1. United Nations Economic Commission for Europe Statistical Division Standards and Statistical Production Architectures Steven Vale UNECE steven.vale@unece.org

2.  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 Background

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

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

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

6. Common generic statistics production GSBPM GSIM MethodsTechnology Statistical Concepts Information Concepts Statistical HowTo Production HowTo Standards-based modernization

7. Projects  2012  2013

9. GSBPM – Standard Processes

10. GSIM and GSBPM  GSIM describes the information objects and flows within the statistical business process.

11. 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

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

14. 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

16. 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

17. Historically statistical organisations have developed specialised processes and systems

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

19. EA helps you get to this Disseminate

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

21. NSI 1 Collect Process AnalyseDisseminate Survey ASurvey B NSI 2 Survey ASurvey B ? ?

22. …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

23. NSI 1 Collect Process AnalyseDisseminate Survey ASurvey B NSI 2 Survey ASurvey B ? ?

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

25. 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”

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

29. Catalogues

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

31. More information HLG Wiki www1.unece.org/stat/platform/display/hlgbas Linked Group Business Architecture in Statistics