How to future-proof your AI-driven data strategy

Think of your enterprise AI strategy like a rocket. You can have the best design and the brightest minds behind it, but without the right fuel, it’ll never leave the launchpad.

The fuel that AI needs is data, and the good news is that enterprises certainly no longer have to worry about finding enough AI data. Now, it’s about getting the right data and using it in the right ways.

Datasets are growing, both in volume and complexity. We’re capturing data in ways we wouldn’t have thought possible before. Take manufacturing, for example: 15 years ago, factories tracked production levels and nothing else. Today, sensors are everywhere, streaming a constant flow of data. It’s a whole new world of possibilities.

This trend toward more data from more sources is happening across every industry, and it will continue into the future. The question is, how do we turn that data into business results?

AI provides the game-changer. I truly believe that AI will impact every enterprise, and that it will be just as disruptive as the internet was. AI has the power to transform everything about the way we live our lives. Exciting, right? But first, enterprises need a future-proof AI data strategy. That starts with deploying infrastructure on a flexible platform that’s positioned to support data growth.

Deploying in AI-ready data centers

We all know that AI requires data centers. But not all data centers are created equally, particularly not when it comes to AI.

To start, AI isn’t just one thing happening in one place: it’s a series of interconnected processes happening in locations throughout the world, with data constantly moving between them. Therefore, you don’t need an AI-ready data center; you need an interconnected platform of AI-ready data centers. Specifically, you may need different types of data centers for different AI workloads:

1. Hyperscale data centers are primarily used by service providers for training large language models (LLMs) that require extremely high compute capacity.
2. Colocation data centers are often used by enterprises for private models that use smaller training datasets.
3. Edge data centers are used for inference workloads, which need to be distributed close to data sources to keep latency low and ensure the most current data possible.

The right digital infrastructure partner brings it all together—hyperscale, colocation, and edge data centers—on a single global platform. Organizations can also access the network infrastructure needed to tie those data centers together and keep data moving between them.

AI-ready data centers are both powerful and interconnected

The need for powerful hardware to support AI workloads is a given. It’s also clear that enterprises would struggle to get that inside legacy on-premises data centers that weren’t built with the needs of AI hardware in mind.

In contrast, leading colocation providers have consistently invested in AI innovations for years, and they will continue to do so. Enterprises can capitalize on those investments just by deploying inside high-performance colocation data centers. This also saves them the cost and complexity of attempting to modernize their own data centers.

Being AI-ready also means ensuring connectivity and data access, which you can’t do if your data center is isolated. This is where reliable, low-latency network infrastructure comes into play. Again, this is something high-performance data centers provide, and conventional data centers do not.

AI takes more than just hardware

AI workloads have grown to require extreme power density, which means that an AI-ready data center must also provide advanced cooling capabilities. Liquid cooling is now required to support modern AI hardware like GPUs. This is yet another example of a technology commonly found inside high-performance colocation data centers.

Also, the energy requirements of AI workloads will further complicate sustainability efforts for many enterprises. It’s more important than ever to work with an operator that’s pursuing innovations in both renewable energy and data center efficiency.

Ensure scalable, secure connectivity

Reliable, high-performance network infrastructure is essential to move growing AI datasets quickly and securely, whether that means moving them between data centers or to ecosystem partners.

Most enterprises include multiple public cloud providers in their AI ecosystems, but they also rely on specialized partners such as GPU as a Service (GPUaaS) and data hosting providers. No matter who they’re connecting with, enterprises need to do it on their own terms. This means maintaining control over data to avoid latency and high egress costs.

Also, using direct interconnection instead of the public internet allows for higher performance and better security. You can move your data where it needs to go using a dedicated connection that’s not publicly accessible. Thus, you can make public cloud and XaaS part of your AI strategy while minimizing the risks.

Modernizing your multicloud architecture

With the right multicloud networking capabilities, you can ensure the flexibility to move workloads between clouds whenever the need arises. This is important, as your cloud needs will inevitably change over time.

To meet your compliance requirements, you’ll need control over where your data moves and who can access it. If you put your data into cloud native storage, it could mean giving up that control. Due to public cloud costs and complexity, you may find it difficult to get that data back out of the cloud, thus limiting your future infrastructure flexibility. Also, major public clouds are global by design, which means you likely can’t use them for workloads with specific data residency and sovereignty requirements.

Instead, deploying your own storage environment in proximity to multiple clouds and service providers allows you to access services on demand while also maintaining control over your data. This means that you can avoid vendor lock-in, meet your regulatory requirements, and ensure workload portability.

Scale network infrastructure as you scale AI datasets

As AI datasets grow ever-larger, scaling network infrastructure without sacrificing performance will be another essential aspect of a future-proof AI data architecture.

A software-defined interconnection solution such as Equinix Fabric® can help. It not only ensures high performance, but it also makes it easy to scale as needed. Instead of needing to add new physical connections, you’ll be able to add capacity to your existing virtual connections with the click of a button.

Meet data governance requirements

We’ve established that AI requires data, but it can’t be just any data. There’s a lot of talk about how to do AI in a more sustainable manner, but it’s not just environmental sustainability that matters. To make AI viable over the long term, responsible data management and consumption are essential.

This is something that both enterprises and regulators now seem to recognize. Data governance is essential to ensure AI transparency and comply with emerging regulations such as the European Union Artificial Intelligence Act.

Federated AI is a smart way to build your AI datasets while also meeting governance requirements. Essentially, this means connecting with ecosystem partners to exchange data on a neutral platform. All participants maintain control over their own data, while also tracking the full lineage of any AI data their partners share with them.

Edge inference pods help meet residency and sovereignty requirements

You need AI inference pods at the edge to ensure data is processed close to the source. In addition to limiting latency, this also helps meet your data residency and sovereignty requirements. You’ll get the control needed to ensure that inference happens within prescribed borders and thus avoid unexpected regulatory penalties.

But this doesn’t mean inference pods should be isolated. You still need them connected to the rest of your global AI infrastructure—just in a secure, controlled manner. Again, this is why it’s so important to choose a platform that offers the right data centers in the right locations, as well as the network infrastructure to tie them all together.

Capitalize on your data with private AI

You also need ways to make your sensitive data work for you in AI models without putting it at risk. This is where private AI can help.

Private AI means building models for your organization’s own use on private infrastructure that you control. Essentially, you’re placing your models close to the data source, so that you don’t have to move your data to the models. Thus, you can gain value from your proprietary data without having to move it outside your security domain.

The right colocation partner can help you tap into leading AI hardware providers to quickly deploy what you need to do private AI in all the right places. This can be especially helpful in highly regulated industries like healthcare, finance, and legal.

Start building your future-proof AI data strategy today

At Equinix, we help our customers turn their AI data dreams into reality. Only Equinix offers a global platform with different types of data centers, a dense ecosystem of clouds and service providers, and on-demand virtual interconnection services. Your future-proof AI data strategy begins here.

To learn more about building your future-proof AI data strategy, visit here.