In this era of big data, chances are that you have heard and used terms like machine learning and predictive modeling. For the average person, these terms may actually seem interchangeable. However, in the world of data analysis, there are big differences between these terms. In fact, these tools are used differently to provide different kinds of insights and add different strategic value. Today, we will take a closer look at predictive modeling, machine learning, and artificial intelligence, and discuss when to use them for the best results.
Predictive modeling simply uses a given dataset to predict a probable outcome. It uses statistical analysis, model classifiers, and/or detection theory to determine the likelihood of a certain result. As with all such tools, the larger and higher integrity the initial dataset, the more accurate the prediction will be. To make things more confusing, predictive modeling and predictive analytics are often used to refer to the same processes, when they are actually different. Predictive modeling runs a specific model on a specific dataset to predict an outcome. Predictive analytics is a broader term that encompasses predictive modeling, machine learning, and other processes to predict outcomes.
Common application for predictive modeling
Predictive modeling is used most often to predict certain outcomes from a specific dataset. For example, you might use predictive modeling to anticipate how many current customers might respond to a specific offer or incentive. It may also be used to determine what kind of health conditions increase the likelihood of hospital admissions, or what kind of driving records increase the risk of accidents.
At the most basic level, machine learning advances predictive modeling. With machine learning, a dataset or model is analyzed, a prediction is made, and then the actual outcome is measured against the prediction, in order to increase the accuracy of future predictions. In this way, the algorithm “learns” and gets more accurate over time by assessing actual outcomes. Where predictive modeling analyzes information and infers future behavior based on the model or sample, machine learning evaluates a data set for repeatable and predictable patterns. “Deep learning” is a subset of machine learning that specializes in nonlinear reasoning, and can be used to detect more subtle patterns.
Common applications for machine learning
Since machine learning is ideal for pattern recognition, it is often used to segment customers and contacts, grouping them by similarities. It is also used to find correlations between events (people who are X tend to like Y). Since machine learning algorithms learn and perform more quickly and accurately over time, they are often used for long-term goals and objectives.
If machine learning takes the basic functions of predictive modeling and builds on it, then artificial intelligence is likewise an enhancement of machine learning. Artificial intelligence is designed to simulate human intelligence and apply it to problem-solving. Where machine learning is used to make predictions that are faster and more accurate over time, artificial intelligence is used to postulate different methods of achieving desired outcomes. In other words, where machine learning is used to make increasingly accurate predictions, artificial intelligence is used to suggest actions that may change those outcomes.
Common applications for artificial intelligence
Artificial intelligence is most often used in business to assess potential outcomes of different decisions, allowing you to theorize the outcome of data, models, or processes that are not yet in place. If your machine learning algorithms identify a pattern of reduced revenue or increased overhead, artificial intelligence algorithms may be used to find new efficiencies or streamline processes. Artificial intelligence is also used in applications that require complex actions in real time, like network security or customer service interactions.
While these applications are just the tip of the iceberg for how these technologies are being used, and how they may be used in the future, this guide is a good overview of how these terms and technologies are being used today, and how they are being used in businesses around the world.
The ease of access to data is revolutionizing economies. Positive outcomes like predicting future customers, increasing profits, and improving efficiency are all windfalls of using data to make decisions. If you are looking to implement or revitalize your data analytics strategy, contact the experts at Helios for all your data and analytics needs.