1. Metadata used throughout statistics production
Max Booleman
Statistics Netherlands

2. Content Recapitulate: functions of metadata
Generic Statistical Business Process Model
Statistical production cycle
Phases of the process

3. Functions of metadata (1) (once more)
Input data + transformation = output data
Describing
Data
Process
Quality (data and process)

4. Functions of metadata (2) (once more)
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?

5. 2-5-2019 Quality Management / Metadata Management 1 Specify Needs 2
Design 3
Build 4
Collect 5
Process 6
Analyse 7
Disseminate 8
Archive
1.1
Determine need for information
1.2
Consult and confirm need
1.3
Establish output objectives
1.5
Check data availability
1.6
Prepare business case
2.1
Design outputs
2.2
Design frame and sample methodology
2.3
Design data acquisition methodology
2.4
Design statistical processing methodology
2.5
Design processing systems and workflow
3.1
Build data collection instrument
3.2
Build process components
3.3
Configure workflows
3.4
Test production system
3.6
Finalize production system
4.1
Select sample
4.2
Set up collection
4.3
Run collection
4.4
Finalize collection
5.1
Integrate data
5.2
Classify and code
5.3
Validate and edit
5.5
Derive new variables and statistical units
5.7
Calculate aggregates
6.1
Prepare draft outputs
6.2
Verify outputs
6.3
Scrutinize and explain
6.4
Apply disclosure control
6.5
Finalize outputs for dissemination
7.1
Update output systems
7.2
Produce dissemination products
7.3
Manage release of dissemination products
7.5
Manage user support
7.4
Promote dissemination products
8.1
Define archive rules
8.2
Manage archive repository
8.3
Preserve data and associated metadata
8.4
Dispose of data and associated metadata
5.6
Calculate weights
1.4
Identify concepts and variables 9
Evaluate
9.1
Gather evaluation inputs
9.2
Conduct evaluation
9.3
Agree action plan
5.4
Impute
3.5
Test statistical business process
5.8
Finalize data files

6. Generic Statistical Business Process Model
Non linear!!!
All kinds of processes: stove pipe, register based
International communication
Internal communication
Generic Tools development

7. Design phase (1) Develop metadata: Conceptual: tune with users
Process: tune with IT, methodology, producers
Quality: tune with users and producers
Ex ante metadata: what should you do

8. Design phase (2) The fundamentals NSI’s are describing Reality
A model of reality
Registrations (input = output)?
(target population versus survey population)

9. Design phase (3) The Statistical Cube:
Timeliness by coherence by revision

10. The statistical cube (1)
Single source
Multiple source
Integrated
Before
(tendency)
Month
Quarter
Annual
Populaties en begrippen vaak verschillend
Maar wel te relateren aan elkaar
Enkelvoudige bron meestal:
inputconcepten=outputconcepten
Herkenbaar voor respondenten
Kleine correcties
Vertelllen wat de bron levert
Geïntegreerd meestal:
Nieuwe outputconcepten
Herkenbaar voor specifieke gebruikers
Grotere correcties
Theoretisch model
Relatie tussen enkelvoudige bron en geïntegreerd:
Check kwaliteit enkelvoudige bron

11. The statistical cube (2)
Third dimension: versions, corrections, revisions
Multiple indicators
Later = more accuracy
Later = more coherence
Later = more comparability
Later: should be ‘better’

12. The statistical cube (3)
past
today
time
tendency
monthly
quarterly
annual
integrated

13. Use of Statistical Cube (1)
Coherence:
Presentation guide (related indicators)
Explanation guide (how does it work)
Conceptual differences

14. Use of Statistical Cube (2)
Consistency and Coherence:
Quality declaration inside office
Quality declaration outside office
‘Allowed’ differences between indicators

15. Use of Statistical Cube (3)
The easy part:
Building cubes based on equal concepts
Presentation of differences
Challenges:
Building cubes based on fuzzy relations between concepts
Appointments between departments
Changing concepts

16. Pre-input phase Receive data and metadata from the outside world
Primary inputs
Secondary inputs
External terminology
External observation units
External formats
Information layers (paper, files, cd, etc)
External metadata, external data formats

17. Input phase input + process = output Internal data format
External metadata
Ex post metadata: what did you do
Quality metadata: compare ex ante with ex post
External metadata, internal data formats

18. Micro phase input + process = output
Internal metadata (re-use metadata)
Linking sources
Linking to Population base on individual level
Editing data
Internal metadata, internal data formats

19. Analytical phase input + process = output Combining data (re-use data)
Rising, sum up, etc
Indicators, indexes, averages, etc
Statistical Quality (confidence intervals, etc)

20. Output phase Transform into output format: Presenting
Statistical tables (data and metadata)
Metadata itself
Methods
Disclosure
Special language for external users (external terminology)

21. Lessons learned (1) Internal and external use of Homonyms and Synonyms
Code lists
Classifications
Populations

22. Lessons learned (2) The fundamentals NSI’s are describing Reality
A model of reality
Registrations?