MetaPlus

1. MetaPlus Klas Blomqvist Statistics Sweden Research and Development – Central Methods klas.blomqvist@scb.se

2. Agenda • The VHS-project • Background • MetaPlus • The concepts, the product • KMI-group • Support and management for classifications, metadata and content harmonisation • The Lotta project, process reengineering at Statistics Sweden • A more effective production process • MetaPlus, future development • MetaPlus and the production process

3. Why do we need metadata?

4. Why do we need metadata?

5. Metadata at Statistics Sweden • SCBDOK • Word template, standardised structure free text description • MetaPlus (Metadok) • Formalized metadata, coded information • KDB, the classification database • Statistical standards and classifications • Other documentation • Description of the statistics – the quality declaration • Product database • Private production documentation

6. The VHS-project • The VHS-project started 2004 • Compilation of requirements: Metadok, new requirements • Systems design from 2005 • Use cases, modeling • System development 2006 • Application, data base • Production 2007 • From January 2

7. MetaPlus • Replace Metadok • Scope: • Documentation tool with improved quality • Better overview over variables and data • Tool for standardisation and harmonisation • Possibilities for reuse and coordinated use • Consecutive documentation • Requirement: • That the documented material in Metadok can be migrated

8. Information • User groups • The Metadata group (reference group) • The methods council • The IT council • The register council • The board of directors • The scientific council • Seminars

9. The plus with MetaPlus • Support for the production • Cooperation with other metadata systems, Archiving, the Personal Data Act, connection to data • User support • Search the microdata • Improved quality • Contents, accessibility and comparability • More cost-effective • Increased use of collected data • Reduced respondent burden • No new collection if data already exists

10. Easier to document • Starting point in already existing metadata • Classifications and standards • Documentations made by others • Search the metadata repository • Document once - reuse • Input data in the form of tables • Document the table at once instead of one cell at a time • Consecutive documentation • Effects: • Higher metadata quality • Automatic harmonisation

11. Using MetaPlus • New requests (commissions), new surveys • Examine if the issue can be solved with already existing data. • Information for facilitating coordinated use and harmonisation • Search for variables • Look at value domains • Find information on populations • Shows possibilities for matching data • Highlights differences in data materials

12. Content • Standard variables • Variables • Object classes • Classifications and value domains • The survey’s registers • Survey population and register population

13. Object class Variable Conceptual value domain Register Register variant Population Object variable Value domain Register version Context Population Context variable Value The model

14. Register Object class Can be reused Contentoriented Register variant Population Value domain Register version Variable Unique for the survey round IT-oriented Database/ file Column The application structure

23. MetaPlus functionality, some examples • Advanced search • Longitudinal registers • Time series • Historical information • Archiving • Web prototype • Documentation • Reuse • Harmonisation • Administration • Classifications • Variable groups • Personal Data Act Administration System

24. The KMI-group • Research and Development department, all units represented • Management, Central Methodology, Register coordination and microdata and Central IT units • Responsibilities • Classifications • Metadata • Content harmonisation • MetaPlus support • Migration, training, helpdesk

25. The Lotta project • Standardised production and tools • Process orientation • Customer focus • Efficiency • Standardisation • Quality control • New organisation after summer • Internal review during summer

26. The Statistical Production Process The colours relate to potential areas for process ownership Level 1 Target,cust.dem Frame and sample Data collection Data preparation Statistical computation Dissemination and communication Evaluation/cust.satisf. Cont.,predesign Administrative registers Compilation of frame Coding Estimations Dissemination Customer reactions Customer contacts Identification of data sources Direct data collection L e v e l 2 Sampling Editing Production of tables and diagrammes Data dellivery To customers Analysis of Process data and customer reactions Assessment Other primary statistics Corrections Archiving Statistical analysis Data delivery Survey design Feed-back to the production process Prognosis Tender/agr. Simulation models Technical preparations Compilation of results Documentationpreparations General for all processes: Internal evaluation, quality control Deliveries between processes Process data Meta data System architect. Keeping of reg. Treatment of Time series breaks

27. General for all processes: Internal evaluation, quality control Deliveries between processes Process data Meta data System architect. Keeping of reg. Treatment of Time series breaks MetaPlus in the Statistical Production Process Target,cust.dem Data preparation Statistical computation Frame and sample Data collection Dissemination and communication Evaluation/cust.satisf. Cont.,predesign MetaPlus

28. Conclusions • Reuse • Harmonisation and standardisation tool • MetaPlus 1.2 in production • Organization (the KMI-group) • Content – slow progress The End!