Predictive analytics is currently one of the most important Big Data trends. But both predictive analytics and data mining attempt to make predictions about possible events in the future with the help of data models. What are the differences between them?
“Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.” (Wikipedia)
The classical data mining techniques include:
- Data clustering – The aim here is to segment data and to form various groups
- Data classification – Data elements are automatically assigned to different predefined groups/classes
- Regression analysis – relations between (more) dependent and independent variables are identified
- Association analysis – search for patterns in which an event is connected to another event; the dependencies between the data sets are described on if-then rules.
“Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.” (Wikipedia)
Predictive analytics forms a sub-discipline of Business Analytics (BA), a technique that provides answers to questions about the reasons, effects, interactions or sequences of events. It is also possible to play through scenarios and to identify alternatives – what happens if we turn to this or that screw?
Predictive analytics sets up where Online Analytical Processing (OLAP) reporting or stop. Rather than analyze the existing situation, predictive analytics attempts to make predictions about possible events in the future with the help of data models. Here is a close connection with data mining.
Often data mining and predictive analytics used interchangeably. In fact, methods and tools of data mining play an essential role in predictive analytics solutions; but predictive analytics goes beyond data mining. For example, predictive analytics also uses text mining, on algorithms-based analysis method for unstructured contents such as articles, blogs, tweets, Facebook contents..
Predictive analytics is now used in many industries with great success. For example, Banks are using predictive analytics to estimate the credit scorings from the probability of a customer could not afford the future installments of a loan granted.
An example from industry is predictive maintenance. While sensors transmit data to the state of the system such as power, temperature, revolutions and utilization to the Cloud platform, and then the predictive analytics application will analyze the system characteristics such as usage, wear and condition from various sources and recognize error patterns and poor quality components. The application can respond in time and prevent costly machine failure.
There are countless examples of predictive analytics in other sectors. Basically predictive analytics is an ongoing, iterative process. The model parameters can be adjusted and improved by progressive use of the service and the forecasts will become more and more precise.