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Value of Meta-information System for Czech Statistical Office

This article explores the importance of the Statistical Meta-information System (SMS) in the Czech Statistical Office and its impact on the redesign of the Statistical Information System (SIS).

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Value of Meta-information System for Czech Statistical Office

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  1. Value of Meta-information System for the Czech Statistical OfficeTopic 2(i) Advocating for metadata in corporate context Joint UNECE/Eurostat/OECDwork session on metadataLuxembourg, 9–11 April 2008 Ebbo Petrikovits ebbo.petrikovits@czso.cz

  2. Introduction • In 2005 two new and important project were launched: • Reform of statistical survey system (SSS) • Statistical meta-information system (SMS) • based on the SMS Vision • In 2006 • Reform of the SSS was transformed into Redesign of statistical information system • SMS became a standard part of the Redesign project

  3. Redesign of SIS - major goals • reducing response burden and boosting respondent motivation • improving quality of statistical information • optimising production of statistical information in the CZSO • designing a conceptual model of Redesigned SIS and of SMS • defining a unified architecture of statistical tasks • increasing users’ comfort

  4. Redesign of SIS - core principles • systematic assessment and evaluation of statistical data requirements • increasing share of administrative data • increasing use of data modelling • implementation of SMS • implementation of statistical data warehouse • freeze of statistical surveys for 2-3 years • avoiding redundancy in statistical surveying

  5. Unification of statistical processes • Work on the GAS-SIS opened the need for description and standardization of the key process - production and dissemination of statistical information • We proposed a model of this process • It consists of 7 main phases • Inside the phases we defined set of activities

  6. Key process • Phases: • Evaluation of users requirements • Definition of statistical task • preparation of data collection and processing • Data collection • Data processing • Data analysis and output production • Dissemination

  7. Links to other processes • Supporting processes • Costs controlling • Work efficiency evaluation based on the processing quality

  8. SMS goals • principle goal - to support, standardize and describe the key process in statistics • in this context - support of : • management of methodology-related activities • assessment of statistical data quality • monitoring of respondent burden • integration of SIS with public administration and international organizations • design, implementation and management of statistical tasks

  9. SMS Architecture • Based on the SMS Vision • Global Architecture of SMS (GA-SMS) • defined the basic principles and rules for design and implementation • set up the IT environment

  10. Content of the SMS Statistical Registers Statistical Tasks Statistical Quality Users SMS Time Series Dissemination Respondents Data Fund GA-SMS Statistical Classifications Statistical Variables

  11. SMS implementation strategy • definition and development of individual subsystems • implementation of individual subsystems • tests of individual subsystems • integration tests • semi-operational running • pilot project on selected statistical task • operational running • step-by-step transition of existing statistical tasks into SMS

  12. Technological environment • Technological infrastructure • UNIX operating system • Oracle database system • PC with OS Windows/Linux as a client workstations

  13. Technological principles • Work stations independent on operating system • Internet browser as a basic tool for communication • No supplementary products on the client work station • Oracle Forms as a basic tool for development of applications • Access to the SMS subsystems via SMS Access Portal

  14. Subsystem CLASSIFICATION • Inspired by Neuchâtel Classification Model • Described objects: • classification • version of classification • variant of classification • code-list • basic code-list • combined code-list

  15. Subsystem VARIABLES(1) • Described objects • statistical variables • basic • subject-matter broken-down • On conceptual level very similar to the Neuchâtel Variables Model

  16. Subsystem VARIABLES(2) • Detailed model: • a statistical data is identified by set of metadata • this set we divide into four complex variables • complex variable consists of elementary variables • elementary variable consists of: • type of variable • specification of variable • type/specification of a elementary variable consists of: • code-ist code • code of code-list item • valid from

  17. Subsystem VARIABLES(3) • Complex variables: • statistical variable - describes the content of a data • statistical object - describes observed object • time variable - describes the current time of observation • complementary variable - describes other identification attributes which do not belong to the above mentioned variables

  18. Subsytem TASKS • Described objects: • statistical task • structure of a questionnaire • elements of a questionnaire • input/output sets • VIP (virtually identified items) • time-tables • program modules and runs • response duty specification

  19. State-of-art in SMS implementation • CLASS, VAR • tests of version 1.0 finished, • preparation of real code-lists, classifications and statistical variables needed for the pilot test • tests of version 1.1 • TASKS • preparation of tests • training of the member of the test team

  20. SMS Management • management in the implementation phase • project approach applied • multi-professional teams • permanent monitoring from the top management • management in the operational run phase • establishment of the SMS administration

  21. SMS management in the implementation phase

  22. SMS management in the operational phase SMS Administration Central Administration CLASS Administration VAR Administration TASKS Administration QUALITY Administration SMS -Methodologist S-Administrator S-Administrator S-Administrator S-Administrator C-Administrator C-Administrator C-Administrator C-Administrator S-Methodologist S-Methodologist S-Methodologist S-Methodologist Technology Administration S-Administrator - subsystem administrator C-Administrator - content administrator T-Administrator - technology administrator S-Methodologist - subsystem methodologist T-Administrators

  23. Major findings(1) • SMS strategy - content and methodology -> fully in the responsibility of the statistical office • SMS design and implementation should be organize in multi-professional teams -> increasing of research capacity • Development of software applications -> may be outsourced (contract based) • Testing -> close cooperation of the project teams and the contractor (follow-up the time-schedule is necessary)

  24. Major findings (2) • Coordination of time schedules for Redesign project and SMS project • Motivation of project teams - sharing of knowledge an information • Monitoring of the activities by: • the top management - quarterly • the steering committee - quarterly • the project task force - monthly • project teams - weekly

  25. Major findings (3) • Importance of training and transfer of SMS know-how • Focus on the subject matter topics and use of SMS tools in statistical practice is advisable

  26. Thank you for your attention

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