Predictive modeling and predictive analytics are terms used, often interchangeably, in big data. 

Predictive modeling uses regression model and statistics to predict the probability of outcome and it can be applied to any unknown event predictive modeling is often used in the field of machine learning and artificial intelligence (AI). The model is chosen using detection theory to guess the probability of an outcome given a set amount of input data.

Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.

Machine learning is considered a modern-day extension of predictive analytics. Efficient pattern recognition and self-learning are the backbones of machine learning models, which automatically evolve based on changing patterns in order to enable appropriate actions.

It’s certainly not a Magic 8-Ball, but predictive analytics can and has been used in a variety of applications. Most often it is associated with meteorology and weather forecasting, but it has many applications in business as well.

Some of the most common uses of predictive modeling touches everyone at some point, whether you are aware of it or not. Online advertising and marketing professions use a web surfers’ historical data, running it through algorithms to determine what kinds of products users might be interested in and what they are likely to click on.

Predictive modeling is often considered to be primarily a mathematical problem. However, users must plan for the technical and organizational barriers that might prevent them from getting the data they need. For example, systems that store useful data are not always connected directly to centralized data warehouses.

Predictive models often perform calculations during live transactions—for example, to evaluate the risk or opportunity of a given customer or transaction to guide a decision. For example, if health insurers could accurately predict secular trends, premiums would be set appropriately, profit targets would be met with more consistency, and health insurers would be more competitive in the marketplace.

Melody K. Smith

Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.