Among the emerging technologies which have gained widespread acceptance, and have the potential to cause significant disruptions, is predictive analytics. Predictive analytics measures and analyses variables, to unearth patterns, and predict the likely behaviour of machinery or people.
The emergence of Big Data has fuelled the growth of predictive analytics. A data model for predictive analytics co-opts many variables likely to influence the outcome of an event. Predictive analytics software applies advanced algorithms and statistical methodologies such as time series analysis, decision trees, logistic regressions, and more on the data model to identify the correlation between the different data elements.
Predictive analytics is now a critical component of the business model of many enterprises. Businesses apply predictive analytics to get a holistic view of customers and events, optimise processes, discover changes early to make strategic interventions, and improve their decision-making capabilities.
Insurance companies, an early adopting branche, analyse trends related to variables such as age, gender, location, type of vehicle, and driving record, to price car insurance policies. Likewise, financial service establishments apply predictive analytics to identify fraudulent transactions. Marketers build data models based on customer preferences, to deliver personalised advertisements, and optimise stocking patterns of retailers. Healthcare providers apply predictive analytics to individual and macro health data, to identify patients at risk of specific medical conditions. Industrial users of predictive analytics may identify machinery and plants likely to break-down, and need preventive maintenance. The emergence of the Internet of things (IoT) will increase the importance of predictive analytics in several sectors. For instance, subjecting the data collected from sensor-equipped weather stations to predictive analytics would deliver accurate and hyper-localised weather forecast to farmers, event organisers, theme parks, transportation operators, and others.
Several vendors, including biggies such as Microsoft and IBM, offer ready-to-use predictive analytic tools. Such tools support deep learning applications, and co-opt machine learning software. The high level of maturity attained by open source languages such as R, Phyton, and Sacla, and the easy availability of open-source algorithm libraries, makes it viable for analytic teams to develop custom predictive analysis software as well.
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