Data analysis — the process of collecting, processing, and drawing insights from data — comes in many flavors. Predictive analysis is just one type of data analysis, but it’s highly valued for the benefits it provides in making business decisions. In this article, we’ll look at the basics of predictive analysis, including its definition, applications, models, tools, and examples!
Predictive Analysis: Definition
Predictive analysis, more commonly known as predictive analytics, is a type of data analysis which focuses on making predictions about the future based on data. There are several other types of data analysis, like descriptive analysis and diagnostic analysis, but the predictive analysis is particularly popular in the business analysis world as it is invaluable in effective decision-making.
In any case, predictive analysis usually involves the use of various statistical models, techniques, and tools, all of which help to understand the patterns in datasets, and thus make predictions about the future.
When to Use Predictive Analysis
Whereas some types of data analysis are only valuable in reviewing what has already happened, predictive analysis is all about making predictions. As a result, you can use predictive analysis whenever you feel the need to make predictions about the future.
Specifically, predictive analysis can be helpful when evaluating a business decision. This is because effective decision-making is all about understanding the consequences of decisions, based on predictions of how a venture, group, environment, or other entity will perform.
Types of Predictive Analysis Models
There are a variety of data analysis models that fall under the category of predictive analysis. Almost all of these are regression models, which means that they seek to identify the relationships between two or more variables. By identifying the relationships between these variables, they can help to predict the value of an unknown variable as the value of a known variable — like time — changes.
The most simple model used in predictive analysis is a linear regression model. In this model, the value of an unknown variable is assumed to scale linearly with the value of a known variable. Linear regression models can be helpful to track simple relationships, such as the growth in a customer base, and thus predict their future.
Random forests are machine learning models that can be used to model regression, among other things. They consist of a series of decision trees and are suitable for large sets of data with numerous variables.
Neural networks are a cutting-edge technology used in predictive analysis. They are a group of biological or digital neurons that communicate between one another. Depending on the data that is put through a neural network, the network changes shape and draws new conclusions.
Predictive Analysis Tools
Setting aside models, there are plenty of dedicated tools for predictive analysis purposes. These tools help to identify relationships which can be used to make predictions about the future, based on data. They incorporate many of the statistical models used in predictive analysis, doing the heavy lifting for the user.
RapidMiner Studio is a popular commercial tool for all aspects of predictive analysis. It helps in collecting and processing data, as well as applying various statistical models so as to draw valuable conclusions.
KNIME is an open-source data analysis platform which provides many of the same features of RapidMiner Studio. However, it appears to be designed for more advanced users.
IBM Predictive Analytics
When it comes to SaaS solutions, IBM offers a suite of predictive analytics products, including its flagship SPSS Statistics software offering. The solution is primarily targeted towards enterprise users, featuring a range of predictive analysis models.
SAP Predictive Analytics
Another popular SaaS offering in the predictive analytics space comes from SAP. The provider of enterprise management software offers an Analytics cloud for enterprise users, which is similar in implementation to IBM’s offering.
Predictive Analysis Examples
In order to help you better understand the principles of predictive analysis, let’s dive into a few examples of how the practice is applied.
Example #1: Financial Markets
Predictive analysis is invaluable in financial markets, where it is used by a vast number of stakeholders in order to educate trading decisions. Wherever possible, traders seek to use technical and fundamental analysis to improve their chances of succeeding in the markets. While there are a number of rudimentary indicators that any trader can use to estimate technical and fundamental variables, large-scale predictive analysis operations are attractive for their ability to take into account patterns and variations in a multitude of variables.
To give a specific example of how predictive analysis might be applied, a trader could create a statistical model to identify patterns in stock prices that usually occur before the stock increases in value. This model could then be used as a buy signal for the trader.
Example #2: Supply Chains
An easier example of predictive analysis lies in supply chains. Just look at the consumption of various food products: our consumption changes throughout the year, and there are times where certain products need to be stocked more than others. This way, all areas of supply chains, including the supermarkets that ultimately sell products to customers, can be prepared for patterns in consumption.
Once again, to give a more specific example, consider the consumption of potatoes. Predictive analysis models might find that potatoes are more often consumed during the winter than the summer. If an abnormally cold month is forecasted, predictive analysis models could also use the forecast as another input, thus modelling a greater demand for potatoes.
Predictive analysis is an incredibly complicated area within the field of data analysis. However, being able to make predictions about the future is invaluable, so the complexity is well worth it! In this article, we’ve reviewed some of the tools and models commonly used for predictive analysis. We’ve also looked at some specific examples of where it might be used and what value it can provide, so that you can better understand its place in the world of data and business analysis.
Image by Gerd Altmann