What is Predictive Analytics?
Predictive analytics uses historical data to determine future probabilities. For example; Historical data works with predictive analytics to create a mathematical model that captures key trends. These predictive probabilities also use available data to predict what will happen next, as well as suggest actions to take for best results. Predictive analytics has become extremely popular recently due to advances in supporting technology, especially in the fields of big data and machine learning.
What are the Benefits of Predictive Analytics?
- Big Data
Predictive analytics is highly advantageous for big data. Big data such as engineering data, sensors, business system data, marketing information are most functionally used in predictive analytics system.
- Increased Competition
With the competion increasing worldwide, it is also very advantageous to carry businesses, products and services to crowded markets. Data-driven predictive business designs also find permanent solutions to long-standing problems of companies.
- Superior Technology
The biggest advantage of predictive analytics models is that they can be applied to many different datasets. Even data sources, process databases, equipment log files, images, video, audio, sensors or other types of data can be examined. With all this data, it is essential to analyze insights and trends and find appropriate solutions.
Areas of Use of Predictive Analytics Models
• Automotive - Autonomous: Predictive analytics is used to analyze sensor data and create driver assistance algorithms.
• Aviation - Aircraft: Real-time analysis is performed to predict subsystem performance for an engine manufacturer, oil, fuel, take-off, mechanical health and controls to improve aircraft operating processes and reduce maintenance costs.
• Power Generation - Forecasting electricity price and demand: Large companies profit in the long run thanks to advanced forecasting applications.
• Industrial Automation and Machinery - Performs the function of predicting machine failures.
• Medical Devices - In diseases such as asthma and COPD, the patient's respiratory data are analyzed and feedback is generated with smart devices.
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