1. Quality vs quantity: Stovepipe better than DWH?
COE on S-DWH
Wiesbaden

2. The European Statistics Code of Practice
Five principles covering statistical processes
Sound methodology
Appropriate statistical procedures
Non-excessive burden of respondents
Cost effectiveness
10 May 2019

3. The European Statistics Code of Practice
Five principles covering statistical processes
Sound methodology
Appropriate statistical procedures
Non-excessive burden of respondents
Cost effectiveness
Five principles covering statistical outputs
Relevance
Accuracy and Reliability
Timeliness and Punctuality
Coherence and Comparability
Accessibility and Clarity
10 May 2019

4. Quality in ‘Best Practice’ cases
Best practice cases emphasize the statistical processes
Example: Statistics Finland’s planned costs and savings for S-DWH project
10 May 2019

5. Quality in S-DWH manual
S-DWH manual deals mainly with quality of statistical processes
Metadata chapter: Metadata of the S-DWH layers
Metadata chapters: – Metadata functionalities by layers
Governance Chapter: 2.1 Governance of the Metadata
Implementation: Quality requirements for the statistical data
There is not much about quality of statistical outputs
10 May 2019

6. Quality in S-DWH manual
S-DWH manual deals mainly with quality of statistical processes
Metadata chapter: Metadata of the S-DWH layers
Metadata chapters: – Metadata functionalities by layers
Governance Chapter: 2.1 Governance of the Metadata
Implementation: Quality requirements for the statistical data
There is not much about quality of statistical outputs ... until today!
10 May 2019

7. Group Work We’d like you to form into 5 quality groups, and discuss
(a) how your quality dimension(s) can be measured in a DWH
(b) how DWH & stovepipes perform re your quality dimension(s)
Once you’ve had your discussion (~ 20 minutes) we would like each group to present feedback to everyone (~ 5 minutes per group)
GO!
10 May 2019

8. Relevance Who are current/potential users What are their needs
Does output meet needs
10 May 2019

9. Accuracy & Reliability
Sampling error
Non-sampling error
10 May 2019

10. Timeliness & Punctuality
Production time
Frequency/punctuality of release
10 May 2019

11. Accessibility & Clarity
Needs of analysts
Assistance to locate data
Dissemination format
10 May 2019

12. Coherence & Comparability
other statistics/data in same domain
or at different frequencies
Comparability
over time
space
domains
10 May 2019