Predictive Analytics and Its Uses across Diverse Industries

Posted by syntelli on March 27th, 2019

Predictive analytics is a type of data analytics that make predictions about future outcomes based on historical data and analytics techniques like statistical modeling and machine learning. It can generate future insights with a significant degree of precision. Today any organization can use past and current data to forecast trends and behaviors days or years into the future with the help of sophisticated predictive analytics tools and models. Organizations using predictive analytics find and exploit patterns contained within data in order to determine risk and opportunities. For instances, models can be designed to discover relationships between different behavior factors. These models allow to assess either the promise or risk presented by a particular set of conditions, help in decision making across various categories of supply chain and procurement events.   

Today organizations are using predictive analytics in a virtually endless number of ways, as this technology helps businesses of diverse fields ranging from healthcare, finance, retailing, hospitality, automotive, pharmaceuticals, aerospace, automotive and manufacturing. Organizations using the information from predictive analytics can suggest actions that can affect positive operational changes. Analytics use predictive analytics to foresee if a change will help them reduce risks, improve operations and increase revenue.

Although, predictive analytics is applied to almost all industries but there are some sectors to which it is particularly beneficial.

Health Care - Healthcare field face consistent challenge to reduce operating cost and keep it manageable along with improving patient outcomes. Physicians make use of predictive analytics to make correct diagnoses and determine best treatment for people with certain conditions. It is also indicated by a study that predictive analytics reduce wait times of emergency room by up to 15%. Predictions about future visits of patients allow shift managers to make more informed decisions when placing sufficient employees on a schedule to handle the anticipated rush.    

Banks - Banking sector is increasingly relying on predictive analytics to improve customers service, boost efficiency and identifies issues that could cause substantial problems later. For example banks that experience fraudulent activities or associated with fraud incidents are likely to loose trust of customers. However, using predictive analytics banks can spot characteristics associated with strange and potentially illegal customer behavior, thereby allowing banks to intervene before any fraud is committed. Predictive analytics also prove useful while screening application and cross-selling attempts and to understand best ways to courage customer loyalty.  

Manufacturing - Manufacturing - Professionals of this sector make use of predictive analytics to calculate the probability of system failure, incorporate characteristics like stress factors, operating environments and quality levels. They also depend upon predictive analytics to become aware of mechanisms that require repair and maintenance before they break down and cause temporary shutdowns.

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