FEATURE
ETL VS AI PIPELINES: WHY UNSTRUCTURED DATA NEEDS A NEW APPROACH
Intelligent CXO sat down with Prateek Kansal, Head of Engineering for Komprise in India. In this wide-ranging conversation, Kansal explains the financial stakes of unstructured data, the limitations of ETL and how AI-driven data pipelines are becoming essential for performance, governance and cost control.
Why are CXOs so focused on unstructured data pipelines for AI right now?
The pace of AI development is extraordinary and executives see real money on the table. The MIT Center for Information Systems Research reported that top-performing organisations generate about 11 % of their revenues from data monetisation, compared with just 2 % for lower performers. That’ s a fivefold gap and it’ s widening.
Artificial Intelligence has shifted from an experimental tool to a boardroomlevel priority. The competitive edge that comes from monetising data is now measured in direct revenue gains. Yet, most of the data enterprises hold is unstructured – scattered across formats and silos, messy to manage and difficult to feed into AI systems effectively. Traditional data approaches like extract, transform and load( ETL) fall short here.
The challenge is that AI thrives on unstructured data – documents, medical images, video, audio, logs and sensor data – which makes up about 90 % of what enterprises store. But this data is noisy and cluttered. Feeding it indiscriminately into AI models increases costs and degrades outcomes. CXOs are intent on building automated governed pipelines because without them, AI projects either stall or deliver poor results while competitors push ahead.
24 www. intelligentcxo. com