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Predictive analytics encompasses a variety of statistical techniques from modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future, or otherwise unknown, events. Topics like Competitive Analysis of Mining Algorithms , Data integration .
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Scope and importance
Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Predictive analytics does not tell you what will happen in the future. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment. Predictive models and analysis are typically used to forecast future probabilities. Applied to business, predictive models are used to analyse current data and historical facts in order to better understand customers, products and partners and to identify potential risks and opportunities for a company. It uses a number of techniques, including data mining, statistical modelling and machine learning to help analysts make future business forecasts.
Predictive analytics and data mining solutions for the enterprise are currently available from a number of companies, including SAS (Predictive Analytics Suite), IBM (IBM SPSS Statistics), and Microsoft (Microsoft Dynamics CRM Analytics Foundation
The Predictive analytics market has been achieving strong revenues. The Global Market Statistics shows that it has reached a revenue height of 7.39 USD (billion), 7.15 USD (billion) and 4.12 USD (billion) for services, hardware and software, respectively for the year 2013. The global market statistic also shows a revenue forecast for the global big data market from 2011 to 2017. For 2016, the source projects the global big data market is predicted to grow to more than 45 billion U.S. dollars in revenue.
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This page was last updated on 12th Sep, 2015
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