Meta Plus Klas Blomqvist Statistics Sweden Research and

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

Agenda • The VHS-project – Background • Meta. Plus – 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 – Meta. Plus, future development • Meta. Plus and the production process

Why do we need metadata?

Why do we need metadata?

Metadata at Statistics Sweden • SCBDOK – Word template, standardised structure free text description • Meta. Plus (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

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

Meta. Plus • 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

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

The plus with Meta. Plus • 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

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

Using Meta. Plus • 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

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

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

The application structure Register Object class Register variant Population Register version Variable Can be reused Unique for the survey round Database/ file Column Value domain Content oriented IToriented

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

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 • Meta. Plus support – Migration, training, helpdesk

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

The Statistical Production Process The colours relate to potential areas for process ownership L e v e l Target, cust. dem Frame and sample Data collection Data preparation Statistical computation Dissemination and communication Customer contacts Compilation of frame Administrative registers Coding Estimations Dissemination Customer reactions Identification of data sources Sampling Direct data collection Editing Production of tables and diagrammes Data dellivery To customers Other primary statistics Corrections Analysis of Process data and customer reactions Cont. , predesign Evaluation/ cust. satisf. 1 L e Assessment v Data delivery Survey design e Tender/agr. Archiving Feed-back to the production process Prognosis Simulation models l Technical preparations 2 Statistical analysis Compilation of results Documentation preparations 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

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

Conclusions • Reuse • Harmonisation and standardisation tool • Meta. Plus 1. 2 in production The End! • Organization (the KMI-group) • Content – slow progress