What are Predictive Analytics?

Predictive analytics describe the use of statistics and modeling to determine future performance based on current and historical data. Predictive analytics look at patterns in data to determine if those patterns are likely to emerge again, which allows businesses and investors to adjust where they use their resources to take advantage of possible future events.

Key Takeaways

  • Predictive analytics is the use of statistics and modeling techniques to determine future performance.
  • It is used as a decision-making tool in a variety of industries and disciplines, such as insurance and marketing.
  • Predictive analytics and machine learning are often confused with each other but they are different disciplines.

Understanding Predictive Analytics

There are several types of predictive analytics methods available. For example, data mining involves the analysis of large tranches of data to detect patterns from it. Text analysis does the same, except for large blocks of text.

Predictive models look at past data to determine the likelihood of certain future outcomes, while descriptive models look at past data to determine how a group may respond to a set of variables.

Predictive analytics is a decision-making tool in a variety of industries. For example, insurance companies examine policy applicants to determine the likelihood of having to pay out for a future claim based on the current risk pool of similar policyholders, as well as past events that have resulted in payouts. Marketers look at how consumers have reacted to the overall economy when planning on a new campaign, and can use shifts in demographics to determine if the current mix of products will entice consumers to make a purchase.

Active traders look at a variety of metrics based on past events when deciding whether to buy or sell a security. Moving averages, bands and break points are based on historical data, and are used to forecast future price movements.

Common Misconceptions of Predictive Analytics

A common misconception is that predictive analytics and machine learning are the same things. At its core, predictive analytics includes a series of statistical techniques (including machine learning, predictive modeling, and data mining) and uses statistics (both historical and current) to estimate, or predict, future outcomes. Predictive analytics help us to understand possible future occurrences by analyzing the past. Whereas machine learning, on the other hand, is a subfield of computer science that, as per the 1959 definition by Arthur Samuel—an American pioneer in the field of computer gaming and artificial intelligence which gives "computers the ability to learn without being explicitly programmed."

The most common predictive models include decision trees, regressions (linear and logistic) and neural networks—which is the emerging field of deep learning methods and technologies.

Example of Predictive Analytics

Forecasting is an essential task in manufacturing because it ensures optimal utilization of resources in a supply chain. Critical spokes of the supply chain wheel, whether it is inventory management or shop floor, require accurate forecasts for functioning. Predictive modeling is often used to clean and optimize the quality of data used for such forecasts. Modeling ensures that more data can be ingested by the system, including from customer-facing operations, to ensure a more accurate forecast.