Predictive analytics is the use of data, algorithms, and techniques involving machine learning to understand the chances of a possible outcome. Why has predictive analytics become so popular now?
Predictive analytics is basically the use of data, algorithms, and techniques involving machine learning to understand how likely a future outcome may occur based on historical data. The idea is to go past the concept of knowing what’s already happened and be able to provide a reasonably accurate assessment of what is going to occur in the future.
Even though predictive analytics has been around for a number of years, it’s beginning to be used by more and more businesses and organizations to increase their competitive advantage and, hopefully, their bottom line as well.
The question is why has predictive analytics become so popular now? The answer is that it appears to be a specific type of technology whose time has come. It seems that today, more and more companies are beginning to turn to predictive analytics so they can increase their competitive edge and, eventually, their bottom line.
Today there are continually growing volumes and different types of data. There’s also considerably more interest in using data to extrapolate valuable insights. Another reason is that faster and cheaper computers are being developed along with software that’s easier to use. Tough economic conditions also are playing a part, and so is the need for competitive differentiation.
Software has become easier to use and more prevalent due to interactive use. Predictive analytics isn’t only the domain of statisticians and mathematicians anymore. Businesses use it for their analysts to get ahead of the competition and statisticians use it to broaden their results to make them more meaningful.
Organizations have turned to predictive analytics in helping them solve numerous difficult problems and to uncover wide new opportunities. Some of the most common uses of predictive analytics include:
By combining analytics methods, the patterns of criminal behavior can be vastly improved. Cybersecurity has always been a concern since the creation of online computing. High-performance behavioral analytics can examine every action on a web network in real-time and instantly identify abnormalities to indicate the presence of fraud, persistent threats, and zero-day vulnerabilities.
Predictive analytics can also be used in determining customer purchases or responses and to promote cross-selling opportunities. Predictive models can help a business attract, keep, and grow customers who are the most profitable.
Various companies also use predictive models in forecasting the management and inventory of resources. The airline industry uses predictive analytics in setting its ticket prices. A hotel can turn to predictive analytics to predict how many guests they can expect to show up for any given night so they can maximize their occupancy levels and increase revenue.
Credit scores are now universally used in assessing the likelihood of a buyer defaulting on purchases. A credit score that’s generated by a predictive model is turned to frequently to assess the worthiness of a person’s credit. Insurance collection companies also use predictive analytics for other types of risk-related uses.
By using predictive analytics, businesses and organizations can go far beyond learning what may have happened in the past and discover wide-ranging insights about what will occur in the future.
Predictive analytics has been long embraced by the financial industry. Because banking and financial services deal with huge amounts of data and money, they use predictive analytics for detecting and reducing fraud, measuring credit risk, maximizing cross-selling and up-selling opportunities, and to retain their most valuable customers.
Retailers use predictive analytics for optimizing prices and general merchandise planning. They also use it to analyze how effective promotional events are and to determine how appropriate special offers for specific customer groups.
Offline retailers can use predictive analytics to mimic the nuanced pricing strategies of e-commerce stores by tracking consumers through their smartphones. They can connect with and log customers as they enter the store, track the types of goods they stop to look over, and follow them through the checkout line when they make a purchase.
Predictive analytics is used by oil, gas and utility companies to predict equipment failures and determine their resource needs in the future. Their engineers also use it to mitigate safety and reliability risks, and for improving their overall performance.
Governments have always been one of the biggest proponents of the advancement of computer technology. The Census Bureau in the U.S. has analyzed data for decades to understand population trends. Governments use predictive analytics for improving their services and performance; detecting and preventing fraud; enhancing cybersecurity, and in better understanding consumer behavior.
The health insurance industry has used predictive analytics for years to detect claims fraud. It is also used to identify patients who are the most at risk of having a chronic disease and try to determine which interventions will prove to be the best to use in each situation.
Manufacturers around the world use predictive analytics so they can identify factors which lead to quality problems and production failures. They also turn to it for optimizing parts, product distribution, and service resources.
The airline industry has become particularly interested in predicting mechanical failures so that they can reduce flight delays and cancellations. The Cortana Intelligence Suite team at Microsoft has developed software that predicts the probability of an aircraft being delayed or canceled. By using flight route information and aircraft maintenance history, predictions can be made in real-time to determine the likelihood of a mechanical issue occurring that could result in the delay or cancellation of a flight.
Based on predictive analysis of previous performances of landscape-wide resource utilization, Syslink Xandria can forecast exactly when your landscape will require various levels of demand for database and server output. Xandria’s advanced and unique resource forecasting capabilities allow organizations to properly schedule and manage resource consumption. Systematic resource forecasting helps to accurately construct and adhere to budgets.
Today, multiple products can be used for predictive analytics and machine learning. To implement advanced analytics into any business process, it’s essential to begin by taking into account each of the different predictive models and identify the needs of your business or organization.
Predictive analytics is becoming more and more prevalent today in our being able to understand the likelihood of the future outcome of things occurring based on our previous historical data. Its time has finally come and the opportunities it will help us with in the future are yet to be realized.
To learn more about how predictive analysis is used to better manage SAP read this post.
By Danielle Canstello
About the Author: Danielle Canstello is part of the content marketing team at Pyramid Analytics. They provide bi analytics and business intelligence software. In her spare time, she writes around the web to spread her knowledge of the marketing, business intelligence and analytics industries.