Predictive analytics is a type of data analysis that uses statistical algorithms and machine learning techniques to identify patterns in data and make predictions about future events.
It involves analyzing current and historical data to make predictions about future events or outcomes, such as customer behavior, market trends, or risk assessment.
Predictive analytics relies on data mining, machine learning, and statistical analysis to make predictions.
Real-time analytics is the process of collecting, analyzing, and acting on data as it is generated, rather than after the fact.
It involves the use of technologies and techniques that allow organizations to process and analyze data in real-time, as it is being generated, rather than waiting for the data to be collected and analyzed at a later point in time.
Real-time analytics has a wide range of applications, including customer behavior tracking, fraud detection, supply chain management, and real-time decision making.
By providing organizations with immediate insights into their data, real-time analytics can help them make more informed and timely decisions, improve efficiency, and respond to changing conditions in real-time.
To support real-time analytics, organizations may use technologies such as stream processing, in-memory databases, and real-time data visualization tools.