1. United Nations Economic Commission for Europe Statistical Division International Collaboration to Modernise Official Statistics Steven Vale UNECE steven.vale@unece.org

2. Introducing UNECE Statistics

5. UNECE Statistics: Priorities  Population censuses, migration, Millennium Development Goals  Globalisation, National Accounts, employment, business registers  Sustainable development, environmental accounts, climate change  Modernisation

6. Introducing the HLG  High-level Group for the Modernisation of Statistical Production and Services  Created by the Conference of European Statisticians in 2010  Vision and strategy endorsed by CES in 2011/2012

7. Who are the HLG members?  Pádraig Dalton (Ireland) - Chairman  Trevor Sutton (Australia)  Wayne Smith (Canada)  Emanuele Baldacci (Italy)  Bert Kroese (Netherlands)  Park, Hyungsoo (Republic of Korea)  Genovefa Ružić (Slovenia)  Walter Radermacher (Eurostat)  Martine Durand (OECD)  Lidia Bratanova (UNECE)

8. What does the HLG do?  Oversees activities that support modernisation of statistical organisations  Stimulates development of global standards and international collaboration activities  “Within the official statistics community... take a leadership and coordination role”

9. Why is the HLG needed? Before the HLGNow Many expert groupsClear vision Little coordinationAgreed priorities No overall strategyStrategic leadership Limited impactReal progress

10. The Challenges

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

12. 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?

13. HLG 30+ Expert Groups Projects

14. 2012

16. What is GSIM?  A reference framework of information objects  It sets out definitions, attributes and relationships of information objects  It aligns with relevant standards such as DDI and SDMX

17. A step back: The GSBPM

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

22. Clickable GSIM

23. 2013

25. Standards-based Modernisaton 135 28% 43% 34,600

26. Simplified - 150 110 information objects Incorporates revised Neuchâtel Model of classification terminology Better aligned with other standards, particularly DDI Outcomes

27. GSBPM v5.0

28. Main Changes  Phase 8 (Archive) removed Archiving can happen at any stage in the statistical production process  New sub-process "Build or enhance dissemination components"  Clearer distinction between detection and treatment of errors  Sub-processes re-named to improve clarity  Descriptions of sub-processes improved  Terminology is less survey-centric

29. Mappings Fundamental Principles of Official Statistics

31. Frameworks and Standards Project

33. Problem statement: Specialised business processes, methods and IT systems for each survey / output

34. Applying Enterprise Architecture Disseminate

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

36. ... we get this...

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

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

39. ... sharing is easier!

40. CSPA development project ArchitectureProof of Concept

41. The Proof of Concept  5 countries built CSPA services  3 countries implemented them

42. Project Outcomes The CSPA approach works It promises increased: sharing interoperability collaboration opportunities Some licensing issues!

43. United Kingdom 5 Build teams 42 individuals 2 Sprints 3 Assemble teams 1 Working Group FAO

44. 2014

45. Implementation of the CSPA

46. Services built 1. Seasonal adjustment – France, Australia, New Zealand 2. Confidentiality on the fly – Canada, Australia 3. SVG generator – OECD 4. SDMX transform – OECD 5. Sample selection – Netherlands 6. Linear error localisation – Netherlands 7. Linear rule checking – Netherlands 8. Error correction – Italy

47.  Architecture Working Group: Australia, Austria, Canada, France, Italy, Mexico, Netherlands, New Zealand, Turkey, Eurostat  Catalogue team: Australia, Canada, Italy, Hungary, New Zealand, Romania, Turkey, Eurostat

48. Specify Needs DesignBuildAnalyseDisseminateEvaluateProcessCollectDisseminate Sharing Infrastructure across Statistical Organisations By sharing infrastructure development, we can: Reduce costs of development Adopt new methods quickly Increase comparability of statistics

49. Big Data

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

52. Physical sprint Consultation Virtual sprint Task teamsGuidelines

53. Priority Areas  Partnerships – Guidelines  Privacy – Guidelines  Quality – Guidelines  Skills – Survey  IT / methodological issues - Sandbox

54. Sandbox  Irish Centre for High-end Computing is hosting a Big Data ‘sandbox’ containing data and tools for international experiments “Play is the highest form of research” – Einstein

55. Sandbox: Aims  Test feasibility of remote access and processing: - Could this approach be used in practice?  Test whether existing statistical standards / models / methods can be applied to Big Data  Determine which Big Data software tools are most useful for statistical organisations  Learn about the potential uses, advantages and disadvantages of Big Data – “learning by doing”.  Build an international collaboration community on the use of Big Data

56. 2015

57. Key priorities agreed last month  CSPA Develop many more services Support implementation by effective governance and appropriate technical standards  Big Data Continue the sandbox Produce joint international outputs Develop skills and capacity to work with new sources to support the “Data Revolution”

58. How to work together for minimum cost and maximum benefit?

59. The “Sprint” method

60. Virtual meetings  We use Webex – others are available  Flexibility  Free for participants Join meetings from office, home, airport etc.  Screen sharing  Virtual whiteboard

61. Wikis  Central repository of information  Latest versions and comments in one place  Good for joint drafting of papers  Access anywhere with a web connection  Can be public or restricted

62. Governance

63. HLG Activities – Engagement Map

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