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Standards and Statistical Production Architectures

Standards and Statistical Production Architectures. Steven Vale UNECE steven.vale@unece.org. Background. High-Level Group for the Modernisation of Statistical Production and Services Created by the Conference of European Statisticians in 2010

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Standards and Statistical Production Architectures

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  1. Standards and Statistical Production Architectures Steven Vale UNECE steven.vale@unece.org

  2. Background • High-Level Group for the Modernisation of Statistical Production and Services • Created by the Conference of European Statisticians in 2010 • 10 heads of national and international statistical organisations

  3. The Challenges Riding the big data wave Increasing cost & difficulty of acquiring data New competitors & changing expectations Competition for skilled resources Rapid changes in the environment Reducing budget

  4. These challenges are too big for statistical organisations to tackle on their ownWe need to work together

  5. Using common standards, statistics can be producedin a more efficient way No domain is special! Do new methods and toolssupport this vision, or do they reinforce a stove-pipe mentality?

  6. Standards-based modernization StatisticalConcepts InformationConcepts GSIM GSBPM Common generic statisticsproduction Technology Methods StatisticalHowTo ProductionHowTo

  7. Projects • 2012 • 2013

  8. GSBPM – Standard Processes

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

  10. So what is GSIM? • A reference framework of information objects: • Definitions • Attributes • Relationships • GSIM aligns with relevant standards such as DDI and SDMX GSIM gives us standard terminology

  11. “support the enhancement and implementation of the standards needed for the modernisation of statistical production and services”

  12. Components • Implementing GSIM • Mapping GSIM to DDI and SDMX • Reviewing / revising GSBPM and GSIM • Links to geo-spatial standards • New task team to assess the role of geo-spatial standards in the modernisation of official statistics • Training and advocacy

  13. Aims Increased: • interoperability through sharing processes and components • ability to find real collaboration opportunities • ability to make international decisions and investments • sharing of architectural design, knowledge and practices

  14. Historically statistical organisations have developed specialised processes and systems

  15. How does architecture help? • Many statistical organisations are modernising using Enterprise Architecture • It can help to remove silos and improve collaboration across an organisation.

  16. EA helps you get to this Disseminate

  17. …but if each statistical organisation works in isolation…..

  18. NSI 2 NSI 1 Survey A Survey A Survey B Survey B Collect Process Analyse Disseminate ? ?

  19. …but if statistical organisations work together An industry architecture will make it easier to standardise and combine the components of statistical production, regardless of where they are built

  20. NSI 2 NSI 1 Survey A Survey A Survey B Survey B Collect Process Analyse Disseminate ? ?

  21. Lego pieces could be: Wrapped legacy/existing Brand new

  22. An industry architecture is: “a set of agreed common principles and standards designed to promote interoperability in an industry” “an architecture template for statistical production” “a common vocabulary to discuss implementations”

  23. Proof of Concept • Demonstrate the process of working together • Demonstrate business viability to senior management • Prove the value of the architecture

  24. Catalogues

  25. Do you want to get involved? • Projects • Sprints / virtual task teams / reviews • Other activities / project proposals? Let us know support.stat@unece.org

  26. More information HLG Wiki www1.unece.org/stat/platform/display/hlgbas LinkedGroup Business Architecture in Statistics

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