The oft used phrase “knowledge is power” draws an explicit parallel between information and might, and the 21 century – also known as the information age – has certainly shown this to be true. Today, staying ahead of the competition requires you to know what’s coming and anticipate an effective response to ensure not just the survival of your enterprise, but its growth.
Well, that’s where predictive analytics comes in: using a variety of techniques, you analyze data to decide the best action to take, factoring in any uncertainty life could throw your way. There are numerous techniques that come under the umbrella of predictive analytics such as data mining, classical statistics, machine and deep learning but their ultimate purpose is the same – to help you make the optimal decision when faced with uncertainty.
Predictive analytics is used mostly in the business setting, where financial figures are of paramount concern. This covers domains such as insurance costs and fraud, trends for the stock market, marketing for industries like telecommunications, and operations for services like general healthcare and the government.
Because of the different techniques employed in predictive analytics, it can be difficult to comprehend as a concept. However, at its core, it can be viewed as a collection of techniques used to break complicated problems into simpler, less complex ones that are easier to model, and thus easier to grasp and solve.
Sometimes predictive modeling and predictive analytics are thought to be the same thing. While they are closely tied, they’re not the same: predictive models are used solely to create a representation of conclusions and information, whereas predictive analytics is what make that conclusion.
Predictive analytics is dependent on various approaches for problem solving. Some of the techniques that are part of predictive analytics are data mining, artificial intelligence, and machine learning.
Data mining is the process of collecting huge amounts of raw information. For a program to make the best decisions possible, and to graph what conclusions can be extrapolated from a situation, it needs to have as much initial information as possible. Thus, data mining underlies the core existence of predictive analytics, giving it the ability to make predictions out of what has already happened. It is only the initial stage of analyzing raw information though, and its output must go through several subsequent stages to obtain the final workable decision.
Artificial intelligence employs algorithms to connect all the data points and come up with solutions to specific problems, whether it’s playing chess or graphing trade forecasts. In essence, artificial intelligence aspires to make computers reason and behave like humans. Machine learning might be the phenomenon that makes these programs grow more efficient and effective, but it is the greater structure of artificial intelligence that enables it to use the available data effectively, and to make the required predictive models and decisions.
Machine learning uses mined data to create intelligent systems that learn in either a supervised or unsupervised environment before being exposed to new data. A subset of artificial intelligence, it is responsible for the efficient and effective retrieval of useful information from what was mined, and the weeding out of unnecessary data from the predictive analytics process. Because much of predictive analytics is heavily reliant on raw data collected on numerous subjects, the program must learn to separate useful information from noise and make the most logical connections. The machine learning algorithms used in predictive analytics make this possible and are applied in several fields including robotics, computer vision, NLP and fraud detection.