1. Mogens Grosen Nielsen Statistics Denmark (mgn@dst.dk)
Implementation of GSBPM, DDI and SDMX reference metadata at Statistics Denmark UNECE workshop on International Cooperation for Standard-based Systems Geneva 5-7 May 2015
Mogens Grosen Nielsen
Statistics Denmark

2. History, strategy and principles on quality and metadata
Agenda
History, strategy and principles on quality and metadata
Definitions, models and solution on quality reporting
Towards more integrated metadata
Information models
Lessons learned and opportunities

3. Short history on metadata initiatives
Before 2010: Separated and partially standardised metadata systems
January 2010: Taskforce: integration variables, classifications, statistical concepts and quality
October 2011: Test DDI (focus on integration)
January 2012: EU grant. SDMX quality concepts, DDI, and GSBPM. Colectica as tool
January 2015: Quality declarations for all statistics (300+) in Colectica
February 2015: Strategy on quality and metadata approved

4. Strategy: Vision
Metadata about content and quality must guide internal and external users
in their knowledge processes
give precise information about the products
Internal efficiency gains
International standards
GSBPM,
SDMX,
GSIM,
DDI
and others

5. Strategy: Principles
Business Process Management (End-to-End Processes)
Stepwise implementation
Code of Practice and Quality Assurance Framework
Principles on metadata
Metadata must fulfill user needs
Metadata and metadata flow integrated into GSBPM
As much reuse as possible
Active use of metadata in IT-systems (incl. metadata driven production)

6. Metadata definition #1. How to communicate this term to statisticians?
Use the SDMX definition as the short and easy-to-understand definition
Reference metadata:
Conceptual metadata
Methodological and processing metadata
Quality metadata
Structural metadata:
Metadata act as identifiers and descriptors of the data

7. Metadata definition #2. How to communicate this term to statisticians?
Use Generic Statistical Information Model (GSIM) to tell the full story

8. Klassifikationsdatabase
”Classical” metadata using Data Documenation initiative (DDI), SDMX and Colectica – ”The Diamond”
StatBank
Methods/ ”Survey”
Methods papers
Quality declaration
Concept
Variable/dataset
Concepts database
Hvad betyder
Variable database
Classifications
Implemented in
Klassifikationsdatabase
Class database

9. SDMX Standard for Quality Single Integrated Metadata Structure (SIMS)
Content (population, concepts, reference time etc)
Statistical processing (sources and methods)
Information on 5 quality dimensions
Relevance
Accuracy and reliability
Timeliness and punctuality
Comparability
Accessibility and Clarity

10. SIMS and reporting formats Euro-SDMX Metadata Structure (ESMS) and ESS Standard for Quality Reports Structure (ESQRS)

11. GSBPM and work processes with focus on quality declarations
Needs
Prepare user needs etc.
Analyse :
Fill in accuracy etc.

12. The solution Enter Quality Information Publish at Dst.dk
Existing metadata
METADATA IN
COLECTICA
Publish at the Intranet
Quality eports to Eurostat 12

13. 300 surveys implemented January 10 2015
Enter Quality Information
Publish at Dst.dk
Existing metadata
METADATA IN
COLECTICA
Publish at the Intranet
Quality reports to Eurostat 13

14. Towards more integration of metadata
Business perspective: Business Process Management (BPM) and metadata-driven approach
GSIM compliant model in DDI and Colectica (concepts, variables, classification etc)
Harmonisation of statistical concepts
Integration of metadata in publishing systems
Metadata management

15. Business Process Management
General Environment: Political/legal context, Technology/standards
Ressources:staff IT-systems etc ?
Respondents/ registers etc
Users
User needs /orders

16. Management-, core- and support-processes
General Environment context: Political/legal, Technology/standards
Ressources:staff IT-systems etc
Management processes
Respondents/ registers etc
Users
Support processes: Quality, metadata, methods & IT
User needs /orders

17. Business processes and metadata driven approach

18. Business processes and metadata driven approach
Reference metadata recorded: needs, purpose etc.

19. Business processes and metadata driven approach
Structural metadata: DDI on questionnaire, variables and cubes etc
Reference metadata: concepts, population etc

20. Business processes and metadata driven approach
Metadata used to create survey system, databases and output systems etc

21. Business processes and metadata driven approach
Auto-generated survey system used

22. Business processes and metadata driven approach
Reference metadata on quality etc

23. Business processes and metadata driven approach
Structural and reference metadata used for dissemination (e.g. quality reporting)j
Autogenerated code used in dissemination systems

24. Business processes and metadata driven approach
All metadata used for evaluation

25. Models: conceptual, logical and
physical
What we are doing at Stat DK
Model / level
What are we doing at Stat DK
Conceptual
Selection of variable, concept etc from GSIM (the concept corner)
Logical
GSIM compliant DDI model (3.2)
Physical
GSIM compliant DDI model implemented in Colectica

26. Lessons learned Need for
Improving content - declarations more uniform and compliant with common guidelines
Using the same quality-concepts for many purposes: report to Eurostat, publish at national web-site and to other international organisations.
Need for more analysis and improved dissemination at
Improved focus on change management and communication
Clear organisational roles and a dedicated cross-cutting group to introduce and implement standards

27. Opportunities for cooperation
Work on models – from abstract to concrete level (BPM, GSIM, GSBPM, DDI and SDMX)
GAMSO and CSPA needs more attention
Sharing of solutions for input, processing and output systems – e.g. Colectica add-ins
How to handle metadata management integrated into GSBPM
Common metadata

28. Thanks for your attention
Remember:
DDI conference in Copenhagen 2-3 December 2015

29. The End!