FEATURE
What makes feeding unstructured data to AI so difficult?
Unstructured data is both critical and problematic. It’ s essential for inferencing but riddled with quality issues. Imagine an AI model sifting through terabytes of irrelevant or outdated files. The results will be inaccurate and costs for storage and compute will spike.
Sending too much uncurated data also raises the risk of exposing sensitive information such as intellectual property, personally identifiable information or regulated healthcare records. Organisations need systematic ways to profile and curate unstructured data at scale so only the right subsets reach AI processes.
Why can’ t traditional ETL tools solve this?
ETL was designed for a different world. It excels at pulling structured data from transactional systems, spreadsheets or relational databases – places where everything is neatly organised.
Unstructured data is a different beast. It’ s massive in scale, scattered across silos and hard to classify. AI workflows are iterative and nonlinear, with branching paths rather than straight lines. Moving unstructured data through a rigid ETL framework doesn’ t work. www. intelligentcxo. com
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