1. at Statistics Netherlands
Data Service Centrum
Implementation of the
Data Service Centre
at Statistics Netherlands
Harry Goossens
Programm Manager DSC

2. Agenda Why, What, How ? The CBS Metadata model
The CBS Business Architectuur
Steady States
Implementation
Daily practice
Demo

3. Data Service Centre: What is it ?
Fundamental corner stone of the CBS Business Architecture
Central ‘vault’ with Steady States, linking:
statistical data (facts & figures)
conceptual metadata (description)
technical metadata (user’s guide)
documentation
Implementation of the Dutch metadata model

4. DSC: The concept No data without metadata
Based upon dedicated metadata model
Strict distinction between the data that are actually processed and the metadata that describe the definitions, the quality and the process activities
Steady states are explicitly designed for re-use.
The metadata (of steady states) are generally accessible and are standardised as much as possible

5. DSC: What offers it ? Generic services: Catalogue: searching & finding
Metadata management
Centralised data distribution
Authorisation management
Automatic process interfacing
Archiving of statistical datasets
Version management

6. DSC: Conceptual metadata
Metadata that describes the data in a generic, non specific
way, in all the various phases of the statistical proces:
Input description of received data; terminology of the supplier (internal and external)
Processing description of data produced from various statistical processes; internal (and international) standards/guidelines
Output description of publishable outputdata; definition of (sub)populations, outputvariables, object types content description,

7. CBS Metadata model - micro

8. CBS Metadata model - macro

9. DSC Metamodel - simplified
Variabele
Dataontwerp
1 : n
1 : 1
Technische
Metafile (XML)
Context
Variabele
1 : n
Documentatie
(Word, PDF,….)
Datasets
(ASCII CSV, Fixed)

10. CBS Business Architecture
Strategy
Design
DSC – Metadata Catalogue
Chain management
Statistics
Production
Steady States
DSC - Data Storage

11. Steady States

12. What are steady states ?
A steady state is a data set together with information for its correct interpretation.
Rectangular
Rows represent units (micro) or classes of units (macro)
Columns represent variables
Heading: population, time
Dataset design (vary time): in design phase
Dataset design is like a template of a table: only borders and heading
1 Dataset design, n Datasets

13. Why steady states ? Reduce storage: Store once Re-use many times
Secure the statistical proces:
Each steady state is a guaranteed fall back point
Improve consistency:
Every following process uses the same dataset
Improve flexibility
Enables independent, generic proces design

14. Implementation Micro model simplyfied for practical use
Skip / Combine objects
Reduce attributes
Why Documentum ?
Completely object orientated, enables to implement DSC metamodel
Largly configurable (user interface, authorisation, etc.)
Large flexibillity
Proven technology
TaskSpace

15. DSC system - Functionalities
Taylor made user interface (TaskSpace)
maintenance of meta objects
constraints mostly in interface, not in DB/repository
Specific flexible search engine
various entrances, easy to extend
Import & Export of datasets
modified according to model
Interface for bulk-import of metadata
based on standard XML schema
conceptual and technical meta

16. Daily practice - Challenges
Available metadata quality often poor
Great variety, each statistic own way of describing
Often tool based (SPSS), more technical then logical
Definition = question from survey
Minimum mapping with DSC model
No real urge
Although rated IMPORTANT, low priority
No clear ownership, resonsibility not felt
Extra work without direct gain (burden)

17. Daily practice - Road map
Explaining the concept & metadata model
Requirements, guidelines
Stocktaking
What meta is available ?
How extensive or poor ?
What quality, actuality ?
Re-usability
Mapping on the model
(Re)Design Datadesigns
Matching attributes

18. Daily practice - Chances
Look for added value
Problems ?
Wishes ?
(Long) Wanted improvements ?
Re-usability
Define pilot
Quick hands on experience, short cycle
Good estimation time & resources
‘Proof of the design pudding’

19. Daily practice – At work
Excel template
Porch / Ballot
Visual check on guidelines
Automated check on completeness, inconsistencies, relations VARIABLE – CONTEXT VARIABLE
Corrections
Bulk import
TMF
Meta XML

20. Screenshot

21. Screenshot: Metadata attributes of a Data design

22. Screenshot: and this is how we stor the statistical datasets