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H2O.ai > Case Studies > H2O.ai empowers New South Wales Government To Deliver Exceptional Services for its Citizens with AI
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H2O.ai empowers New South Wales Government To Deliver Exceptional Services for its Citizens with AI

Technology Category
  • Analytics & Modeling - Machine Learning
Applicable Industries
  • Cities & Municipalities
Applicable Functions
  • Business Operation
Use Cases
  • Predictive Quality Analytics
  • Predictive Replenishment
Services
  • Data Science Services
The Challenge
The New South Wales (NSW) Government wanted to build out its data practice and initiatives. They needed to enable its analysts to draw upon data science and automatic machine learning platforms to help find answers, pinpoint solutions and use data to create better services for all. The government was looking for a solution that could improve the accuracy of its predictive models and empower its team of data scientists to build models faster.
About The Customer
New South Wales is a major global and Asia-Pacific cultural hub. Home to a diverse population of 7.7 million people, NSW is Australia’s oldest and largest state. It is also an economic powerhouse, with an economy larger than Hong Kong, Malaysia, and Singapore. The NSW Government is committed to promoting the use of data and information to improve its services and to benefit its citizens.
The Solution
The NSW Department of Customer Service used H2O.ai to improve the accuracy of its predictive models and empower its team of data scientists to build models faster. This allowed them to deliver more personalized and targeted programs to the citizens of NSW with improved services, policies and innovations. Programs such as the Active Kids program, which supports families by providing vouchers to put towards sport and active recreation costs each year, can be better supported with Driverless AI because it can now predict factors that cause families to miss out on these activities, target these factors and support increased participation.
Operational Impact
  • Accelerated the speed to build models
  • Achieved higher prediction accuracy
  • Enabled more personalized and targeted programs

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