In the accelerating pace of business, businesses are thriving to be data driven. To be a data-driven business means to relentlessly measure, monitor, predict, and act on the pulse of the business in a continuous and automated manner. To do so, an organization needs to communicate the value of data across the entire organization, act as catalyst to persuade cultural change to be data driven, and—most importantly—employ and deploy machine learning across the organization.
David Kolb identified human learning as having two separate learning activities that occur in the learning cycle:
The key difference in learning between humans and machines is motivation. Human learning is primarily based on motivation, whereas in machines motivation is built in (taken for granted). The machine-learning process is not straight forward and many times it’s complex and cumbersome.
The use case, data at hand, choice of tools, organization skills, and data-driven culture are often bigger variables in determining the process.
Machine learning is a continuous process where algorithms and models learn continuously to adjust the perception and processing. The more often you feed the data, the quicker you go through the process (automating every possible step).
Data preparation, model development, validating and optimizing, and failing fast are critical for better machine learning. More importantly, failing fast is a really good thing in machine learning.
Failing fast and automating every possible step takes you to the next level in machine learning. It allows companies to respond in the moment and in real time, with the agility and speed necessary to support the business.