Predictive analytics and machine learning are not interchangeable terms as many seem to believe. In practical terms, both processes have important roles to play in the delivery of accurate and actionable insights from your available data.
David Roe differentiates between predictive analytics and ML in this article from CMSWire:
The main difference between machine learning and data analytics depends on which direction you want to look — forward or backward. Data analytics in its simplest form is looking back at what was done to find trends that may help you moving forward. Data analytics is largely focused on identifying key variables that likely caused a result to happen the way it did. Machine learning, on the other hand, takes all these past variables and applies them to future situations using algorithms to predict future outcomes. The more data we can feed into a machine learning algorithm, the more accurate the algorithm is likely to be.
It’s important that these two roles work in tandem and not allow machine learning to replace standard data analysis. “It’s always worthwhile to regularly check back on the results of a machine learning process to ensure it was accurate, and if not, consider re-engineering the algorithm for improved performance going forward,” Underwood said.
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Machine learning is essentially a means to predictive analytics as machine learning is used to perform predictive analytics. In fact, most traditional predictive models, like linear regression, can be deployed using machine learning algorithms. Think of predictive analysis as the destination — the outcome we desire — and machine learning as a freeway to get us there, or the means to achieve that outcome. You may still need to use maps, implement quick thinking and take some back roads, but taking the freeway of machine learning will help you get to solid predictive analysis sooner than before.
