Predictive analytics uses data mining and text analytics, as well as predictive modeling to anticipate what will likely happen in the future based on insights gained through descriptive and diagnostic analytics. The ability to predict what is likely to happen next be essential to satisfying customers, improving operations and beating the competition.
Predictive analytics is about using patterns found in historical and real-time data to signal what is ahead. Predictive analytics is used to identify risks and opportunities for the future in terms of sales, assets, productivity and more.
Predictive models, statistical analysis, data mining, real-time scoring, and a range of advanced algorithms and techniques can be used to improve business performance and strategic decision making by predicting asset failure and behavior, understanding customers, making informed decisions, and analyzing trends and relationships in current and historical data to forecast future trends. Predictive analytics is generally applied in the areas of fraud detection and security, Operations, Marketing and Risk.
Predictive modeling mathematically represents underlying relationships in historical data in order to explain the data and make predictions, forecasts or classifications about future events. It analyzes current and historical data on individuals to produce metrics such as scores which rank individuals on future performance. Predictive models are frequently applied in mission-critical transaction systems and drive decisions and actions in near real time.
Predictive Models can be deployed using both linear and nonlinear mathematical programming algorithms, in which one objective is optimized within a set of constraints, advanced “neural” systems, which learn complex patterns from large data sets to predict the probability that a new individual will exhibit certain behaviors of business interest, and statistical techniques for analysis and pattern detection within large datasets.