1. United Nations Economic Commission for Europe Statistical Division Standards-based Modernisation Steven Vale UNECE steven.vale@unece.org

2. Contents  The data deluge  Standards  Projects  Big Data

3. Why is modernisation important?

4. In the last 2 years more information was created than in the whole of the rest of human history!

5. The Challenges 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

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

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

8. The answer... Standards-based Modernisaton

9. The GSBPM

10. 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 production systems and organisation of resources

11. The GSBPM is used by more than 50 statistical organisations worldwide

13. KSBPM – Republic of Korea

14. Beyond statistics: Data archives Generic Longitudinal Business Process Model

15. Framework for combining standards?

16. Something missing? We need a layer between GSBPM and the data transfer standards!

18. A global project Overseen by the HLG

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

20. So what is GSIM?  A reference framework of information objects: Definitions Attributes Relationships  GSIM aligns with relevant standards such as DDI and SDMX  Version 1.0 released in December 2012 GSIM gives us standard terminology

23. GSIM documentation Different layers of detail for different audiences!

25. HLG Projects for 2013

27. Aim: “to support the enhancement and implementation of the standards needed for the modernisation of statistical production and services”

28. GSIM Implementation Group  Providing support for a community of GSIM “early adopters”  A forum for exchanging ideas and experiences  12 organisations represented  Feedback on how to improve GSIM

29. Mapping GSIM to DDI and SDMX  Detailed mapping between GSIM and the information models of DDI and SDMX.  Evaluate coherence / differences GSIM / SDMX v2.1 GSIM / DDI v3.2

30. Reviewing GSBPM and GSIM  Gathering feedback (until 30 September!)  Expert groups reviewing and refining  New versions of GSBPM and GSIM by the end of 2013  But... Continuity is important Major change is unlikely

32. Historically, statistical organisations have produced specialised business processes, methods and IT systems for each survey / output

33. Applying Enterprise Architecture Disseminate

34. ... but if each statistical organisation works by themselves...

35. ... we get this...

36. .. which makes it hard to share and reuse!

37. … but if statistical organisations work together to define a common statistical production architecture...

38. ... sharing is easier!

39. Layers of Architecture = = = Business Layer Information Layer Implementation Layer

40. Proof of Concept  Currently being developed to: Demonstrate the process of working together and the advantages in cooperation Demonstrate business viability to senior management Prove the value of the architecture - Here is something that we could not do before

41. Big Data: A new project for 2014?

42.  Paper: “What does Big Data mean for official statistics?”  Project proposal from global task team : Work package 1: Strategy and methodology Work package 2: Shared computing environment (“sandbox”), practical application of methods and tools Work package 3: Training and dissemination HLG and Big Data

43.  Is a completely new approach needed? Big Data, but small processes Why move the data? Fundamental paradigm shift: Process data at source (or in the cloud) rather than in house? Just transfer aggregates back to statistical organisations? More research needed! Collecting Big Data?

44. Informal Workshop Friday afternoon All welcome

45. Get involved! Anyone is welcome to contribute! More Information HLG Wiki: http://www1.unece.org/stat/platform/display/hlgbas LinkedIn group “Business architecture in statistics”