The world is currently experiencing the impact of Artificial Intelligence (AI) in various industries, but its full potential remains unknown. However, we do know that AI has the capability to transform how organizations function across all fields by automating repetitive tasks and producing outcomes at a faster pace. AI, Machine Learning (ML), and deep learning have more complex and significant purposes beyond generative AI chatbots. Industries such as healthcare, finance, and retail can use AI to improve customer experiences, lives, and business outcomes. These technologies can learn from vast amounts of data, both past and future, which makes it an extremely powerful tool for finding value in large quantities of data. While all of these examples demonstrate the benefits of AI, the next era of data usage poses a challenge for IT leaders.
Enterprises face the challenge of working with exponentially growing data volumes, which are increasingly unstructured. It is more challenging to manage data in the form of videos or other forms of imagery. Legacy data storage systems are struggling with this vast quantity of data, which organizations need to train their growing AI/ML applications. Especially with the projected increase in AI/ML applications, the need for data will only increase, meaning legacy storage systems that weren’t built to manage data storage at this capacity will be unable to cope.
Organizations need to translate the raw data into usable data, which is data preparation, followed by model training, in which software programs are trained to learn. Finally, in the inference stage, the trained software is applied to new data. This cycle continuously generates vast amounts of data, which rapidly evolves as AI evolves. With new sources of data appearing every day comes a storage crisis, and applications that have not produced data before are now producing an astonishing amount. Unlike vacuum cleaners of the past, which were incapable of or had no reason to collect and store data, robot vacuums now collect and store data in the cloud, which has led to a storage problem for organizations.
For organizations to ensure they are keeping all possible data for the future, a storage solution is essential, even if the value of data is still uncertain in the present. The large datasets used for data preparation, as well as the datasets that AI, ML, and deep learning rely on to function, may need to be stored for years, and if models need to be retrained, datasets may need to be stored for even longer periods. Organizations require a storage solution that archives data inexpensively and offers an easy way to retrieve it, making data accessible for reuse if and when it becomes valuable in the future.
