Presentation on theme: "The use and convergence of quality assurance frameworks for international and supranational organisations compiling statistics The European Conference."— Presentation transcript:
The use and convergence of quality assurance frameworks for international and supranational organisations compiling statistics The European Conference on Quality in Official Statistics July 8-11 2008, Rome, Italy Antonio Baigorri and Håkan Linden Statistical Governance, Quality and Evaluation European Commission, Eurostat
2 The context of Total Quality Management To have an encompassing approach with respect to quality work. To implement the principles of institutional quality frameworks and in particular the principles related to statistical processes and outputs. To improve the measurement, monitoring and management of data quality. To coordinate ongoing quality initiatives (process descriptions, quality reports, evaluation activities etc.). To build on existing quality work (standards, best practices etc.). To promote a culture of systematic quality improvement work.
3 Institutional Quality Frameworks User needs Statistical products Production processes Institutional environment Management systems and leadership Support processes Principles: 1.1, 1.3, 1.4, 1,5, 2.1, 2.2, 2.3, 3.1, 3.2, 4.1, 5.3, 5.6, 6.1, 6.2, 7.1, 8.2, 9.1, 10.3, 10.4 Principles: 4.2, 5.1, 5.3, 5.4, 5.5, 8.3, 8.4, 9.2, 9.3, 9.4, 9.5, 10.1 Principles: 1.1, 1.2, 4.3, 4.4, 4.5, 4.6, 5.2, 6.2, 7.1, 7.2, 8.1, 10.2, 10.5 TQM CCSA Principles Institutional frameworks, like the Principles Governing Statistical Activities, the European Statistics Code of Practice and the IMF Data Quality Assessment Framework, can be seen as general superstructures forming the necessary basis for all other measures an International organisation needs for improving quality at statistical output and product level. User needs Statistical products Production processes Institutional environment Management systems and leadership Support processes 1 Professional independence 2 Mandate for data collection 3 Adequacy of resources 4 Quality commitment 5 Statistical confidentiality 6 Impartiality and objectivity 7 Sound methodology 8 Appropriate statistical procedures 9 Non-excessive burden on respondents 10 Cost effectiveness 11 Relevance 12 Accuracy and reliability 13 Timeliness and Punctuality 14 Coherence and comparability 15 Accessibility And clarity TQM Code of Practice Source: Eurostat (2007) User needs Statistical products Production processes Institutional environment Management systems and leadership Support processes 0. Prerequistes of Quality (Legal and institutional environment, Resources, Relevance, Other quality management 1. Assurance of Integrity (Professionalism, Transparency and Ethical Standards 2. Methodological Soundness 3.Accuracy and Reliability 4. Serviceability 5. Accessibility TQM DQAF
4 Quality Assurance Frameworks Quality assurance frameworks (or frameworks for statistics production) have the objective to establish, in a specific statistical organisation, a system of coordinated methods and tools guaranteeing the adherence to minimum requirements concerning the statistical processes and products. Similarly to institutional frameworks, this includes some kind of assessment. Product/ output quality requirements are being explicitely documented. Processes are defined and made known to all staff. The correct implementation of the processes is monitored on a regular basis. Users are being informed on the quality of the products and possible deficits. A procedure is implemented that guarantees that the necessary improvement measures are being planned, implemented and evaluated.
5 Data quality aspects 1)The perception of the statistical product by the user. 2)The characteristics of the statistical product (or key statistical outputs) 3)The characteristics of the statistical production process.
6 Conceptual framework (2) IT conditions (11) – Management, planning and legislation (12) – Staff, work conditions and competence (13) User needs (3) Data collection (4) Validation Country level (5) International level (6) Confidentiality (7) Dissemination (9) Documentation (8) Follow-up (10) RELEVANCEACCURACYACCESSIBILITY/ CLARITY TIMELINESS/ PUNCTUALITY COMPARABILITYCOHERENCE Relationship between process and output quality Source: Eurostat Process Quality Assessment Checklist [Eurostat, 2007]
7 Product/ Output Quality Components OECD:relevance, accuracy, credibility, timeliness (and punctuality), accessibility, interpretability, coherence (within dataset, across datasets, over time, across countries) Eurostat:relevance, accuracy, timeliness and punctuality, accessibility and clarity, coherence (within dataset, across dataset), comparability (over time, across countries) ECB: accuracy/reliability, methodological soundness, timeliness, consistency IMF:prerequisites of quality, accuracy and reliability, assurances of integrity, methodological soundness, serviceability (timeliness and periodicity), accessibility, serviceability (within dataset, across dataset, over time, across countries) FAO:relevance (completeness), accuracy, timeliness, punctuality, accessibility, clarity (sound metadata), coherence, comparability UNESCO:relevance, accuracy, interpretability, coherence UNECE:relevance, accuracy (credibility), timeliness, punctuality, accessibility, clarity, comparability (across datasets, over time, across countries)
9 Quality and metadata Collection and sharing of metadata - SDMX technical standards - SDMX Content oriented guidelines (incl. Cross domains concepts) Dissemination of metadata on quality - Special Data Dissemination Standards (SDDS) - Euro-SDMX Metadata Structure (ESMS) Assessment and monitoring of metadata Integrated information on quality assessment
10 Methods and tools for the assessment of statistics production Production processes Statistical products User perception Process variables Quality indicators Quality reports User satisfaction survey Self assessmentsQuality reviews Labelling Institutional/ legal environment User requirementsStandards III. Conformity II. Evaluation I. Documentation Measurement Improvement actions N.B. Figure derived from draft Handbook on Data Quality Assessment Methods and Tools (DatQAM), version 31.01.2007.
11 How to apply process oriented quality assessment tools The office-wide management approach Institutional preconditions (procedures and legislations) Assessment methods already in use Relevance – size and periodicity Relevance – importance and specific legal frameworks
12 Quality Assessment Packages Fundamental package Intermediate package Advanced package Process descriptions, product documentation, quality guidelines Quality reports Self assessments Quality reviews User satisfaction surveys Quality indicators Key process variables Labelling N.B. Figure derived from draft Handbook on Data Quality Assessment Methods and Tools (DatQAM), version 31.01.2007.
13 The assessment methods and tools Documentation and measurement - process descriptions - quality reports (“Full Quality Report”, “Summary Quality Report”, and “Basic Quality Information”). - user satisfaction surveys Evaluation - self assessments of all production processes (Quality Assessment Checklist) - quality reviews for key statistical outputs Conformity - a process for labelling of key international statistics
14 Principles for implementation Minimise burden for production domains - test the approach in advance - provide support - flexibility Build on existing information - process analysis - metadata on quality (quality reports etc.) Profit from synergies with other horizontal activities - evaluation function requirements - cost/ benefit analysis - input for management programming
15 Data quality assessment recommendations Top management commitment The role of middle managers Data quality assessment is a long term project Most methods should be implemented and fine-tuned in pilot projects Standardise the use of the methods Establish clear responsibilities and authorities Sufficient resources allocated for supporting the assessments
16 Conclusions Each international organisation should have a quality assurance framework in place. The framework and the applied quality principles should be made explicit. A quality assurance framework needs to be compatible with the general quality management model and office-wide procedures and rules. It should be built into the organisational structure. It contributes to increased awareness of quality concepts and promotes best practices. It provides a mechanism for reengineering and quality improvements It should always acknowledge performance/ cost. Convergence of quality assurance frameworks by applying common concepts, standards, methods and tools (both content oriented and technical). Development and sharing of “best practices” for statistics production between all stakeholders is maybe the most important for continuous quality improvement of a global statistical system.