Machine learning builds up a model that connects data points in complex, multi-dimensional ways, usually without yielding the sort of general principles we're accustomed to reasoning from.
The more systems and subsystems we attempt to stitch together, the greater the unpredictability.
In the field of knowledge management, of course, the idea of turning data into information into knowledge has been a foundation concept for knowledge managers. But frankly, the ability to achieve this alchemy of data to knowledge has not been broadly demonstrated in practice. A next generation information refinery is required to make something meaningful and valuable out of the raw data flying around the firm and throughout the internet economy.
As the world races ahead, purely data-driven approaches will become less attractive. Instead, we need to start gaining a deeper understanding of how to bridge the great divide which separates the artificial and the natural.