Metadata Standards and Official Statistics

1. Metadata Standards and Official Statistics Presentation by: Jack Gager j.gager@metadatatechnology.com Metadata Technology, North America

2. Overview • Why Standardize • Standards Overview • Our Experiences • The Future of the Standards • Summary

3. Why Standardize? • Strategy to implement the vision of HLG-BAS1 laid out the following key points: • Organizations producing statistics are faced with a shift in the way data is produced and utilized • The abundance of data poses both opportunities and challenges • The strategy focused on 5 themes: • Organizations producing statistics need to improve processes • Data and services need to be more economic to produce • Vast amounts of available data should be utilized • Environments which promote reuse of data, processes and methods should be created; including those which allow users to perform their own analysis • Strong leadership is required for this strategy 1. http://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/2012/10_-_HLGBAS_Strategy.pdf

4. HLG-BAS Strategy • Building knowledge based on international standards • GSBPM for business concepts • GSIM for information concepts • Outline a plug-and-play architecture

5. DDI • Even before DDI, survey metadata was being captured in machine actionable ways • DDI 1.0 was finalized around 2000 • Archival • Single survey view with variable centric metadata • DDI 2.0 was introduced in 2003 • Aggregate data • Geographical information • Lifecycle approach accepted 2004 • Move from single survey view • Promote reuse and harmonization of metadata • DDI 3.0 officially released 2008

6. DDI Lifecycle

7. SDMX • Prior to SDMX there was GESMES/TS • SDMX 1.0 was released in 2004 • Data • Structure • Query • Became ISO 17369 in 2005 • SDMX 2.0 released in 2005 • Registry • Reference Metadata • SDMX 2.1 released in 2011 • Enhanced web services

8. GSBPM SDMX DDI DDI

9. GSIM • Information objects for the GSBPM • Goals: • Provide common terminology and definitions for developing metadata systems and information management frameworks • Reference model for implementing on a consistent basis when defining information to drive production processes • SDMX and DDI Lifecycle as starting points • Leveraged where appropriate as representations of GSIM

10. Our Experience • Initially, standards were seen as competing • That has been largely clarified • Aided by ongoing dialogues between SDMX/DDI • Standards are often viewed a magic bullets • Rarely is this the case • Extensions are often necessary • The standards are not great at actually managing metadata • Standards used only for their basic purposes probably hinder more than they help • Standards are only as good as the quality of the metadata behind them

11. Future of the Standards • GSBPM and GSIM are steps in the right direction • DDI and SDMX should take leads from GSIM • Base their models in GSIM so the standards are better harmonized • Follow GSIMs lead in not prescribing metadata management, but rather only the information necessary for the purpose of the standard • Lower barriers for entry and adoption

12. Summary • GSBPM and GSIM form a useful framework for industrializing statistics production • DDI and SDMX are necessary to realize the overall visions of HLG-BAS • This bigger picture view should foster a continued evolution in the standards • As with any framework or standard, the results are only as good as the inputs; sound metadata management is key