Our new report explores administrative data in the Victorian legal assistance sector

July 9th 2020

We are pleased to announce the release of our latest report: Apples, Oranges and Lemons: The use and utility of administrative data in the Victorian legal assistance sector.

This is the first report in the Foundation’s data mapping project which examines how administrative data is collected and used in the Victorian civil justice system. The project investigates what data is available, its accuracy and consistency, how data is used and what needs to be done to improve its utility for access to justice questions.

In stage one, Apples, Oranges and Lemons: the use and utility of administrative data in the Victorian legal assistance sector we examined Victoria’s civil justice system data and invited Victorian legal assistance organisations to participate.

Our findings show that administrative data in the legal sector is used for many purposes in both useful and innovative ways, however there are challenges to using it system-wide for meaningful comparative analysis. Improving the consistency and accuracy of administrative data is likely to be beneficial in answering access to justice questions.

‘While there is undoubtedly much that needs to be done to improve the nature and quality of Victoria’s legal assistance data, we found a sector that had embarked on a data improvement journey, and was eager to learn the outcomes and impacts of their services,’ said Principal Researcher Dr Hugh McDonald

‘The movement to measure outcomes places a premium on data consistency and accuracy. Quality data also requires modern, fit for purpose data management systems that reflect the work and needs of services. Getting to quality data also requires leadership, collaboration and coordination. It does not happen on its own, said Research Director Professor Nigel Balmer.

Strategies to help unlock the utility of administrative data included:

  • Improved data quality and standardised practices.
  • Quality data requires leadership, collaboration and coordination
  • Quality data requires investment in people and time.
  • Quality data requires funding.