Capacity and Efficiency Innovations Crucial for AI’s Storage Demands

Artificial Intelligence (AI) holds immense potential for Australia's economy, with projections suggesting it could generate $280 billion in economic benefits by 2030. The transformative capabilities of AI are crucial to the "Future Made in Australia" agenda, fostering efficiency, innovation, and sustainability across various sectors, particularly in manufacturing. However, the extensive use of AI demands vast amounts of data, and the raw data requirements are accelerating beyond the capacities of current legacy environments. Both enterprises and hyperscalers must innovate to scale sustainably within the limitations of space, power, and budget to prevent the progress of AI from faltering.

Data has always been the cornerstone of innovation, even before the rise of generative AI. According to Seagate's Multicloud Maturity Report, organizations with advanced data storage and management capabilities can introduce new solutions six times faster than those with lower levels of data maturity. The challenge lies in achieving this level of maturity amidst rapid data acceleration. Two primary factors contribute to this challenge. First, the sheer volume of raw data is increasing exponentially. Seagate estimates that generative AI (GenAI) alone will generate 100 zettabytes (ZB) of data within the next four years. This massive volume requires robust data handling capabilities.

Second, the way AI is used fundamentally changes how organizations need to manage their existing data. Rosalina Hiu, Seagate's Global VP of Brand Strategy, explains that AI necessitates longer retention periods for data because AI systems can "hallucinate," or produce misleading outputs if not properly managed. Maintaining a historical record of data is essential to verify the authenticity of new information, creating additional storage pressures. Since future AI tools will likely extract unprecedented insights from past data, organizations need to retain as much data as possible.

However, the high costs of data retention often force companies to delete data prematurely. No company has an unlimited budget, and while some GenAI applications require fast processing speeds, data preparation and training benefit from tiered storage systems. The cost of flash storage is significantly higher than that of hard drives, with a price ratio of at least 6:1 per terabyte. Hyperscalers, for instance, store 90% of their data on hard drives, a model likely mirrored by enterprises. At Seagate, the reliance on hard drives is even higher, with 99% of data stored on them.

To manage the immense data demands of AI, organizations must adopt strategic approaches to data storage and management. Implementing tiered storage systems can optimize costs and performance. Critical, frequently accessed data can be stored on faster but more expensive flash storage, while less frequently accessed data can be stored on cost-effective hard drives. This approach balances the need for speed in AI applications with the financial constraints of large-scale data storage. Additionally, organizations should invest in advanced data management practices that enhance data accessibility and reliability. This includes implementing robust data governance frameworks, ensuring data integrity, and maintaining comprehensive metadata records to facilitate efficient data retrieval and analysis.

Developing sustainable data retention policies is also crucial. Organizations need to determine the appropriate retention periods based on the type of data and its potential future utility. Retaining data for longer periods, or even indefinitely, requires careful planning and investment in scalable storage infrastructure. Collaboration with technology partners specializing in data storage and management can provide valuable insights and solutions. Companies like Seagate, with extensive experience in data storage, can offer tailored solutions that meet the specific needs of enterprises and hyperscalers.

The future of AI is intricately linked to advancements in data storage and management. As AI technologies evolve, the ability to efficiently store and process vast amounts of data will become increasingly critical. Organizations that invest in robust data infrastructure and innovative storage solutions will be better positioned to leverage AI's transformative potential. This means that the potential economic benefits of AI for Australia are substantial, with projections of $280 billion by 2030. However, realizing this potential requires addressing the significant data challenges associated with AI development.

Organizations must adopt sustainable and strategic approaches to data storage and management, balancing the need for extensive data retention with financial and operational constraints. By investing in tiered storage solutions, advanced data management practices, and sustainable retention policies, enterprises and hyperscalers can effectively navigate the new era of AI and drive innovation, efficiency, and sustainability across industries.