05 April 2024
Tim Sherbak, enterprise products and solutions, Quantum
AI’s influence is pervasive and undeniable. It is weaving its way into every industry sector and reshaping the fabric of our daily lives. From chatbots that can solve customer queries within minutes to autonomous vehicles and facial recognition, this ever-advancing technology is helping propel society towards a new future.
Yet, for its full potential to shine, robust infrastructure to support high-performance AI workloads and computing is paramount. AI requires high-calibre systems designed to bear the weight of intense workloads and store the vast amounts of data required for AI model training. A computer can only ever learn from the data you give it – the more data, the ‘smarter’ the machine. One of the biggest challenges is storing and managing this expanding volume of data. Deleting data is simply no longer an option because organisations never know when it will be useful in the future. Before widespread AI usage, this level of retained massive data sets for analysis primarily existed in high-performance computing (HPC) environments. However, the advancement of AI has now brought this infrastructure challenge to any organisation wanting to capitalise on the potential of AI, essentially turning every kind of organisation into a HPC environment.
Organisations now find themselves juggling multiple complex storage needs. While high performance storage is essential to power AI workloads and deliver real-time analytics and processing using what is known as ‘hot’ data, organisations also must retain massive data sets for extended periods of time. Therefore, they need to find a way to retain that ‘cold’ data in a cost-effective way. With data growth and analytics not slowing down any time soon, organisations need to find that solution sooner rather than later.
New data for a new world
The dialogue is shifting: organisations are transitioning from asking “what data do I have?” to a more discerning “what data do I need?” Analysis, repurposing, and AI model training are all now front and centre in an organisation’s data priorities. It’s critical therefore to find a storage solution that can make the process of searching massive data stores simple, giving organisations the ability to index, tag, and catalogue data, making it easy to find, enrich, and repurpose the data they need to drive business forward and power AI applications. Tagging, cataloguing, and indexing data enables organisations to easily search and find a clip they need in their archives to repurpose for a highlight reel, watching game film, or any other new purpose.
Many are struggling to work at this level. In response, they should adopt data storage and data management capabilities that scale along with their different and ever-changing needs in a cost-effective, efficient way. It’s critical that organisations employ an end-to-end solution that can deliver the performance required for AI workloads and immediate analysis along with an effective way to move that data easily to a lower-cost, secure solution. In doing so they can retain that data for years or even decades, where it can be accessed for repurposing and analysing once again across the data lifecycle.
Prepare for the unknown
Data is currently evolving at an equal rate to the organisations which are producing it – nothing stands still in today’s tech landscape. Recent advancements in technology and the proliferation of AI have underscored the importance of readiness for the unknown. It has become imperative for enterprises to contemplate innovative storage and infrastructure strategies that align with the diverse facets of dynamic data.
These strategies must encompass the entire spectrum of necessities, ranging from high-speed data retrieval to the enduring safekeeping of archival information, which demands an approach that not only caters to present organisational requirements but also possesses the scalability and flexibility to accommodate the unpredictable demands of the future.
With the changes that organisations are experiencing, it’s crucial that the ways in which data is being used and stored adapt to provide the best experience to match. If the past year of tech and public AI development has taught us anything, it’s to be prepared for whatever may come next. Organisations must be considering storage and infrastructure solutions that store, manage, protect, enrich, and archive that data across a continuum of needs, from high-performance storage to long-term archiving in the most seamless, cost-effective way.
For this, organisations need a solution that has endless potential. It must be able to deliver the storage needs of today, as well as have inbuilt capabilities to adapt as required for changing needs in the coming years and decades.
As we continue to see AI develop in ways that, right now, seem unimaginable, organisations will rely on flexible, scalable infrastructure solutions that can enable them to utilise their data to its full potential and drive their business forward.
