1. Max Booleman Statistics Netherlands
Metadata models
Max Booleman
Statistics Netherlands

2. Content Introduction Functions of metadata Kinds of metadata
Why do we need a metadatamodel?
Choosing a model
Brief overview different models
Communities/platforms
Lessons learned

3. Introduction The ‘old’ way The ‘new’ way Special dedicated surveys
Combined complex designs of registrations and samples (minimize administrative burden)
Stove-pipe statistics
Common input- and outputbases (sharing data)
Knowledge in the head of employees
Knowledge in documents (metadata)
Tailor made tables
Common structure

4. Functions of metadata (1)
Input data + transformation = output data
Describing data
Describing process
Describing quality (data and process)

5. Functions of metadata (2)
Information for users, producers inside and outside the office
What does it mean?
(Automatic) Rules for producers inside the office
Ex ante vs ex post:
What should you do?
What did you do?

6. Kinds of metadata Related to the functions:
Conceptual: describing text, relating elements
Process: methods, programs, sequence
Quality: norms and indicators (data and process)
Technical: the hardware

7. Why a model? We want: Re-use of definitions, classifications, …
Re-use of processes, rules, methods
Re-use of data
A model facilitates the conceptual level:
Structure (coherence)
Relations between (data consistency)
Meaning of (textual consistency)
Processes: metadata driven (machine readable)

8. Properties of a model A good model should: Meet the user needs
Be compact
Have a coherent set of metadata object types
Model: metadata of the metadata
There is no universal model, like there is no universal car.

9. Example What do I need to understand ’21’? Turnover Costs Profit
Trade, Enterprises, 2001
Turnover
*1000 euro
Costs
Profit
Size class 1 9
Size class 2 12
total 21
What should be the metadata of ’21’?
What do I need to understand ’21’?

10. Example (cont. 2) What do I need to understand ’Turnover’? Turnover
Trade, Enterprises, 2001
Turnover
*1000 euro
Costs
Profit
Size class 1 9
Size class 2 12
total 21
What should be the properties of the variable ‘turnover’ (the metadata of ‘Turnover’)?
What do I need to understand ’Turnover’?

11. Example (cont. 3) Modelproperties Example name Turnover description
Earnings of an enterprise
statistical unit
Enterprise
period
Year
relation
Turnover=costs + profit
measurement unit
Euro
type of aggregation
Sum

12. Remarks (1) Part of the properties? Period ‘Year’/ Name ‘Turnover’
Measurement unit ‘euro’
Versioning (lifecycle)
Homonyms/Synonyms

13. Remarks (2) A model is like decomposition of sentences:
The total turnover of enterprises in The Netherlands was in 2008 equal to … billion euro.
The total turnover of the enterprise Shell in The Netherlands was in 2008 equal to … euro.
A Population of Statistical units at or during a ‘time’ will be described by Variables

14. Remarks (3)
Definition of Age, Turnover etc.: in principle unit independent but formulated user friendly.
The concept of ‘age’ is the same for electrons, cars, buildings and human beings.

15. Remarks (4) Relation between statistical units:
A student is a kind of a person: inherit properties of person additional (useful) own properties
A household contains persons
An enterprise contains establishments

16. Remarks (5) Relation between populations:
Income of all persons of one household = Income of the household?
Income of all persons = Income of all households?
Turnover of all establishments = Turnover of all enterprises? Consolidation?

17. Julius Ceasar
Columbus BC AC
Present
statistics
forecast

18. Julius Ceasar Columbus BC AC Present statistics forecast 1-1-2006
Present
statistics
forecast

19. Julius Ceasar Columbus BC AC Present statistics forecast
Dutch nationality
Julius Ceasar
Columbus BC AC
Present
statistics
forecast

20. Julius Ceasar Columbus BC AC Present statistics forecast
Dutch nationality
Julius Ceasar
Columbus
Inhabitant of The Netherlands BC AC
Present
statistics
forecast

21. Remarks
A population is a collection of statistical units limited in time, area, …..
Could ‘student’ be a statistical unit?
‘Student’ is a kind of ‘person’ so
‘Students’ is formally a subpopulation of ‘persons’
Should we distinguish 5 or 1000 kinds of statistical units?

22. ‘Choosing’ a model Own wishes Checking existing models
Logical, coherent description of input, output (files)
Checking existing models
Compile own model (compact!)
Map to/from existing models
Plan-Do-Check-Act

23. Overview (1) XBRL: exchange of micro data (http://www.xbrl.org/Home/)
IMF (GDDS, SDDS)
SDMX: exchange of statistical data
Push
Pull
Neuchâtel group (classifications, variables)

24. Overview (2)
ISO (
DDI 3.0 (
Dublin Core (

25. Communities/platforms/conferences
Metanet (
Metis (
Working group Eurostat (
Q2008/Q2006/Q2004/Q2001 ( and
SDMX
XBRL (
CODACMOS (

26. Lessons Learned (1) The ultimate model does not exist (yet?)
Mapping from and to models
Start with your own wishes
Start with a standard model and adjust
80%-20% rule: don’t try to do everything at once
Store only what is in use

27. Lessons Learned (2) Think broad, start small Homonyms and synonyms
Survival of the fitting: using standards should be efficient
Adjusting standards often (very) expensive
Homonyms and synonyms
Formal description is difficult and takes time and effort

28. Questions?