Source | Linkedin.com | BY:Enrique Dans, Senior Advisor for Innovation and Digital Transformation at IE University
Machine learning is, above all, a set of tools that allow a machine to iteratively learn from data and develop models that have not been specifically programmed by a person. Machine learning’s capacity for disruption can provide a competitive advantage: the algorithms are adapted to the data and end up generating better predictions and results than those developed by people, so companies that use machine learning obtain greater efficiency, better performance, more agility and other previously impossible functions.
But as a tool, machine learning is not something that can simply be “bought and installed”, because it depends on the quality and accessibility of data, and therefore requires a “data-centricity” that for many companies is still not possible. In reality, a large part of what is called machine learning remains inflated expectations, unfulfilled promises and the unrealistic hopes of businesses that believe it will turn them into the company of the future. Developing the procedures that allow the collection and preparation of data is enormously complex. Machine learning now faces an epidemic of misinformation. Only those companies able to orient themselves to the generation and processing of data will benefit from machine learning and turn them into real competitive advantages.