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Statistics Norway Information Architecture – some challenges

Statistics Norway Information Architecture – some challenges. ODaF meeting, Colchester 14 -15 April 2008 Rune Gløersen Director Department for IT and Data Collection Statistics Norway. Initiatives dependent on enhancement of information architecture. E-Government Need for interoperability

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Statistics Norway Information Architecture – some challenges

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  1. Statistics NorwayInformation Architecture – some challenges ODaF meeting, Colchester 14 -15 April 2008 Rune Gløersen Director Department for IT and Data Collection Statistics Norway

  2. Initiatives dependent on enhancement ofinformation architecture • E-Government • Need for interoperability • Reducing the response burden • Common, (inter)national metadadata repositories • Access to data for reasearchers

  3. Common public ICT-Architecture Access and Presentation layer Business Portal Local Services Citizen Portal Common Services and Components layer Forms Engine Data Exchange Service Library Common Metadata Security Rules Engine Public Mailbox Authentication eID Administrative Registers Open Standards Local Services and Administration Local Services Local Information Local ICT architecture Subject Matter Systems Data administration

  4. Statistics Norway IT Strategy Business Portal Citizen Portal Intranet ssb.no Cooperative IT systems Dissemination Data Editing and Analysis Data Collection Management of Statistical Populations Metadata Data Exchange Observation Registers, Longitudinal data, Data Archive IT Infrastructure Security

  5. Towards a coherent production system based uponthe Statistical Business Process Model Quality assessment and control Need Develop and design Build Collect Process Analyse Disseminate Methodology and infrastructure

  6. Easy and flexible access to information Local metadata Local metadata Local metadata End users Content (Quality) declaration Question- naire repository File descript. DATA Access ? Variable definitions Statistical activities Classifications Macro database

  7. Variables Data Archives Longi-tudinal data Classifications Metadata integration Metadata sources/services Data collection Questionnaires Processing Statistical products Dissemination

  8. Variables Data Archives Longi-tudinal data Classifications Metadata integration Metadata sources/services Data collection Questionnaires Processing Lack of common architecture & standards Statistical products Dissemination

  9. Structures, domains and user needs Dissemination database Information Availability Data Ware House Data Ware House Data Ware House Data Ware House Analysis Data Ware House Data Ware House Functionality Data Ware House Data Ware House Archive Research Reliability

  10. Standards? Dissemination database Information Dublin Core? Topic maps? Data Ware House Data Ware House Data Ware House Data Ware House Analysis Data Ware House Data Ware House ISO 11179 ? Neuchatel ? Whatever ? Data Ware House Data Ware House Archive Research DDI ?

  11. Access to microdata for researchers Local metadata Local metadata Researcher Local metadata Content (Quality) declaration Browse/Request Question- naire repository File descript. DATA Variable definitions Statistical activities -Delivery by license -Remote Access -Public Use Files Etc. Classifications Service Macro database

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