Predictive analytics is the prediction of trends and behavior patterns using information extracted from data. It is a branch of statistics that is used in most industries including the healthcare industry. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.
Healthcare Predictive Analytics is the processes of determining the patients who are at risk of getting or developing certain conditions such as lifetime illnesses, diabetes or asthma using data collected in the past and by studying patterns that have been formed. Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. This distinction is why it can be used in industries where the margin of error is very small because there is a higher guaranteed accuracy due to the use of past data and the study of trends using big data. For example, Predictive analytics technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions. In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.
Predictive analysis is important for several reasons, the major ones are listed below
The Rise of Big Data
I already mentioned big data briefly above and now we dive into it further. Predictive analytics is often discussed in the context of big data, Engineering data, for example, comes from sensors, instruments, and connected systems out in the world. Business system data at a company might include transaction data, sales results, customer complaints, and marketing information. Increasingly, businesses make data-driven decisions based on this valuable trove of information.
With increased competition, businesses seek an edge in bringing products and services to crowded markets. Data-driven predictive models can help companies solve long-standing problems in new ways. And in the healthcare industry, it can help provide better care to patients and offer better services thereby increasing the value of care while reducing cost. Companies also use predictive analytics to create more accurate forecasts, such as forecasting the demand for electricity on the electrical grid. These forecasts enable resource planning (for example, scheduling of various power plants), to be done more effectively. To extract value from big data, businesses apply algorithms to large datasets. The data sources might consist of transactional databases, equipment log files, images, video, audio, sensor, or other types of data. Innovation often comes from combining data from several sources.
With all this data, tools are necessary to extract insights and trends. Machine learning techniques are used to find patterns in data and to build models that predict future outcomes. A variety of machine learning algorithms are available, including linear and nonlinear regression, neural networks, support vector machines, decision trees, and other algorithms.
Real World Examples
In practice, predictive analytics can take a number of different forms and can be used in various ways. The term might sound foreign to you but chances are you’ve already seen it being used. Take these examples
- Identify customers that are likely to abandon a service or product. Consider a yoga studio that has implemented a predictive analytics model. The system may identify that ‘Mary’ will most likely not renew her membership and suggest an incentive that is likely to get her to renew based on historical data. The next time Mary comes into the studio, the system will prompt an alert to the membership relations staff to offer her an incentive or talk with her about continuing her membership. In this example, predictive analytics can be used in real time to remedy customer churn before it takes place.
- Send marketing campaigns to customers who are most likely to buy. If your business only has a $5,000 budget for an upsell marketing campaign and you have three million customers, you obviously can’t extend a 10 percent discount to each customer. Predictive analytics can help forecast the customers who have the highest probability of buying your product, then send the coupon to only those people to optimize revenue.
- Improve customer service by planning appropriately. Businesses can better predict demand using advanced analytics. For example, consider a hotel chain that wants to predict how many customers will stay in a certain location this weekend so they can ensure they have enough staff and resources to handle demand.