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Improving interoperability in Statistics Some considerations on the impact of SDMX

Improving interoperability in Statistics Some considerations on the impact of SDMX. 59th Plenary of the CES Geneva, 14 June 2011 Rune Gløersen IT Director Statistics Norway. Contents. The characteristics of processes and data at NSIs Applicable standards for various business processes

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Improving interoperability in Statistics Some considerations on the impact of SDMX

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  1. Improving interoperability in StatisticsSome considerations on the impact of SDMX 59th Plenary of the CES Geneva, 14 June 2011 Rune GløersenIT DirectorStatistics Norway

  2. Contents • The characteristics of processes and data at NSIs • Applicable standards for various business processes • The preconditions for increased interoperability • A top-down approach to further standardisation • SDMX as part of the industrialisation of statistics

  3. GSBPM – leaving stove pipes

  4. Data archiving

  5. Process stages and data archiving Quality Management/Metadata Management Specifyneeds Design Build Evaluate Data archiving spans the 4 main business processes,and comprises 4 steady states of the data life cycle

  6. Dissemination of aggregated statistics using SDMX SDMXConversion SDMX Common Architecture Can (somewhat) easily be streamlined

  7. Dissemination of any statistical data using SDMX SDMXConv SDMX Common Architecture Requires a paramount strategy

  8. Adopting standards Quality Management/Metadata Management Specifyneeds Design Build Evaluate DDI SDMX ?

  9. The diversity of users, needs and data flows Common high level models, vocabulary etc Questionnaires Data transfers Public Registers Domain specific Research

  10. Challenges • The high-level decision to use SDMX for the exchange of statistical data; how should this be envisaged? • The role of the standardisation experts, the IT experts, the subject domain experts and the top management • SDMX implementation is strategic, but is regarded as technical • The importance and impact of the Information Model and the Metadata Common Vocabulary • Choosing standards; DDI, SDMX, DSPL etc. • No standard is likely to fit all purposes. • Will a common high-level information model contribute to easier implementation of standards? • Can a high-level information model bridge different standards? • Provide well defined interfaces, or develop software to hide the challenges? • Common requirements for the quality of software

  11. Improved interoperability Some trends Organisational interoperability Semantical interoperability Technological interoperability

  12. Maturity growth in e-Government OrganisationalInteroperability Legislation,Whatever AligningStrategies Joining ValueCreation Common information models, process models and service catalogues, shared development costs SharingKnowledge Share best practises, metadata specifications,Set up standards for technical systems and dataexchange Aligning WorkProcesses Bilateral data exchange, semi automated,Technical specifications and standards SemanticalInteroperability Source: www.semicolon.noAnalytical Framework for e-Government Interoperability

  13. Industrializing Statistics Statistical Concepts Information Concepts conceptual GSBPM GSIM Common Generic Industrial Statistics Methods Technology practical Statistical HowTo Production HowTo De-coupling content and technical standardisation

  14. Conclusions • Standardisation is not a goal in itself; any standardisation effort must be based on well defined business cases. Success requires a top-down, management driven approach. • The adoption of SDMX must be aligned with the on-going process oriented developments among NSI’s. • Utilize the benefits of SDMX for the exchange of aggregated data, improve the international harmonisation of requirements, and simplify implementation whenever possible. • Agreeing on common high-level models, creates an opportunity for flexible, targeted and effective solutions on the detailed level, still harmonised within a standardised framework • The statistical community should act as an industry, not only as individuals, in order to increase commercial attention to the industry of statistics

  15. Actions ? • (Continue to) set up a common reference framework comprising the objectives of harmonisation/standardisation • Appreciate clustered initiatives, but require precise description on the contributions to the overall objectives • Better prioritisation among projects; it is unlikely that we can achieve all goals at once • Improve governance and coordination • Let the drivers drive • Evaluate • Decide where to provide for best practises, architectures, standards and tools/shared software components • Improve the strategy on how to coordinate process developments with subject matter/domain specific developments • Provide for innovation

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