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What is Predictive Analytics? Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. It uses a number of data mining, predictive modeling and analytical techniques to bring together the management, information technology, and modeling business process to make predictions about future. The patterns found in historical and transactional data can be used to identify risks and opportunities for future. Predictive analytics models capture relationships among many factors to assess risk with a particular set of conditions to assign a score, or weightage. By successfully applying predictive analytics the businesses can effectively interpret big data for their benefit.

The data mining and text analytics along with statistics, allows the business users to create predictive intelligence by uncovering patterns and relationships in both the structured and unstructured data. The data which can be used readily for analysis are structured data, examples like age, gender, marital status, income, sales. Unstructured data are textual data in call center notes, social media content, or other type of open text which need to be extracted from the text, along with the sentiment, and then used in the model building process.

Predictive analytics allows organizations to become proactive, forward looking, anticipating outcomes and behaviors based upon the data and not on a hunch or assumptions. Prescriptive analytics, goes further and suggest actions to benefit from the prediction and also provide decision options to benefit from the predictions and its implications.