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Contents

The Generic Statistical Business Process Model and its Implementation in Practice Steven Vale, UNECE. Contents. Introducing the GSBPM Links to other standards Further development of the GSBPM Implementation in practice. Statistical Concepts. Information Concepts. conceptual. GSIM. GSBPM.

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  1. The Generic StatisticalBusiness Process Modeland its Implementationin Practice Steven Vale, UNECE

  2. Contents • Introducing the GSBPM • Links to other standards • Further development of the GSBPM • Implementation in practice

  3. StatisticalConcepts InformationConcepts conceptual GSIM GSBPM Common Generic Industrial Statistics Methods Technology practical StatisticalHowTo ProductionHowTo

  4. GSBPM – The Background • Statistical production has traditionally been organised by topic, e.g. transport, trade, … • Financial pressures are encouraging new ways of thinking • Some statistical organisations are moving towards a process-based approach • Others are considering a matrix approach

  5. Terminology • Defining and modelling processes in statistical organisations started at least 10 years ago • “Statistical value chain” • “Survey life-cycle” • “Statistical process cycle” • “Business process model”

  6. Terminology • Defining and mapping business processes in statistical organisations started at least 10 years ago • “Statistical value chain” X • “Survey life-cycle” X • “Statistical process cycle” X • “Business process model” X Generic Statistical BusinessProcess Model

  7. Why do we need a model? To define and describe statistical processes in a coherent way To standardize process terminology To compare and benchmark processes within and between organisations To identify synergies between processes To inform decisions on systems architectures and organisation of resources

  8. Developing the GSBPM Developed by the UNECE Steering Group on Statistical Metadata (METIS) Based on the business process model developed by Statistics New Zealand Three rounds of comments made the terminology and descriptions more generic Adopted in April 2009

  9. Applicability • All activities undertaken by producers of official statistics which result in data outputs • National and international statistical organisations • Independent of data source, can be used for: • Surveys / censuses • Administrative sources / register-based statistics • Mixed sources

  10. Structure of the GSBPM Process Phases Sub-processes (Descriptions)

  11. Structure of the GSBPM (2) • National implementations may need additional levels • Over-arching processes • Quality management • Metadata management • Statistical framework management • Statistical programme management • ........ (8 more – see paper)

  12. Key features Not a linear model Sub-processes do not have to be followed in a strict order It is a matrix, through which there are many possible paths, including iterative loops within and between phases Some iterations of a regular process may skip certain sub-processes

  13. Links to other standards • SDMX standards refer to business processes, but do not have a model • DDI has the Combined Life Cycle Model

  14. Combining standards?

  15. Functionality stretched too far?

  16. Further development of the GSBPM • UNECE Task Force • No change to model (for at least 2 years) • 5 themes: • National Implementations of the GSBPM • Communication resources • Metadata flows within the GSBPM • GSBPM and process quality management • Other groups using the GSBPM as a framework for their activities

  17. Workshop on Statistical Metadata • Theme: Implementing the GSBPM and combining metadata standards • Where: Geneva • When: 5-7 October 2011 • More information: UNECE website -www.unece.org/stats/documents/2011.10.metis.htm • All welcome!

  18. Implementation • 30+ countries have adopted the GSBPM or national versions as a framework to describe statistical production • Also used for: • Quality management • Cost allocation • Time recording • “Classification” of IT systems

  19. Statistics Sweden

  20. Czech Republic

  21. Governance Statistical policy management Statistical coordination Quality management Statistics-based policy management Support for population-related information Support for the production quality Quality check in each production step Management of statistical production Support for sampling design Sharing of statistical information Production process pool Support for production Sharing of statistical business knowledge Support for enumeration districts and maps Planning Data collection Dissemination Metadata use Design Data processing Archive Help desk Implementation Analysis Evaluation Republic of Korea - KSBPM

  22. Questions and Comments? steven.vale@unece.org www.unece.org/stats/gsbpm

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