Data quality aspects of administrative data (London)

Date: 6 October 2026.

Location: Room 6.03 A&B (6^th floor), UK Research and Innovation (UKRI), 157-197 Buckingham Palace Road, London SW1W 9SP

PLEASE NOTE: This course is also taking place in the following locations:

• Swansea: 14 April 2026 – REGISTRATION CLOSED
• Glasgow: 28 May 2026 – REGISTRATION CLOSED

Duration: 09:00 to 17:00, including breaks.

Learning mode: The course will be a mixture of four hours of interactive presentations (containing small practical exercises) plus two one-hour sessions with group discussions. Additionally, participants will be sent links to watch two introductory/primer videos (approximately 1.5 hours total) prior to attending the workshop.

Course delivered by

Course description

As researchers and practitioners working with administrative data, we are often given data sets where we do not know the full provenance about how this data set was captured, what kind of processing has been applied to it, and if it has been linked or merged with data from other sources. Complete and up-to-date metadata are not always available. Not fully understanding the provenance of a data set can lead to assumptions and misconceptions being made about the content and quality of the data set. This can result in incorrect processing and / or analysis of a data set which potentially can lead to bad outcomes and decision making.

This course will provide an introduction to data quality and its impact on all aspects of working with administrative data, and will cover:

1. Data quality dimensions, including:

• • Technical considerations
  • Social factors

2. Frameworks used to assess or quantify data quality.
3. Real-world examples and case studies showing how poor data quality can harm data science projects.
4. A focus on awareness rather than technical steps:

• • Not centred on data cleaning, processing, or linkage
  • Instead highlights key issues practitioners should consider

5. Practical recommendations for improving data quality awareness.
6. Interactive sessions where participants can share experiences of how data quality challenges have affected their own projects.

Who should attend?

This one-day course is aimed both at researchers and practitioners who are working with administrative data, as well as those who are involved in the management of data centric systems in organisations that act as data custodians, or who are involved in the capture, processing, and linkage of data that potentially will be used for administrative data research. The course requires little technical knowledge, and all technical background will be introduced during the course.

The course will be a mixture of four hours of interactive presentations (containing small practical exercises) plus two one-hour sessions with group discussions.

Skills & competencies

(View the Datacise Skills Framework here)

Data Stewardship and Standards
1. Incorporates the FAIR Guiding Principles for scientific data management and stewardship into practices, where appropriate and practicable.

Data Collection and Management
1. Source and access data appropriate for the problem.
2. Critically analyse the availability of appropriate data and resources to meet project requirements.
3. Critically evaluate and synthesise data.
5. Identify data characteristics (volume, velocity and variety).
7. Show familiarity or experience with tabular and non-tabular data (e.g. unstructured and streaming data).

Data Engineering
1.  Source and access data appropriate for the problem.
2. Construct data sets, potentially drawing from multiple disparate sources using data linkage.
3. Perform data profiling and characterisation to understand the surface properties of the data.
4. Handle missing data, through principled inclusion/exclusion criteria and/or imputation methods.
5. Take a systematic approach to data curation and the application of data quality controls.

Data Preparation and Feature Modelling 
1. Identify appropriate solutions, including statistical and machine learning approaches.
2. Identify and evaluate appropriate evaluation metrics, including computational performance and accuracy.
3. Manipulate data with due regard for differences in characteristics.
4. Creation and evaluation of new data features.

Data Analysis and Model Building
3. Adopt a systematic approach to exploratory data analysis to embrace and manage ambiguity and uncertainty.
4. Critically analyse data and analytical results.
5. Adopt appropriate methods to visualise data and communicate complex findings.

Ethical Behaviour
1. Identify and manage the risks of erroneous and biased data.
2. Uphold principles of ethical and safe use of data and AI technologies

Taught by

Peter Christen is the Research Lead on the Scottish Historic Population Platform (SHiPP) project, run at the Scottish Centre for Administrative Data Research (SCADR) at the University of Edinburgh. He is also a Professor at the School of Computing at the Australian National University in Canberra. Peter is a world-leading expert in record linkage with over 20 years’ experience in working with administrative data. He has over 200 publications in the area of data science, including the two books “Data Matching” in 2012 and “Linking Sensitive Data” (co-authored with Thilina Ranbaduge and Rainer Schnell) in 2020. As of February 2025, his work has attracted nearly 18,000 citations at Google Scholar.