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Steven Vale UNECE steven.vale@unece

Standards-based Modernisation An update on the work of the High-level Group for the Modernisation of Statistical Production and Services. Steven Vale UNECE steven.vale@unece.org. Contents. Modernisation and the High-level Group Standards Projects Big Data.

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Steven Vale UNECE steven.vale@unece

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  1. Standards-based ModernisationAn update on the work of the High-level Group for the Modernisation of Statistical Production and Services Steven Vale UNECE steven.vale@unece.org

  2. Contents • Modernisation and the High-level Group • 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. Making sense of the Data Deluge

  6. Statistics fights back! • High Level Group for the Modernisation of Statistical Production and Services (HLG) • Created by the Conference of European Statisticians in 2010 • 10 heads of national and international statistical organisations

  7. 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

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

  9. 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?

  10. The answer ...Standards-based Modernisaton

  11. The GSBPM

  12. Why a Generic StatisticalBusiness Process Model? 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 Business register processes mapped to GSBPM (see 2011 paper)

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

  14. 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

  15. Projects for 2013

  16. Reviewing GSBPM and GSIM • Gather feedback from users of the models through public discussion forums • New versions of GSBPM and GSIM by the end of 2013 • But ... any changes will need a strong business case and wide agreement • Continuity is important • Major change is unlikely

  17. Mapping GSIM to DDI and SDMX • Detailed mapping between the information objects in the GSIM, with those in the information models of DDI and SDMX. • Aims to identify any issues affecting the coherence of these standards • Propose solutions where possible

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

  19. How does architecture help? • Many statistical organisations are modernising and transforming using Enterprise Architecture • Enterprise Architecture shows what the business needs are, where the organisation wants to be and aligns the IT strategy to this • It can help to remove silos and improve collaboration across an organisation

  20. Applying Enterprise Architecture Disseminate

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

  22. ... we get this ...

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

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

  25. ... sharing is easier!

  26. Big Data: A newproject for 2014?

  27. HLG and Big Data • Paper: What does Big Data mean for official statistics? • Project proposal from global task team: • Objectives: Develop and test methods and tools • Scope: Big Data in modernisation of official statistics • Work package 1: Issues and methodology • Work package 2: Shared computing environment (“sandbox”), practical application of methods and tools • Work package 3: Training and dissemination

  28. Big Data and Business Registers • Many un-answered questions: • Data on-site or in the cloud? • How to link Big Data with registers / surveys? • How to define units? • Classifications – on the fly? • How to ensure sufficient confidentiality? • Continuity?

  29. 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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