Real-World Applications of Predictive Analytics

The field of data analytics is the set of techniques used to analyze raw data (unprocessed data) in order to extract relevant information, trends, and insights. This process includes collecting data, organizing it, and storing it, then performing statistical analysis on the data. Once the information is collected, conclusions can be drawn from it, which can be presented visually to both those working within an organization as well as to external stakeholders, who can then apply it to problem-solving, business processing, decision-making, and predictions that can inform what a company’s next steps should be. This process relies on disciplines like mathematics, statistics, and computer programming.

The 4 Types of Data Analytics

Generally speaking, there are four types of data analytics:

• Descriptive Analytics measures data trends that occur over a designated time period.
• Diagnostic Analytics is concerned with why an event happens and any hypotheses can be used to explain this causality.
• Predictive Analytics suggest what events may unfold in the near future.
• Prescriptive Analytics is an advanced type of analytics that involves suggesting a course of action or solution based on a noticeable trend.

This article will take a deeper look at predictive analytics, as well as several of its real-world applications.

What is Predictive Analytics?

The field of predictive analytics draws on summarized data, as well as past trends and behaviors, to offer logical predictions on what may occur in the future. This branch of analytics incorporates statistical modeling to make forecasts that seek to answer the questions: “What happens if?” and “What could happen next?” The accuracy of these forecasted estimates depends on the quality of the data being used.

However, unlike descriptive and diagnostic analytics, which are common to many businesses, predictive analytics is used less frequently. Not all companies have the desire or resources to implement predictive analytics, as it often requires a combination of advanced statistical algorithms and machine learning.

Real-World Applications of Predictive Analytics

Businesses and organizations that rely on predictive analytics to tackle problems as well as to discover new opportunities receive many benefits from implementing this form of analytics. Predictive models are used on a daily basis for sales forecasting, risk assessment and mitigation, sports analytics, and fraud detection. This field of data analytics informs weather forecasts, video game development, and customer service decisions, as well as other future-looking products or services.

Here are some of the ways predictive analysis is currently being used in various industries:

• Marketing campaigns: Applying predictive models to a business provides valuable insights into customer purchasing patterns, as well as responses to products and services. It also assists businesses with attracting and retaining profitable customers.

• Fraud detection: Criminal behavior can be detected using analytic methods like predictive analytics that apply pattern detection techniques. By studying network actions in real-time, threads, abnormalities, and vulnerabilities can be spotted and flagged. It also is a powerful tool for uncovering health insurance billing fraud, especially Medicaid fraud.

• Retail: Predictive analytics offers helpful insights into online customer actions as they occur in real-time so that incentives or supplementary product information can be offered when needed so that the customer will be more likely to complete their transaction.

• Automotive: Predictive analytics has several applications in the automotive industry. It is used to analyze the behavior of drivers so that improved driver assistance technologies can be introduced, which eventually can lead to the development of autonomous cars. In addition, predictive models are applied to manufacturing plans to monitor the sturdiness of car parts and potential for failure.

• Risk reduction: One of the most common uses of predictive analytics pertains to credit scores. By reviewing a buyer’s credit history, it’s possible to get a picture of that person’s likelihood to default on a purchase.

• Law enforcement: Crime trend data can provide valuable information pertaining to which neighborhoods are at a higher risk for crime, and when specifically crime tends to occur in these neighborhoods so that law enforcement can devote additional efforts to provide protection when it is most needed.

• Aerospace: When applied to air travel, predictive analytics helps to estimate the potential impact of maintenance projects on such variables as fuel use, uptime, and aircraft reliability.

• Healthcare: Predictive analytics has many uses in the healthcare industry:
  • In terms of diagnosis, predictive analytics can detect malignant mesothelioma in patients early on so that treatment can be started, thus improving the chances of survival.
  • With regard to prognosis, physiological data from patients with congestive heart failure can help to forecast which are at the highest risk for hospital readmission. This allows medical professionals to propose early interventions to cut back on eventual readmissions, as well as to evaluate patient risk for post-surgery infection, sepsis, or cardiac arrest.
  • When treating patients, predictive analysis provides a way to track medications and monitor vital signs.
  • Pharmacy benefits companies like Express Scripts apply predictive analytics to locate those who are not following their prescribed treatment plan. This can lead to thousands of dollars of savings per patient.

• Resource management: Companies in various industries incorporate predictive models to decide how much inventory to stock, as well as to manage the resources necessary to do so.

• Energy: When applied to various aspects of the energy sector, predictive analytics can help anticipate price and demand ratios. It also can forecast the potential impact of variables like equipment failure, regulations, and severe weather events on the cost of service.

• Power Plants: In order to cut down on the number of unanticipated equipment failures, power plants and wind power installations apply predictive analytics models. This can subsequently cut back on the cost of maintenance as well as increase the availability of power.

• Government: For decades, the US Census Bureau has applied data analytics to study population trends. It also has applications in other industries, such as improving cybersecurity measures, detecting fraudulent behavior, and improving performance and service.

• Hospitality: Predictive analytics plays an important role in the hospitality industry. It can help casinos and hotels predict their staffing needs for specific times, such as during the holidays or when a major sporting event or concert is in town. This not only ensures that venues will be properly staffed to handle crowds but helps to prevent overstaffing, which wastes revenue, affects customer service, and leaves employees feeling overworked.

Regardless of the industry to which it is applied, predictive analysis is an important tool that empowers users to gather the most helpful insights in order to take the most effective action. It can cut down on excess costs, increase efficiency, and even provide life-saving medical information for disease prevention and treatment.

Learn Data Analytics with Hands-On Classes

A great way to learn more about how to perform predictive analysis on data is to enroll in one of Noble Desktop’s data analytics classes. Courses are offered in New York City, as well as in the live online format in topics like Python, Excel, and SQL.

Other great data analytics classes are also available from top providers. More than 130 live online data analytics courses are currently listed, in topics like FinTech, Excel for Business, and Tableau, among others. Courses range from three hours to six months and cost from $219 to $27,500.

Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others. These rigorous courses are taught by industry experts and provide timely, small-class instruction.

Are you looking for a data analytics class nearby? If so, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse approximately 400 data analytics classes currently offered in in-person and live online formats. This convenient tool makes it easy to find the class that’s a match for your professional goals and budget. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.