1. Case Study Statistics Netherlands Max Booleman m.booleman@cbs.nl Statistics Netherlands METIS, 2010

2. Outline of presentation 1.Architecture 2.Relation with GSBPM 3.Interrelated processes and GSBPM 4.Business Principles 5.Steady States 6.Metadata model 7.Challenges 8.Lessons learned

3. Business Architecture

4. Business Architecture- Output driven Client Customer Source Respondent

5. Business Architecture- Layers Statistics Production Chain Management Design Shared service: Data Service Centre

6. The BA principles of SN 10 main principles. Examples are A strict distinction is made between the data that are actually processed and the metadata that describe the definitions, the quality and the process activities During the design of the statistical process, the benefits of re-use are exploited to the maximum degree, both within and outside Statistics Netherlands Steady states are explicitly designed for re-use. The metadata (of steady states) are generally accessible and are standardised as much as possible The production of statistics is output-driven Observational inputdata are stored as observed (in the inputbase). Publishable outputdata are stored as published (in the outputbase).

7. Business Architecture- Steady states inputbase microbase statbase outputbase pre-inputbase post-outputbase Data and metadata (including quality)  Quality control  Chain management  Re-use of data Central role in (re)design and production Access through central Data Service Centre

8. Relation with GSBPM Not covered by GSBPM GSBPM 4 till 7 Chain Management GSBPM 1 till 3 Not covered by GSBPM Steady states

9. Interrelated processes and GSBPM Population, Units, Registers Large Units STS SBS SNA External Output M,Q,Y Y Q, Y Y Y

10. 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

11. Why Steady States? 1.Reduce storage: store once, re-use many times 2.Secure the process 3.Improve consistency 4.Improve flexibility: time to market Steady States: corner stone of BA

12. Metadata classified according to three criteria (1) First criterion: when it is developed. Ex ante metadata is developed during the first three phases of the Generic Statistical Business Process Model (GSBPM): Specify needs, Design and Build. Ex post metadata is developed during each production run. These consist of the other phases of GSBPM.

13. Metadata classified according to three criteria (2) Second criterion: the function of the metadata. Here we distinguish four types of metadata: conceptual, process, quality and technical metadata. For instance, ex ante process metadata will tell you how statistics should be produced. Ex post process metadata tells you how they were produced.

14. Metadata classified according to three criteria (3) Third criterion: quality. Metadata in our pre-input and input bases are formulated according to the respondent's wording. Metadata in our other bases are formulated according the office standards. These also contain explicitly the international standards.

15. Dutch metadata model Dedicated model to –describe Datasets including process and quality metadata –ex ante in Dataset design –ex post in Dataset –find datasets Inspired (among others) by Swedish model and Neuchâtel Treats micro data and macro data differently

16. Micro model

17. Macro model

18. Challenges Quality of metadata will become a high priority issue in the (near) future. ISO 11179 presents some guidelines and rules. Acceptance of Data Service Centre by the business. Data Service Centre implies a new way of working and an additional workload for producers. The benefits are for the users of the data and not in first place for the producers. So we have to convince the producers to spend time and money on it. Additional Workload. Metadata should be made during the development phase of a project. But if you want to store existing datasets (our history) in the Data Service Centre it means an additional workload for a production environment.

19. Lessons learned 1.A metadata model serves the functions you need 2.There is no universal model 3.No model without explicit formulation of required functionality 4.Limit the functions of the model 5.Start small, think broad 6.Quality of metadata often a forgotten issue 7.There was no business case for metadata but there is a business case for Steady States.