Predictive analytics employ a number of statistical techniques to analyze historical and current data to predict future results, behaviors, and events.
Predictive analytics draw from predictive modeling, machine learning, and data mining techniques. The defining function is arriving at a statistical probability on an individual unit basis from big data that can be applied to much larger pools of constituents.
Predictive Analytics analyze organizational units such as clients, products, patients, employees, vehicles and more. That information is used to influence processes that relate to sales, marketing, healthcare, manufacturing, and risk assessment to name a few.
In business, predictive models take advantage of identified patterns found in current and historical data to guide decision making, identify opportunities and mitigate risk.
What distinguishes Predictive Analytics from other big data analytics is that it provides futuristic forecasts. The days of acting on executive "hunches" is over. Businesses today need to invest in intelligent forecasting to avoid the lost revenues and operational expenses associated with strategical mistakes and uneducated decisions.
Business applications for Predictive Analytics range from personal lending to fraud detection, law enforcement, eCommerce, retail sales, banking, manufacturing, insurance, pharmaceuticals, real estate, hospitality and more. In today's world, businesses need to rely on Predictive Analytics to make informed strategic decisions to improve sales, operations, and overall profitability.
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