Covering Disruptive Technology Powering Business in The Digital Age

Attributed to: Henry Kho, Area Vice President and General Manager for Greater China, ASEAN and South Korea at NetApp

Henry Kho, Area Vice President and General Manager for Greater China, ASEAN and South Korea at NetApp

On AI Appreciation Day (16 July), we celebrate the amazing achievements that have been made possible by artificial intelligence (AI) – from generating new, quality content with GenAI, to detecting cyber-attacks, to powering diagnostics and drug discovery in healthcare.

Singapore emerged as one of the leading AI economies in NetApp’s 2024 Cloud Complexity Report, with 57% of technology decision-making respondents here already having AI projects up and running or in pilot. As organizations gear up for the AI revolution, it is important that they lay the groundwork necessary for AI success.

AI Projects Need Intelligent Data Infrastructure

Data underpins all AI processes. In other words, AI success is very much dependent on your data infrastructure. The recent Scaling AI Initiatives Responsibly survey by NetApp and IDC identified the top reasons for AI initiatives failing: 

1. Inability to access data 
2. Insufficient data to train models 
3. Privacy, compliance, and data governance concerns or requirements 
4. Data engineering complexity 
5. Untrustworthy or poor-quality data sources 

AI transformation is often hindered by the inability to access scattered data in siloed storage infrastructure — so it’s harder for AI engineers to train and develop models.

Organizations should adopt an intelligent data infrastructure that supports the preparation, movement, analysis and use of data across on-premise and hybrid cloud environments. Such seamless integration makes data sources for machine learning and AI readily available, regardless of their location. 

AI also isn’t just about algorithms and models; it’s about trust, transparency, and the ethical use of data. Responsible AI is a method for creating AI algorithms that minimize sources of risk and bias throughout the AI lifecycle. To establish effective data governance and strong data practices, companies should consider four key principles: fairness (ensuring unbiased results), interpretability (ensuring data traceability), privacy (maintaining data confidentiality), and security. 

Apart from the above, organizations must heighten their cyber-resilience capabilities, including having appropriate AI/ML-embedded storage technologies to combat ever-evolving cyber threats like ransomware.

Building an AI-Ready Future

The potential of AI is limitless, and we are excited to be at the forefront of this transformative journey. By combining intelligent data management with the power of AI, organizations will be empowered to unlock new possibilities, drive growth, and shape a brighter, smarter future for all. 

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