The five challenges to preparing data for AI

Having trouble with your AI initiative? You might be looking in the wrong place for answers. Rather than blaming your LLM, consider your data preparation. Bad planning kills more AI projects than bad algorithms. More often than not, the main stumbling block for badly prepared AI projects is data storage that can't keep up.

AI is a glutton for data, and most enterprises can't shovel it fast enough. Their storage is choking on petabytes of training sets and AI inference flows that they can't get through the pipeline.

Five challenges dominate the data collection and preparation stage of an AI pipeline. Each feeds the other, creating a death spiral that kills AI ambitions before they start. Let's look at them in order.

1.    Storage capacity

The biggest problem is also the simplest: a lack of space. ESG research has found that nearly half of IT teams rank capacity as their worst storage nightmare. A single LLM training run might dump 50TB of data. Traditional SANs weren't built for that punishment.

This is partly why 83% of respondents to ESG's survey plan storage upgrades within twenty-four months to support AI demands.^[1] They've learned what happens when storage can't keep pace with AI ambitions.

You have options here beyond simply adding more storage nodes. Choosing a vendor with solid compressing and deduplication technology will get you a long way. Modern vendors guarantee 5:1 data reduction without assessment.

Another valuable solution here lies in procurement. Flexible purchase models mean you can install storage but pay only for what you use. That stops you from over-provisioning while preserving capacity margins.

2.    Security and compliance

Twenty-four per cent of IT teams rank security as their second-biggest AI data preparation headache, right after capacity constraints. Every AI dataset is a ransomware target. Modern storage systems counter with immutable snapshots and file-level retention, which are proven defenses when ransomware strikes.

Compliance requirements compound the challenge. European data must stay in Europe. Healthcare demands HIPAA compliance, while financial services frameworks at a state and federal level demand documented data protection. Data sovereignty rules give compliance teams veto power over your entire AI architecture, forcing many organizations back to on-premises deployments.

Trust issues run deep when companies are deciding where to put their data. ESG research found that 50% of enterprises use their own data centers or colo or edge facilities as their primary location for AI data over hyperscalers. And 76% of them insist on keeping their most valuable data in their own data centers.

Modern storage systems offer built-in data protection to preserve data integrity at the point it's written, whether that's to an on-premises device or the cloud.

3.    Data quality

Garbage in still means garbage out. One in five companies admits they have a data quality problem. That could be sensor data tagged three ways, customer records with no metadata, or training sets nobody can validate.

This is where quality storage also comes into play. Fast storage enables better tagging and improves your control over your metadata. The less time that your engineers spend fighting your infrastructure, the more they get to build.

4.    Cost

Nearly one in five organizations watches helplessly as AI storage devours their infrastructure budget. While GPU costs aren't often negotiable, working with the right vendor can efficiencies in storage, with data reduction guarantees via always-on deduplication. And larger storage vendors maintain long-term agreements with flash manufacturers, which makes pricing more predictable in a volatile market.

5.    Scalability

Eighteen per cent of companies see scalability as a challenge when they're preparing data for AI processing. They have to flex storage loads with training runs especially as they fine tune models to support their own specific use cases. This is where hybrid storage solutions come into play. A single storage ecosystem that supports on-premises and cloud environments enables teams to mix and match data in the same environment as they move from collection and preparation through to training and AI inference.

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Conclusion

Modern storage platforms tackle all five of these challenges simultaneously. They scale linearly without forklift upgrades, embed security from the ground up, and provide the metadata tools teams need for data quality. They speak every protocol your AI pipeline needs, and deliver efficiency gains to boot.

Your AI initiatives deserve storage that matches your ambition.