A cloud-neutral AI and analytics platform key to enabling whole-of-government digital transformation

By Red Hat and SAS

The ability to deploy AI and analytics across different cloud and on-premise environments can help to effectively scale digital transformation across the government, says representatives from Red Hat and SAS.

Representatives from Red Hat and SAS share how government agencies can adopt integrated AI and analytics solutions that account for complex cloud environments. Image: Canva.

Asia-Pacific governments like Singapore, India, and Australia are adopting a hybrid multi-cloud cloud strategy: combining the best of both private and public cloud environments to ensure access to leading cloud solutions.


But a hybrid multi-cloud strategy can lead to disjointed efforts, particularly when agencies adopt solutions that can only operate within one cloud environment.


Today, government agencies are increasingly looking at AI and analytics platforms to drive digital government efforts – and they will have to be strategic.


GovInsider speaks to Jason Loh, Head of Technology Futures, Global Technology Practice, SAS Institute, and Gunasekharan Chellapan, General Manager, Red Hat Singapore, to find out how agencies can adopt integrated AI and analytics solutions that account for complex cloud environments.

An AI and analytics platform for government


For government agencies looking to implement an AI and analytics platform across complex IT environments, they can consider tapping on open-source solutions that are built to run on any cloud platform.


For instance, AI, analytics and data management platform, SAS Viya, is now delivered on Red Hat OpenShift. The integrated solution supports AI and analytics anywhere, including on-premise, public and private cloud environments.


This means that regardless of where different agencies are at in their cloud journeys, they can continue to work with other agencies on a common platform to convert AI and consolidated data into actionable insights centered around the citizen.


As the demand for digital public services continues to grow, government agencies are expecting their software to be agile and robust, says Loh.


The platform has been optimised for the massive data and workload demands of government agencies, he adds. Also, it is automated to ensure that the software stays secure and scalable.


Automation is also deployed in SAS Viya to simplify routine tasks and enhance overall decision-making through real-time data analysis.


To personalise citizen services without sacrificing data privacy, SAS uses synthetic data generation, which are artificially generated datasets that mimic real data while safeguarding individual privacy, to account for individual citizen profiles and behaviors, says Loh.

Case study: Shifting away from legacy systems


Like government agencies, commercial organisations face similar operational challenges and can learn from examples like the United Overseas Bank (UOB), says Loh.


UOB’s retail credit management team faces challenges like handling multiple data sources, manual processes, and a lack of real-time customer insights.


“Legacy platforms that don't support AI and machine learning hinder sophisticated risk management,” he adds.


Implementing SAS Viya on Red Hat OpenShift for UOB addresses these challenges by providing a unified data source and automation.


“This integration enhances customer insight-driven risk management using advanced analytics, optimises collections through automated customer segmentation and treatments, and promotes data democratisation, fostering a culture driven by data and analytics," he explains.

Five reasons to adopt a cloud-neutral AI platform


“The partnership between SAS Institute and Red Hat is further testament to how AI enabled platforms are better at supporting AI applications,” says Chellapan.


He highlights five guiding pillars for governments to consider when leveraging AI across diverse and evolving environments.


The key pillars include:

  1. Bring AI to your data, not your data to AI: Rather than centralise all data in a common data portal – which can lead to higher risk – emphasise on a consistent AI layer across a mix of cloud and on-prem environments,
  2.  Optimise your AI, minimise your costs: Pick one AI tool that can run across all platforms, rather than a variety of vendor-specific AI tools to minimise costs and optimise investments towards talent and operational costs,
  3. Future-proof innovation: Pick a cloud-agnostic, open-source platform to provide flexibility to the agencies. Instead of being locked-in to vendors, this can allow pivoting to new technologies as they emerge regardless of solution provider or cloud platform,
  4. Unify AI and app development: A comprehensive AI platform enables agencies to unify AI tools and apps. It simplifies workflows while empowering developers with the ability to scale and manage AI applications easily,
  5. Build trust with transparent ML supply chains: By using open-source trusted software supply chain principles, government IT leaders can peek under the hood and truly understand the components that make up its AI and analytics tools.

Asia-Pacific digital governments like Singapore, Australia, and India have adopted whole-of-government analytics and AI platforms to support agencies in providing more efficient, personalised and seamless services to citizens.


However, complex IT environments, including hybrid cloud and interoperability barriers, are barriers to achieving that.


Attend this webinar on 13th June, from 11.00am - 12.15pm (SGT), to hear from government tech leaders and experts from Red Hat and SAS as they discuss key considerations and best practices for developing and implementing whole-of-government analytics and AI platforms.

Go to the event page here >>>