1. Administrative Data Challenges: Joining Forces A view from the UK
DIME – June 15, 2018

2. Vision and ambition Shared vision with other NSIs:
To make better use of administrative and transaction data
To do this, and to ensure statistics can be transparent and reproducible, we need to develop new methods: catalyse a paradigm shift.

3. Administrative Data Challenges: Joining Forces
GSS Methodology Advisory Committee Extraordinary Meeting –28 February 2018, London
68 key stakeholders and experts in administrative data participated in a parallel strand workshop, discussing the challenges identified by Prof David Hand in Statistical Challenges of Administrative and Transactional Data

4. The Challenges summarised…
Challenge 1. Statistics teaching should cover data quality issues
Challenge 2. Develop detectors for particular quality issues.
Challenge 3. Construct quality metrics and quality scorecards for data sets.
Challenge 4. Audit data sources for quality.
Challenge 5. Be aware of time series discontinuities arising from changing definitions.
Challenge 6. Evaluate the impact of data quality on statistical conclusions.

5. The Challenges summarised…
Challenge 7. Explore potential sources of non‐representativeness in the data.
Challenge 8. Develop and adopt tools for adjusting conclusions in the light of the data selection processes.
Challenge 9. Explore how suitable the administrative data are for answering the questions. Identify their limitations, and be wary of changes of definitions and data capture methods over time.
Challenge 10. Report changes and time series with appropriate measures of uncertainty, so that both the statistical and the substantive significance of changes can be evaluated. The measures of uncertainty should include all sources of uncertainty which can be identified

6. The Challenges summarised…
Challenge 11. Be aware that administrative data are observational data, and exercise due caution about claiming causal links.
Challenge 12. Be aware of the risks that are associated with linked data sets and the potential effect on the accuracy and validity of any conclusions. Recognize that quality issues of individual databases may propagate and amplify in linked data. Develop better measures of overall combined data quality.

7. The Challenges summarised…
Challenge 13. Continue to develop statistically principled and sound methods for record linkage and evidence assimilation, especially from non‐structured data and data of different modes.
Challenge 14. Develop improved methods for data triangulation, combining different sources and types of data to yield improved estimates.
Challenge 15. Continue to explore anonymization and deidentification methods.

8. Translating the challenges to workstreams
Development of frameworks for measurement and reporting of quality in administrative data
Representativeness/Design of structured data collection to measure errors
Record linkage
Estimation
Missing values: Imputation/adjustment
Disclosure control

9. Development of frameworks for measurement and reporting of quality in administrative data  
Strand 1
Challenge 1
Statistics teaching should cover data quality issues
Challenge 2
Develop detectors for particular quality issues
Challenge 3
Construct quality metrics and quality scorecards for data sets
Challenge 4
Audit data sources for quality
Challenge 5
Be aware of time series discontinuities arising from changing definitions
Challenge 6
Evaluate the impact of data quality on statistical conclusions
Challenge 9
Explore how suitable the administrative data are for answering the questions. Identify their limitations, and be wary of changes of definitions and data capture methods over time
Challenge 11
Be aware that administrative data are observational data, and exercise due caution about claiming causal links.

10. Representativeness/ Design of structured data collection to measure errors
Strand 2
Challenge 7
Explore potential sources of non-representativeness in the data
Challenge 8
Develop and adopt tools for adjusting conclusions in the light of the data selected
Challenge 9
Explore how suitable the administrative data are for answering the questions. Identify their limitations, and be wary of changes of definitions and data capture methods over time

11. Record linkage Strand 3 Challenge 12
Be aware of the risks that are associated with linked data sets and the potential effect on the accuracy and validity of any conclusions. Recognise that quality issues of individual databases may propagate and amplify in linked data.
Challenge 13
Continue to develop statistically principled and sound methods for record linkage and evidence assimilation, especially from non-structured data and data of different modes.

12. Estimation Strand 4 Challenge 6
Evaluate the impact of data quality on statistical conclusions
Challenge 8
Develop and adopt tools for adjusting conclusions in the light of the data selected
Challenge 10
Report changes and time series with appropriate measures of uncertainty, so that both the statistical and substantive significance of changes can be evaluated. The measures of uncertainty should include all sources of uncertainty that can be identified.
Challenge 14
Develop improved methods for data triangulation, combining different sources and types of data to yield improved data sources.

13. Missing values: Imputation/adjustment
Strand 5
Challenge 8
Develop and adopt tools for adjusting conclusions in the light of the data selected
Challenge 9
Explore how suitable the administrative data are for answering the questions. Identify their limitations, and be wary of changes of definitions and data capture methods over time

14. Disclosure control Strand 6
Challenge 15
Continue to explore anonymization and de-identification

15. What next?
Establishing an Admin Data Methods Branch in the Methodology Division.
Incorporating the workstreams into a plan.
Developing expertise: blended approach to solutions: in-house, commissioned, developed in partnership (loosely modelled on ESCoE).
Publication to ensure credibility.

16. Any questions?

17. Appendix

18. Admin data experts University of Edinburgh University of Bristol
Liverpool University
University of Huddersfield
University of Leicester
Queens University, Belfast
Swansea University
University of Surrey
Lancaster University
Valtech
Oxford University
National Centre for Social Research
SAIL, Swansea
Imperial College London
Demographic
Queen Mary University, London
Southampton University
University of Leeds

19. Admin data experts cont.
Greater London Authority
Department for Work and
Office for National Statistics
Pensions (DWP)
NHS Digital
Department for Existing the EU (DExEU)
Ministry of Justice(MoJ)
ISTAT
UK Statistics Authority
Department for Business, Energy and Industrial Strategy (BEIS)
NRS Scotland
Her Majesty Revenue & Customs (HMRC)
Department for Education (DfE)
Local Government Association
NISRA
Statistics Norway