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SDMX – an oecd perspective

SDMX – an oecd perspective. Paul Schreyer OECD CCSA Special Session, September 2014 Rome. Outline. Why we think SDMX is important Key OECD Activities What Have W e learned : Lessons and Challenges Looking Ahead. Why we think SDMX is important.

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SDMX – an oecd perspective

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  1. SDMX – an oecd perspective Paul Schreyer OECD CCSA Special Session, September 2014 Rome

  2. Outline Whywethink SDMX is important Key OECD Activities What Have Welearned: Lessons and Challenges LookingAhead

  3. Whywethink SDMX is important

  4. Whywethink SDMX is important Standardisation of data transmission Speed Quality: accuracy, readability Consistencybetween international sources

  5. Example: data differencesbetween international sources Example: Government Deficit 2010 Differences between highest and lowest result, in %-points of GDP

  6. Whywethink SDMX isimportant (2) • The Processitselfisuseful: • Detailed discussions withotherIOs and with countries concerning: • Data requirements • Templates • Data sharing

  7. Whywethink SDMX is important (3) Example Mexico SDMX isenabler of co-ordination of data production and dissemination at the national level

  8. Key OECD Activities

  9. 1. Definition and maintenance of Global Data Structure Definitions (DSDs) • Already established: • National Accounts (SNA 2008) • Balance of Payments • FDI (OECD is maintenance agency) • Forthcoming: • Education, R&D, Merchandise Trade Statistics • Ongoing: Management of Global Registry in 2013-2014 with Eurostat • Administrative duties for users • Maintaining the registry content • Coordination with SDMX working groups

  10. 2a. Implementation: OECD Short Term Economic Statistics collection • DSD for OECD-specific data transmission of short-term indicators (prices, real indicators, etc.) • 8 countries providing STES SDMX data, 9 countries ongoing implementation work • Goals: • For disseminators: • An open format specification with which to transmit data • Avoids having to package and push the data to OECD • For OECD: • Timeliness • One format and structure enables automated processing and checking of collected data • Ease of validation of data structure (correct coding, file format)

  11. 2b. Implementation: Task Force on International Data Co-operation • Pilot exercise under the IAG (see ECB presentation) • testing SDMX data exchanges (push mode) • decreasing respondent burden of national data providers • Minimising data differences between IOs • First phase, since 2013: Key National Accounts and population data (annual and quarterly) • Second phase, 2015: institutional sector accounts • The first exchanges: • proved technical feasibility • revealed problems in coding data available according to pre 2008 SNA • helped clarifying formulation of SDMX messages • wereextremelyuseful in understandingotherIOs’ data collection

  12. 2c. Implementation: OECD Preparations for regular data collection and dissemination • Implement SDMX IT Infrastructure for Global DSDs, both collection and dissemination • Focus on existing tools, reusability, generic tools • Build SDMX capacity for IT and non-IT staff • Align existing questionnaires with SDMX coding

  13. Announcement: SDMX Expert Group meeting  in Korea • Co-organised with the KoStat, and SDMX Sponsor organisations • Seoul, 27-30 October 2014 • Focus on • Experience with implementing Global DSDs • SDMX Working Groups new guidelines and improvements to standards • SDMX Technical solutions • 2 days training • Using SDMX Reference Infrastructure • Implementing Global DSDs • Enquiries: Gyorgy.Gyomai@OECD.org

  14. Lessonslearned

  15. What has OECD learned after over 10 years of SDMX? SDMX adoption is not simply a technological challenge Methodological and subject-matter knowledge and resources are key ‘Business process’ led by statisticians SDMX knowledge good in Ios Knowledge base in NSOs and central banks growing but still unevenly between countries The early focus was on developing the SDMX technical standard. Now the main focus is how to ease adoption and provide guidelines for common issues and use cases

  16. Key Challenges • Motivating statistical agencies to adopt SDMX • Make business case • IO support to implement SDMX • Motivating broader group of IOs to broaden subject matters (CCSA) • Dealing with cost of adoption • Legacy systems and standards must be maintained until all providers have phased out non-SDMX dissemination • Building SDMX knowledge in NSOs • Provide shared tools to ease the move from legacy systems (such as the SDMX Convertor) • Governance structure mayneed to evolvewithtake-up of SDMX by more countries and IOs

  17. WayForward

  18. The way forward – general considerations Near future should be consolidation phase Demonstratereal-life workability Involving more non-European countries and otherCCSAmembers Addresses practical implementation problems Provide practical guidance; revisit and consolidate existing guidelines where required Clearer prioritisation of tasks developments Seekcloserco-ordination with related initiatives, in particular High Level Group on Modernisation of Statistics

  19. The way forward – specific issues • Complete global DSDs for major statistical domains • e.g. currently the SNA DSD can only code properly SNA 2008 data, and activity breakdowns with ISIC rev 4; • FurtherintegrationwithStatistical Information Collaboration Community (SIS-CC) • OECD.Statfully SDMX compatible • Use SDMX for PGI

  20. Thankyou!

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