Predictive analysis is the analysis of big data to make predictions and determine the likelihood of future outcomes, trends, or events occurring. In the business field, it can be used to model various scenarios of how customers will react to new product offers or promotions and how the supply chain may be affected by adverse weather conditions or sudden increases in demand. Predictive analysis can include a variety of statistical techniques such as modeling, machine learning, and data mining.
The power of predictive analysis comes from a wide variety of methods and technologies — big data, data mining, statistical modeling, machine learning, various mathematical operations — that can be used in conjunction with parameters to extract from large volumes of data, both current and past, to make punctures on patterns and predict events and situations that may occur at a given time. This is particularly useful in helping companies find and exploit patterns in data by emphasizing risk and opportunities, behavioral relationships, or supply chain management.
Reliability and accuracy distinguish modern predictive analytics from the tools of the past used to forecast sales, inventory, programming, utilization, earnings, and numerous other important areas of business. Businesses in virtually any market can maximize a marketing campaign by using predictive analytics to support customer acquisition and feedback, and retain the most valuable customers with carefully targeted offers and promotions.
Big data is very large unstructured or structured data and is the most complex to analyze. For this you need advanced big data technology and big data tools that can work with large amounts of unstructured data. The amount of data collected is constantly increasing.
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