This post was originally published on Info World
Why AI exposes the cost of separation
AI introduces fundamentally different demands than the analytics workloads businesses have grown accustomed to. Instead of tables and rows processed in batch jobs by an engine, modern AI pipelines now process large amounts of unstructured and multimodal data, while also generating large volumes of embeddings, vectors, and metadata. At the same time, processing is increasingly continuous, with many compute engines touching the same data repeatedly—each pulling the data out of storage and reshaping it for its own needs.
The result isn’t just more data movement between storage and compute, but more redundant work. The same dataset might be read from storage, transformed for model training, then read again and reshaped for inference, and again for testing and validation—each time incurring the full cost of data transfer and transformation. Given this, it’s no surprise that data scientists spend up to 80% of their time just on data preparation and wrangling, rather than building models or improving performance.
While these inefficiencies can be easy to overlook at a smaller scale, they quickly become a primary economic constraint as AI workloads grow, translating not only into wasted hours but real infrastructure cost. For example, 93% of
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