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GSBPM and GSIM in Statistics Norway Prepared by Rune Gløersen and Jenny Linnerud

Discover how Statistics Norway uses GSIM & GSBPM for improving statistical processes, connecting systems, and enhancing data sharing. Learn about the RAIRD initiative and the GSIM implementation for a standardized statistical metadata framework.

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GSBPM and GSIM in Statistics Norway Prepared by Rune Gløersen and Jenny Linnerud

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  1. GSBPM and GSIM in Statistics Norway Prepared by Rune Gløersen and Jenny Linnerud MSIS, Dublin14-16 April 2014

  2. The GSBPM

  3. Why do we need the GSBPM? To define and describe statistical processes in a coherent way To compare and benchmark processes within and between organisations To make better decisions on development of production systems To optimize organisation and allocation of resources

  4. Statistics NorwaysBusiness process model

  5. Specify needs 1 Develop and design 2 Build 3 Collect 4 Process 5 Analyse 6 Disseminate 7 Prepare data for dissemination database 7.1 Determine need for information 1.1 Outputs 2.1 Build and enhance process components 3.1 Establish frame and registers, select sample 4.1 Classify and code 5.1 Acquire domain intelligence 6.1 Integrate production system with other systems 3.2 Set up collection 4.2 Produce product 7.2 Consult and confirm need 1.2 Frame, register and sample methodology 2.2 Micro-edit 5.2 Produce statistics 6.2 Establish output objectives 1.3 Data collection methodology 2.3 Test production system 3.3 Run collection 4.3 Macro-control 5.3 Quality assure statistics 6.3 Release and promote product 7.3 Check dataavailability 1.4 Process and analysis methodology 2.4 Finalise production system 3.4 Finalise collection 4.4 Impute for partial non-response 5.4 Interpret and explain statistics 6.4 Manage customer queries 7.4 Calculate weights and derive new variables 5.5 Prepare business case 1.5 Production system 2.5 Prepare statistics for dissemination 6.5 Finalise content 6.6 Statistics NorwaysBusiness process model

  6. Statistics NorwaysBusiness process model • Was mapped against GSBPM 4 in the CORA project • Slightly different on detailed level within Build • Some processes on detailed level placed differently within Process and Analyse • Was different with regards to archive as an overarching process, which has been better aligned with GSBPM 5

  7. Complete documentation on our Intranet

  8. GSBPM in Statistics NorwayStreamlining Statistics Production

  9. Categorising systems SFU FDM Norsamu (Trekkbas) Telefinn SMIE SERES Presys • ssb.no • statistic register • Google analytics • Stat. Bank SIV/SIL Blaise Altinn (Idun, Kostra) Java SAS Oracle Fame ISEE ISEE Driller Verify SELEKT X12-Arima Tau-Argus Mu-Argus SAS Insight Service Manager (Helpdesk, OTRS) LDA-app Stat. population registers: - National register - The Central Coordinating Register for Legal Entities - GAB – Landed property, Address, Dwelling (map) ProduktregisterMetadataportals:- Vardok- Datadok- Stabas- ssb.no (About statistics) “Projectplanning”:- JiraDocument centers:Confluence (Trac, Wiki)Windows-server MS OfficeSmartDrawArcGISWebsakSPSSAdobe

  10. Summary • planning new statistics • prioritizing new projects (portfolio management) • improve existing work processes in statistical production • reducing portfolio of IT-systems • reducing risk • making a more complete business architecture • easier training and integration of staff.

  11. Introduction to GSIM

  12. We need consistent information • Modernisation of statistics requires: • reuse and sharing of methods, components, processes and data repositories • definition of a shared “plug-and-play” modular component architecture • The Generic Statistical Business Process Model (GSBPM) will help determine which components are required. • GSIM will help to specify the interfaces.

  13. GSIM and GSBPM • GSIM describes the information objects and flows within the statistical business process.

  14. GSIM in Statistics Norway - Vision META DATA

  15. GSIM in Statistics Norway - Vision GSIM should lead to: • A foundation for standardisedstatistical metadata use throughout systems • A standardised framework for consistent and coherent design of statistical production • Increased sharing of system components

  16. Remote Access Infrastructureto Register Data (RAIRD) Statistics Norway and the Norwegian Social Science Data Services (NSD) aim to establish a national research infrastructure providing easy access to large amounts of registerbased statistical data managing statistical confidentiality protecting the integrity of the data subjects. The work is funded by the Research Council of Norway. See: www.raird.no

  17. RAIRD Information Model (RIM) Basedon GSIM v1.1 • Design principles • Information objects New informationobjects for users (producers, administrators and researchers) Less information objects for details of the official production of statistics RAIRD continues out 2017

  18. Research productionprocessto be supported by RIM?

  19. RIM Data DescriptionsGSIM information objects • Input Unit Data and Metadata/Event History Data Resource • Analysis Data Sets • Disclosure Control • Final Product • Themes and Subject Fields • Classifications, Concepts, Variables, etc.

  20. GSIM Glossaryblue – not in RIM, yellow – in RIM

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