. The paper describes the framework adopted by ISTAT to organize the activities of the program, focusing on strategies developed for risk management. At the enterprise level one of the actions implemented to minimize the risks of statistical activities is the identification of seven strategic innovation programs and the adoption of a Portfolio and Project Management (PPM) approach. According to Business Architecture model (BA), at the corporate level risk management is implemented through an organization of the activities in thematic portfolios connected to statistical registers and to service for statistical production. At operational level, the statistical activities organization following a management by project approach that select initiatives and organize the work in phases, with a specific control of risks associated to the single phase.
Marina Signore/Nilgun Dorsan
Title: <<< Recent developments in the Generic Statistical Business Process Model: Revisions and Quality Indicators >>>
The Generic Statistical Business Process Model (GSBPM) is widely used by the statistical community for a range of different purposes from process documentation and monitoring to training staff. It is currently maintained and updated by the UNECE Supporting Standards Modernisation Group. GSBPM is revised every five years in order to keep it relevant and continue to serve as a common framework for the modernisation of official statistics. The current version (v5.0) was released in 2013. The next revision of the GSBPM started in 2017 where statistical organisations were asked to provide feedback on the model. This feedback has been posted to the public discussion forum on the UNECE GSBPM website, and a group of experts are reviewing and proposing solutions to the issues. The most common issues include how to interpret the model, practical use and application of GSBPM in real life. In 2016, Quality Indicators for all sub-processes of the Generic Statistical Business Process Model (GSBPM) (v1.0) were developed in order to monitor the quality of statistical production. The first version focused on quality indicators for surveys, and complemented the quality management process of the GSBPM. There was a need to incorporate indicators pertaining to administrative data. Therefore, this work was expanded to include quality indicators for administrative data. Version 2.0 of the Quality Indicators for the Generic Statistical Business Process Model (GSBPM) was released in November 2017. In this version, the quality indicators are integrated in each GSBPM sub-process for both surveys and administrative data sources. Another output of the quality indicator work was proposing changes to the GSBPM. This paper describes the proposed changes to the GSBPM resulting from input from statistical organisations and Quality Indicators.

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