Contents

1. The Global ContextInternational developmentsin the organisation ofstatistical productionSteven Vale, UNECE

2. Contents • Streamlining and Industrialisation • Introducing the HLG-BAS • The HLG-BAS vision • Other international initiatives • What does it mean in practice?

3. Streamlining is: • Improving efficiency • Reducing costs • More timely data • Increased flexibility to produce new outputs • A challenge faced by all statistical organisations

4. Industrialisation is: • Common processes • Common tools • Common methodologies • Recognising that all statistics are produced in a similar way, rather than each domain being “special” • A consequence of streamlining

5. Many international groups and projects are talking about streamlining and industrialising statistics

7. Coordination – HLG-BAS • High-Level Group for Strategic Directions in Business Architecture in Statistics • UNECE group, created by the Conference of European Statisticians in 2010 • Mission: • To oversee and guide discussions on developments in the business architecture of the statistical production process, including methodological and information technology aspects

8. HLG-BAS Members • Netherlands - Gosse van der Veen (Chairman) • Australia - Brian Pink • Italy - Enrico Giovannini • Slovenia - Irena Krizman • United States - Katherine Wallman • Eurostat - Walter Radermacher • OECD – Martine Durand • UNECE - Lidia Bratanova • Observers METIS – Alice Born (Canada) MSIS – Rune Gløersen (Norway) SAB – Marton Vucsan (Netherlands)

9. Inventory of Groups

10. HLG-BAS Strategic Vision • Endorsed by the Conference of European Statisticians on 14 June • Perspective • Challenges • Vision • The following slides are based on that presentation

11. The internet has 1800 exabytes of data in 2011 exa = 10^18 Some perspective:

12. We live in exponential times 50,000 exabytes by 2020 27 fold growth in the next 9 years

13. Are these data interesting? • Probably 99.9% are videos, photos, audio files, text messages and other nonsense • But that still leaves1,800,000,000,000,000,000bytes of potentially relevant data

14. Private sector competitors? • Google: • Data labs • Public Data Explorer • Real-time price indices • First point of reference for the “data generation” • Facebook, store cards, credit agencies, ... • What if they link their data?

15. High Level Group Vision: We have to re-invent our products and processes and adapt to a changed world

16. The Challenges are too big for statistical organisations to tackle on their own.We need to work together

17. Other international initiatives • “Industry” standards • Generic Statistical Business Process Model • Generic Statistical Information Model • Statistical Data and Metadata eXchange • Data Documentation Initiative

18. Other international initiatives • New collaborative networks • “Statistical Network” • Sharing Advisory Board • ESSNet projects • SDMX / DDI Dialogue

19. What does this mean in practice? • Collaboration • Coordination • Communication • We need to review our systems and processes – are they right for the 21st century?

20. Changing the focus • From local to corporate optimum • Standard processes within an organisation • Not always the best choice for individual statistical domains, but more efficient at the level of the organisation • Requires strategic decisions and clear management commitment • From corporate to global optimum?

21. Changing roles for NSOs? • Data integration • Quality assurance • More focus on analysis and interpretation • Partnerships for dissemination • Changing staff and cost profiles • Changing organisational culture

22. Next steps • From vision to strategy • Autumn 2011: workshop for representatives of the groups in the inventory to improve coordination and determine how they can contribute to implementing the vision

23. Questions?steven.vale@unece.orgwww1.unece.org/stat/platform/display/hlgbasQuestions?steven.vale@unece.orgwww1.unece.org/stat/platform/display/hlgbas