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Australia’s DTA announces 2nd round of data fellowships to improve government services

In Australia, Digital Transformation Agency (DTA) recently announced the second round of data fellowship for 2017 – 2018. Seven data specialists were selected to take part in three-month placements under DTA’s Data Fellowship Program.

As reported earlier, the data fellowship programme by the Australian Government targets current employees of the Australian Public Service who are interested in building data skills within their agencies. The selected high-performing data specialists will go through a three-month full-time placement to develop a solution for a data-related problem and help to improve government services.

During their placements, the 7 data fellows will work with the CSIRO’s data-innovation group Data61 and will develop solutions for data-related problems or opportunities.

The fellowships give public service staff the chance to step outside their day jobs and create prototypes that improve or completely redesign our approach to real-life problems, while their respective agencies will continue to pay their salaries, superannuation and entitlements, with all travel and accommodation costs reimbursed.

Participants come from a variety of government agencies. These fellowships give them access to mentoring, skills development and new and emerging technologies and techniques. The following are the seven projects that the data fellows will work on from now to mid-October:

(1)    Using text analytics to check financial condition reports at the Australian Prudential Regulation Authority. Text analytics will be used to help identify insights and risks in prudential supervisory reviews of insurance companies’ financial condition reports.

(2)    Developing techniques to automate land-use delineation through advanced geospatial data-analysis and modelling techniques. These techniques will combine satellite-derived information and various national datasets to help Australian Bureau of Agricultural and Resource Economics and Sciences improve methods for building the Land Use of Australia data series for understanding current and long-term changes in Australian land use.

(3)    Analysing government buying patterns and provide data about the way government agencies buy products and services and the sellers they buy from. Using longitudinal network analysis to look at datasets held by AusTender, the analysis will show the effects policy changes have on forming networks between government agencies and sellers.

(4)    Using machine learning to help the Clean Energy Regulator (CER) detect non-compliance in regulatory schemes. The effort aims to develop a process for the Small-Scale Renewable Energy Scheme to help the CER use its resources more efficiently and strengthen the integrity of Australia’s Renewable Energy Target.

(5)    Developing an empirical model to identify and target biosecurity risks at Australian airports. The model will help to find risk patterns and identify international travellers who carry a higher and potential non-compliance biosecurity risk.

(6)    Using machine-learning algorithms to improve survey accuracy for the Australian Bureau of Statistics. The models will improve the quality and efficiency of the address register held by the Bureau.

(7)    Simulation model for call-centre activities that will replicate the day-to-day telephone activities of the Department of Human Services. The project aims to show information including customer wait times, number of transferred calls, busy signals, level of staff occupancy and more.

Other than training on advanced data skills, data fellow will also be part of a bigger alumni network after completing the placement to share learnings and experience.

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