Nordic Statistical Meeting 26.-28.8.2019, Helsinki

1. Data sharing in the public sector in Denmark
A pilot project with the Danish Universities
Annie Stahel, CIO, Statistics Denmark 1

2. The changing environment for official statistics
Data sharing for the 8 universities in DK
Parties involved in the data sharing process:
• The Ministry of Research and Education
• Statistics Denmark
• 8 universities
2

3. The premises for data sharing
• Data that has been collected for the purpose of statistics may not be used
for other purposes, e.g. administrative purposes
• The data owner can decide things about the data handler and authorize /
instruct him in different ways (data collection e.g.)
• Data sharing is a process where one data collection channel is used for
collection of data.
• Afterwards, data can be utilized for statistical and administrative purposes
in two different and segregated environments
3

4. Values at Statistics Denmark
Core Values
• Independence
• Trustworthiness
• Data protection
• User orientation
Values that DST wants to strengthen
• Adaptability
• Holistic approach
• Openness
4
Data sharing: data protection vs. openness

5. Current situation – the statistical purpose
• Universities deliver student record data to DST
• DST will work with data, fill out holes in the curricula, detect errors
and assign students to the correct university if present at two
institutions
• The Ministry of Research and Education will grant the funding and
finances to the universities on the basis of this corrected data.
• But the universities themselves are not allowed to see this data.
Because it may not be used for administrative purposes.
5

6. Data sharing – the idea
• The idea is to break the (legal basis for) data handling into a 2-step process
• A (generic) solution for datasharing processes where data security is not
compromised
• The model ensures segregation between data and access to the various
environments
• Can be enforced without a total explosion of license costs
• The solution establishes a separate environment where statisticians log in
and work with the specific data using a separate logon identity / role
6

7. Possible platforms & solutions
7
1: Internal statistical
environment at
Statistics Denmark
3: Segregated from
statistical production
Embedded in existing
research environment
Acceptable expenses
2: Totally separate &
segregated
environment
Rather costly
Not
recommended
by law firm
Recommended
by law firm
Recommended
by law firm
Firewall

8. Collection of data – a 2-step process
• DST will collect data from the Universities on behalf of and after
authorisation by the data owner – the Ministry of Research and
Education
• DST will carry out basic editing and error detection without using
back end data or existing back end systems – only expertise
• DST will deliver data back to Universites on a micro level. They can
use the data for administrative purposes since the data has not
changed into statistical data yet
• DST will also forward data into the statistical production line after
authorisation by the data owner as usual
8

9. Set up with DST in data processing and data
controlling function, respectively
9
DST as data
controller
DST as data
processor
The data
controlling
authority
1: Authority handles over data to DST for
administrative purposes – for data processing
2: DST delivers data back to Authority
3: Authority handles
over data to DST
for statistical purposes
1
2

10. Segregation of data flow + environments
University-1
Data
Collection
Corrected data
for the
universities
Administrative
data
Data for
Statistical
purposes
University-3University-2
Statistics Denmark as
producer of statistics
Statistics Denmark as
data processor on
behalf of data owner

11. Status and process
• The legal staff at the Ministry have approved this construction
• The Universities have agreed upon it. They are very eager to have
their corrected data back
• The set up is ready at the moment; the Ministry is evaluating it before
access to data is given to the universities
11

12. The three presentations in this slot:
The changing environment for official statistics
1. Data sharing
2. Official statistics as a safeguard against fake news
3. A quality framework for official statistics in Sweden
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What ‘bubbles up’ :
Quality
Accuracy
Clarity
'We know data'
Professionalism
Trustworthiness
Independence
Security

13. Thank you! Questions?
13