In the accelerating pace of business, companies are striving to be data-driven. To be a data-driven enterprise means to relentlessly measure, monitor, predict, and act on the pulse of the business in a continuous and automated manner. To do so, an enterprise 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 enterprise-wide.
David Kolb identified human learning as having two separate activities that occur in the learning cycle:
- Perception (the way we take in information)
- Processing (how we deal with information)
The key difference between humans and machines in learning 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 straightforward; many times, it’s complex and cumbersome.
The use case, data at hand, choice of tools, organizational 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 take you to the next level in machine learning. This allows companies to respond in the moment and in real time, with the agility and speed necessary to support the business.
To learn more, see:
- All the machine learning series posts in the SAP Analytics blog for more on machine learning, predictive analytics, and artificial intelligence
- The predictive Forrester Wave report and the predictive analytics TDWI paper, Machine Learning for Business: Eight Best Practices for Getting Started
- The IDC paper The Value of Analytics in Digital Transformation