Artificial intelligence (AI) and generative AI (GenAI) will only become more popular in 2023. More and more companies are using the technology. A recent survey of IT decision makers in the UK, US, France and Germany found that 76% of respondents believe GenAI can make a difference for their organizations. According to McKinsey, GenAI can contribute between $2.6 and $4.4 trillion annually to the global economy.

However, GenAI brings with it a powerful data package. To build and train GenAI models, you need enormous amounts of information. These models, in turn, generate tons of data for the company. Every business leader must therefore ask themselves whether storage solutions are suitable for fully leveraging AI and GenAI.

In 2024 and beyond, a scalable, secure, and economically viable data architecture will be the difference between organizations that are at the forefront of the AI ​​race and those that are just lagging behind.

Storage solutions for the GenAI era

GenAI requires effective data management. Therefore, companies need to rethink and optimize their storage if they want to be successful with GenAI. In this way, they prevent processes from becoming more inefficient and slower due to inadequate or incorrectly designed storage.

Companies need to rethink and optimize their storage if they want to be successful with GenAI.

Koen Segers, Managing Director Dell Technologies Belux

Traditional storage systems today are struggling to keep up with the data explosion. As GenAI systems continue to evolve and take on new, more complex tasks, these requirements will only increase. Storage platforms must therefore be geared towards more unstructured data, also known as qualitative data, and the emerging requirements of GenAI.

Up to 90% of the data created each year is unstructured. This is largely due to the increase in human-generated data. Companies therefore need a new way to store this amount of complex data cost-effectively. At the same time, they must have easy and quick access to it while being protected from cybercriminals. Unstructured data is particularly interesting to hackers because of its value and enormous volume.

Companies simply expect better data movement, better access, better scalability and better protection. As a quick fix, many companies have turned to a cloud-first strategy, where data is stored in multiple public cloud environments. This represents a short-term solution, but in the long term, companies will face increasing input and output costs, security issues, and data optimization challenges. In order to really use GenAI effectively, it is important to have easy and quick access to data. And that’s difficult with a cloud-first strategy.

It is better for businesses to opt for a multi-cloud by design approach. In this way, they realize the full potential of multicloud in the short and long term, without being limited by siled ecosystems of proprietary tools and services. Multicloud is designed to ensure consistency in how data is stored, protected, and backed up across multicloud environments.

Investment in new storage technologies

Companies need a new and innovative approach that meets GenAI’s specific needs and large, diverse data sets. Some examples of these advanced technologies include distributed storage, data compression, and data indexing.

• Distributed storage improves the scalability and reliability of GenAI systems by storing data in multiple locations. Organizations can quickly scale their storage needs across multiple nodes and replicate their most important data so it can be stored in a separate location and easily accessed in the event of a cyberattack.

• Another important consideration for many organizations is cost. This can be partially remedied by Data compression. By removing unwanted data using data compression methods, companies can reduce their storage needs. This can be achieved by analyzing the data more effectively and removing unnecessary information to create a more summarized version. This reduces the storage space required by the organization and thus saves costs.

• Data indexing improves query capabilities and contributes to faster, more efficient search and training by organizing data into specific locations more effectively.

These three technologies improve performance, increase efficiency and reduce costs. Three of the top priorities for business leaders seeking a smooth transition to GenAI.

It’s tempting to adopt GenAI immediately and focus on effective training and modeling. However, to be successful, GenAI needs a solid storage base as a first step. This may not be the most exciting topic for business leaders. But the way companies store and manage data will drive greater business value in the future.

AI and GenAI are important tools for gaining an edge over the competition. But then they have to be used correctly. So don’t rush into the AI ​​race blindly and without warming up: first make sure that you are completely fit. There are huge opportunities ahead and those who do so with future-proof technology will be in the best competitive position to make the most of the benefits.

This is a contribution from Koen Segers, Managing Director Belgium and Luxembourg at Dell Technologies.