Source:- themandarin.com.au
Data science is increasingly important across both Federal and State Government sectors, especially with the implementation of machine learning. Machine learning is helping these sectors organise, program and analyse data, by automating processes and freeing up human resources that can be better applied in other areas.
A more specific subset of artificial intelligence (AI), machine learning builds a machine, device or program to analyse data and to continue learning a specific process.
Unlike the more generalised power of AI, machine learning is something that can be programmed to do its task without constant supervision, offering government sectors and agencies the potential to cut through bureaucracy and more tedious jobs.
Data scientists’ ability to implement machine learning into their work seeks to have a great impact in a variety of government departments, and will make a real impact on Australian lives.
Streamline processes
Machine learning helps a wide range of governmental projects by running automated processes to eliminate lengthy and tedious operations, such as email delivery and basic administrative tasks. However, even more beneficial possibilities are with data scientists using machine learning to streamline data processes and get better results.
This technology can also help data scientists run continued simulations and better understand the wide range of data sources available for analysis.On the other hand, it is vital that machine learning isn’t seen as a cure-all, and that it can’t just be plugged into government sectors to fix a problem. Human intuition and decision-making will not be entirely replaced by machine learning, but the processes that require decision-making will be augmented by the possibilities machine learning offers.
It allows for the quick collation and analysis of complex data
Machine learning is having a continued effect on how both Federal and State Governments are making their decisions.
The National Criminal Intelligence System (NCIS) is a prime example, with machine learning directly influencing crime prevention, being able to collate and track criminal data to be proactive rather than reactive with potential crimes.
Machine learning is also having an impact on the effectiveness of our emergency services. Data scientists are able to use these processes to implement public safety policies, preventative practices and improved internal management in crisis situations.
One example of this is the CSIRO’s bushfire toolkit called Spark. Computer models are used to predict bushfires, which help emergency management operations and timely issuing of warnings to those in danger.
Spark takes our current knowledge and data of fire behaviour and combines it with state-of-the-art simulations to produce bushfire spreads with predictions, statistics and visualisations. This provides results on the rate the fire is spreading, the locations it already affects, and where it’s going next.
By inputting data from multiple sources, including weather information and forecasts, fire location, vegetation and fuel in the area, as well as the terrain and land slope, Spark runs constant simulations via machine learning gives the government better scope to make informed decisions on bushfire management and prevention.This then helps with land management and infrastructure planning, and even more importantly, save properties and lives.
A high demand for data scientists
As the public sector embraces the power of data, it requires people with the right skill sets able to utilise its functionalities and understand the potential it has to impact the public sector.
With increasing expectations from the public and the need for governments to issue quicker responses to new issues, equipping employees with the capabilities to pave the way is more important than ever.