Immigration inquiry sees need for data analytics boost

Machine learning could help government fine-tune skilled migration settings, Productivity Commission says

A Productivity Commission inquiry has recommended the government consider boosting its investment in data analytics capabilities, including machine learning, in order to strengthen the evidence base available to inform immigration policy.

The PC’s report on Migrant Intake into Australia was released publicly today after being handed to the government in April.

A “stronger evidence base is required to inform future immigration policy,” the report argued. “This requires further investment in data collection, integration and dissemination, and data analytics capacity. “

The inquiry found shortcomings in data collection and analysis in several areas.

“Data deficits constrain the systematic measurement of immigrants’ outcomes over the long term, and the associated community-wide impacts,” the report stated. “This information is important to support the development of policy that seeks to maximise the benefits and minimise the costs of immigration. Data and other forms of evidence that are transparent and publicly available also have a role to play in enhancing community understanding and enabling a more informed community and political discourse about immigration.”

The nation is “making some progress in developing and providing access to linked government administrative data,” the PC argued. “The recent integration of immigration data with tax data is an example of such initiatives and has been particularly helpful to this inquiry. There is, however, scope to do more to better understand the outcomes of permanent and temporary immigrants.”

Revamped skilled migration system

The commission argued that Australia’s current skilled migration program “falls short of generating the best outcomes for the Australian community more broadly” by failing to target migrants “who have the potential to make the greatest economic contribution” and setting a lower bar for migrants sponsored by employers than those who apply through the independent points test.

The inquiry recommended a move to a new “coherent universal points-based system”. A data-driven approach could over time help fine-tune a universal points-based system.

“The data sets underpinning this kind of analysis would be very large and of high quality, and grow even larger over time,” the report argued.

It said government should follow the lead of organisations such as Coles, Woolworths, Medibank Australia, the Commonwealth Bank, Telstra and Australia Post and employ techniques such as machine learning to deal with the large data sets involved.

The Department of Prime Minister and Cabinet (DPMC) recently launched a strategy that seeks to boost the data analytics capabilities of the public service. The four-pillared strategy includes a number of measures to boost both access to specialist skills and general data literacy within the public sector.

Join the newsletter!


Sign up to gain exclusive access to email subscriptions, event invitations, competitions, giveaways, and much more.

Membership is free, and your security and privacy remain protected. View our privacy policy before signing up.

Error: Please check your email address.

Tags governmentbig datadata analyticsmachine learning

More about Australia PostCommonwealth BankMedibankProductivity CommissionWoolworths

Show Comments