The ability to easily access data and leverage it in a meaningful way has always been important, but it has become absolutely essential in the age of AI, machine learning and data analytics. The global AI market, currently worth more than $390 billion, is expected to exceed $826 billion by 2030. However, this growth depends on the continued development of AI technology and the growth of applicable value. Implementation requires a lot of data. Global organizations have stored around 7.2 data on Zettabytes (ZB) in 2024, and the figure is expected to grow to 15.1 ZB by 2027. This significant increase emphasizes the need for reliable, accessible storage solutions that can address increased data demands.
From finance and healthcare to manufacturing and retail, the explosion of AI-powered applications has further accelerated the need for huge and well-prepared data sets. AI systems are thriving on data, using it to perfect algorithms, enhance prediction models, and optimize automation. According to leading companies such as IDC, the higher the quality data an organization has, the more effective it can improve the AI outcomes, enabling smarter decision-making and driving operational efficiency. However, the challenge is not only to collect and generate large amounts of data, but also to ensure its long-term retention and accessibility. Without proper storage solutions, businesses could lose valuable information that could impact the next wave of AI advancements.
The importance of data to AI
In order for AI to continue to move forward at its current pace, it must consistently improve efficiency and accuracy. The only way to achieve this is to provide continuous and high-quality data for AI models. The datasets used to train large language models (LLMs) have grown at an astonishing rate, tripling in size since 2010.
This rapid expansion of AI training datasets presents a major challenge: how to effectively store large amounts of high-quality data. As AI systems consume a lot of text-based data (including books, articles, and research papers), organizations may deplete high-quality human-generated materials. This could force AI developers to rely on AI-generated content for future training, leading to potential problems such as reduced accuracy, reduced creativity and increased repetition. To cope with this risk, organizations must prioritize retaining most of the data they generate, as it could become a valuable resource for future training AI models. This necessity drives the need for robust, scalable and long-term storage solutions.
Data analysis is a competitive advantage: AI without IA
AI-driven analytics has become the cornerstone of modern business strategies, enabling organizations to reveal patterns, predict trends, and make faster, smarter decisions. But while AI has attracted attention, it is easy to ignore the nameless foundation behind it: data. More specifically, the infrastructure for the data available for decades when and where it is needed – we now call it Information Archives (ia).
IAs a deep reservoir of organizational knowledge, it is often based on cost-effective scalable storage such as tapes. Where a lot of structured and unstructured data is preserved here is not only for compliance but also for the competitive advantage of potential innovation. When training an AI model is required, large datasets will temporarily pull it from this archive into a high-performance system. After the training is completed, the data will be returned to the IA for long-term retention. This access and saving cycle makes continuous AI development possible.
Organizations’ ability to make high-impact, data-driven decisions depends not only on the latest AI tools. It depends on whether you can access and retain the right information – by time, at scale and without sacrificing cost-effectiveness. Do a good job, data analytics can personalize the customer experience, simplify operations and quickly respond to changing markets. However, all this depends on a long-term data strategy that will collect information not as storage issues, but as strategic assets. The future belongs to organizations that regard their historical data as a survival resource, which will continue to grow with every AI-driven insight.
New Opportunities for Proven Technology
The surge in data-driven AI applications has introduced new demand for storage solutions. Organizations need a system that can store large amounts of data sets over the long term while ensuring accessibility, sustainability, and security. Additionally, with the increase in cyberattacks, global cybercrime costs are estimated to reach $10.5 trillion per year by 2025 – Data Security has become a key consideration for any storage solution. Many businesses may instinctively seek state-of-the-art newly developed storage technologies to meet these requirements. Because reliable storage is required NowHowever, organizations should consider prior art that has proven their reliability: tape storage.
Many established organizations have relied on tape storage for decades, even if newer cloud-native companies ignore it. But the revival of AI, machine learning and advanced data analytics provides new use cases for this attempted technology. Tape storage provides a powerful combination of scalability, flexibility, cost efficiency and security, making it the ideal solution for managing large numbers of AI and ML workloads. Unlike many other storage solutions, tape is very sustainable because it does not consume energy when storing data, which greatly reduces its carbon footprint. Furthermore, its offline capability provides additional protection for cybersecurity threats such as ransomware attacks, as data stored on tape is inherently not affected by remote violations.
Modern tape storage solutions have been developed to meet the needs of AI and data analytics. With the latest advancements in high-capacity tape technology, enterprises can store data at a fraction of the cost of traditional cloud-based solutions. Furthermore, the lifespan of a tape (usually over 30 years) can determine that an organization can retain valuable data sets without reducing the risk of the data. This makes it a very attractive option for businesses looking for a data infrastructure for the future while remaining cost-efficient.
AI and data revolution
The ongoing AI revolution is fundamentally a data revolution. In an increasingly data-driven world, organizations that cannot determine the risk of data storage and accessibility. More data equals more opportunities for innovation and competitive differences. By including scalable and secure storage solutions, including the re-potential of tape, organizations can ensure they are at the forefront of AI advancements and data-driven decision-making. As enterprises continue to navigate the complexity of AI-driven growth, those who recognize the importance of data retention and smart storage solutions will be solutions that thrive in a data-centric future.
