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Standards-based Modernisation

Standards-based Modernisation. Steven Vale UNECE steven.vale@unece.org. Contents. The data deluge Standards Projects Big Data. Why is modernisation important?. In the last 2 years more information was created than in the whole of the rest of human history!. The Challenges.

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Standards-based Modernisation

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  1. 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 Riding the big data wave New competitors & changing expectations Competition for skilled resources Reducing budget Rapid changes in the environment

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

  7. Using common standards, statistics can be producedmore efficiently No domain is special! Do new methods and toolssupport 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

  12. KSBPM – Republic of Korea

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

  14. Framework for combining standards?

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

  16. A global projectOverseen by the HLG

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

  18. 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

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

  20. HLG Projects for 2013

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

  22. 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

  23. 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

  24. 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

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

  26. Applying Enterprise Architecture Disseminate

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

  28. ... we get this ...

  29. .. which makes it hard toshare and reuse!

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

  31. ... sharing is easier!

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

  33. 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 thearchitecture - Here issomething that we couldnot do before

  34. Big Data: A newproject for 2014?

  35. HLG and Big Data • 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

  36. Collecting Big Data? • 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!

  37. Informal Workshop Friday afternoon All welcome

  38. 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”

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