The document discusses the use of data analytics to enhance public expenditure management in government, emphasizing its role in achieving policy goals amidst rising citizen expectations. It outlines the process of data analytics, highlights a hypothetical benefit payment policy change, and demonstrates the implications of such changes on various income groups. Additionally, it addresses the importance of building capabilities, data sharing, and the future potential of real-time data integration for improved decision-making.

1. 1
Using Data Analytics to
Improve Public
Expenditures
Rosemary Huxtable PSM
Secretary – Australian Department of Finance

2. 2
What is Data Analytics?

3. 3
What is big data?

4. 4
What is data analytics or data science?

5. 5
Global Challenges –
Governments are not
immune

6. 6
Citizen expectations have never been greater

7. 7
Governments are being asked to do more with less

8. 8
How can data analytics help
governments achieve policy
goals?

9. 9
Data Analytics is the process of using data for
interpretation, discovery, and to provide understanding

10. 10
Why 100% samples matter

11. 11
Let’s look at a HYPOTHETICAL policy change:
Policy scenario: A benefit
is being paid to families
on a per child basis, but
the payment is going to
stop using a complex
two step withdrawal
method.

12. 12
Let’s look at a HYPOTHETICAL policy change:
Policy scenario: A benefit
is being paid to families
on a per child basis, but
the payment is going to
stop using a complex
two step withdrawal
method.
Proposed policy change:
benefit paid with a single
withdrawal rate, instead
of the complex two step.
How will this affect the
recipients of this
payment?

13. 13
Let’s look at a HYPOTHETICAL policy change:
Here are all the
recipients based on
the original policy
scenario…
RUN
SIMULATION

14. 14
Let’s look at a HYPOTHETICAL policy change:
Here are all the
recipients based on
the original policy
scenario…
And now here is the
output based on the
proposed policy
change – but how
can we tell what the
change has been?
RUN
SIMULATION

15. 15
Let’s look at a HYPOTHETICAL policy change:
Lots of low income
families would
receive less
payments
Many more high
income families
would receive more
payments
Cross-over among
middle income
families

16. 16
What are the preconditions
for effective data analytics?

17. 17
The Department has invested in five key capabilities to
build a data analytic and visualisation function
hardware
software
dataculture
people data
analytics

18. 18
Hardware and Software – our Data Analytics Platform
provides cutting edge tools and visualisation software

19. 19
People and Culture – having the right staff with the right
skills to find solutions people will listen to

20. 20
Access to Data – our key challenge – building effective
relationships and good governance works

21. 21
The benefits of sharing data
and analysis within
government

22. 22
Ensuring costings of new policies are free of errors and
incorrect assumptions
'Missing' incomes are imputed using the 'uniform_high' setting.
If the customer is not on income support, we assume they have their income
unreported because they are above the zero rate threshold of CCB. We
distribute their income uniformly across $175404 to $450404.

23. 23
Discovering new insights that provide better alternatives
Service Ratio

24. 24
Enhanced policy advice is leading to better government
decision making.

25. 25
Operational rules and ongoing dialogue between
agencies is making data sharing the new “normal”

26. 26
Where to next?

27. 27
Imagine a future where real-time data can be blended
with historical knowledge to give better insight

28. 28
Don’t miss the opportunity