F E A T U R E
new lines of enquiry. Without efficient systems for recall, projects risk delay and duplication of effort. More effective data lifecycle management can help ensure that data is not only captured but also kept accessible.
Built to last
In this context, storage reliability is dependent on ensuring data is durable, accessible when needed and that there are recovery processes in place that can restore service quickly. Indeed, durability is now fundamental to AI performance. According to a recent study by Gartner,‘ through 2026, organisations will abandon 60 % of AI projects unsupported by AI-ready data’. They go on to state that
‘ 63 % … either do not have or are unsure if they have the right data management practices for AI’, a situation that‘ will endanger the success of their AI efforts’.
For any organisation investing heavily in AI, that should make for interesting reading. Large-scale AI workloads rely on reliable distributed storage systems to provide uninterrupted access to training data. Even brief disruptions, such as metadata server failures or network timeouts or more problematic issues relating to data loss, can have a disastrous impact on overall performance and reliability. When failures occur, the cost is not just technical, but can also result in reputational damage, regulatory exposure and direct financial loss.
Poor data quality already drains US $ 12.9 – US $ 15 million per enterprise annually, while data pipeline failures cost enterprises around US $ 300,000 per hour( US $ 5,000 per minute) in lost insight and missed SLAs. In AI environments, that translates directly into stalled model training, wasted GPU resources and delayed time-to-value.
To illustrate, consider the storage issues facing a university research team running simulations on large datasets. If their systems aren’ t durable, a single outage could distort results and delay development.
But with durable infrastructure in place, their work continues without
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