Disrupting Data Management with AI?

 Value of intelligently positioned disruption.   From Deloitte

To achieve the benefits and scale of AI and MLOps, data must be tuned for native machine consumption, not humans, causing organizations to rethink data management, capture, and organization.

With machine learning (ML) poised to augment and in some cases replace human decision-making, chief data officers, data scientists, and CIOs are recognizing that traditional ways of organizing data for human consumption will not suffice in the coming age of artificial intelligence (AI)–based decision-making. This leaves a growing number of future-focused companies with only one path forward: For their ML strategies to succeed, they will need to fundamentally disrupt the data management value chain from end to end.

In the next 18 to 24 months, we expect to see companies begin addressing this challenge by reengineering the way they capture, store, and process data. As part of this effort, they will deploy an array of tools and approaches including advanced data capture and structuring capabilities, analytics to identify connections among random data, and next-generation cloud-based data stores to support complex modeling.  ... '