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Predictive Analytics is a branch within advanced analytics used to make predictions about future events that are still unknown. Predictive analytics makes use of several techniques including statistics, data mining, machine learning, modeling, as well as AI (artificial intelligence) to analyze present data and make predictions concerning the future. Predictive analytics makes use of various predictive modeling, data mining, and analysis techniques to bring together information technology, management, and business modeling processes to make future predictions. The technology works on the basis of transactional and historical data that are used for purposes such as identifying future risks or identifying future opportunities. Models used in predictive analysis capture relationships among several factors so as to assess risks using a specific set of conditions to assign weights or scores (Waller & Fawcett 2013).
Business users, utilizing text analytics, data mining, as well as statistics crate predictive intelligence through the uncovering of relationships and patterns in both unstructured and structured data. Structured data, such as gender, age, marital status, and sales, can be readily used for predictive analysis. Unstructured data, such as social media content, text data in call center information/ notes, sentiment such as those in social media or open text that must be extracted are also used for predictive analytics in the process of model building (Umachandran & Ferdinand-James 2017). Using predictive analytics, organizations become forward looking, proactive, and anticipate behaviors and outcomes based on data and not on assumptions or just hunches. The result is better decision making and planning, which among other things confer competitive advantages to the organization (Rickman & Cosenza 2007).
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