Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future.
Predictive analytics involves the process of predictive modeling and Predictive modeling put simply, is the process of creating, testing and validating a model to best predict the probability of an outcome. A number of modeling methods from machine learning, artificial intelligence, and statistics are available in predictive analytics software solutions for this task.
Defining Predictive Analytics in Healthcare
Predictive analytics in healthcare is rapidly becoming one of the most-discussed, perhaps most-hyped topics in healthcare analytics. Predictive analysis using machine learning is a well-studied discipline with a long history of success in many industries. Healthcare can learn valuable lessons from previous success to jumpstart the utility of predictive analytics for improving patient care, chronic disease management, hospital administration, and supply chain efficiencies. The opportunity that currently exists for healthcare systems is to define what predictive analytics means to them and how can it be used most effectively to make improvements.
However, predictions made solely for the sake of making a prediction are a waste of time and money. In healthcare and other industries, prediction is most useful when that knowledge can be transferred into action. The willingness to intervene and make active changes is the key to harnessing the power of historical and real-time data. Importantly, to best gauge efficacy and value, both the predictor and the intervention must be integrated within the same system and workflow where the trend occurs.
Predictive Analytics in healthcare is not the in and all of predictive analysis. It is used in the healthcare industry but it doesn’t stop there, predictive analysis is also used in other Industries and I’ll explain a few major ones below.
Banking & Financial Services Industry
The financial industry, with huge amounts of data and money at stake, has long embraced predictive analytics to detect and reduce fraud, measure credit risk, maximize cross-sell/up-sell opportunities and retain valuable customers. Commonwealth Bank uses analytics to predict the likelihood of fraud activity for any given transaction before it is authorized – within 40 milliseconds of the transaction initiation.
Since the now infamous study that showed men who buy diapers often buy beer at the same time, retailers everywhere are using predictive analytics to determine which products to stock, the effectiveness of promotional events and which offers are most appropriate for consumers. Staples analyzes consumer behavior to provide a complete picture of their customers and realized a 137 percent ROI.
Oil, Gas & Utilities Industries
Whether it is predicting equipment failures and future resource needs, mitigating safety and reliability risks, or improving overall performance, the energy industry has embraced predictive analytics with vigor. Salt River Project is the second-largest public power utility in the US and one of Arizona’s largest water suppliers. Analyses of machine sensor data predict when power-generating turbines need maintenance.
Why Predictive Analytics Matters
Predictive analysis is important because organizations including healthcare organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities. Common uses of predictive analysis include:
Detecting fraud: Combining multiple analytical methods can improve pattern detection and prevent criminal behavior. As cybersecurity becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities, and advanced persistent threats.
Optimizing marketing campaigns: Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
Improving operations: Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
Reducing risk: Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.