The field of data analytics doesn’t just provide valuable insights into current trends, but it helps to predict what may occur based on past and current actions. There are four kinds of data analytics: descriptive, diagnostic, prescriptive, and predictive, which can be used independently, as well as in concert, for a company’s specific analytic needs. This article will take a closer look at predictive analytics and its various professional applications.
What is Predictive Analytics?
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 question: “What happens if?” The accuracy of these forecasted estimates depends on the quality of the data.
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 requires a combination of advanced statistical algorithms and machine learning. However, companies that use predictive models tend to see immense benefits, in terms of customer retention and satisfaction, as well as revenue.
Predictive models have many real-world applications. This article will explore some of the main uses of predictive analysis, as well as several benefits and drawbacks of applying predictive models to datasets.
Real-World Applications of Predictive Analytics
The following are some of the main uses of predictive analytics in various professional sectors:
- Predictive maintenance: This relatively new use of predictive analytics can be applied to the manufacturing sector. It is designed to alert manufacturers of any activities that could affect the operations of their industrial equipment. This technique relies on analyzing large amounts of data via sensors that are embedded in the equipment and allows for intervention procedures before a breakdown or malfunction can happen.
- Real estate: Predictive analysis plays an important role in various aspects of the real estate industry. It has applications for real estate brokers, who can use it to offer potential homebuyers a projected home value so that their houses can be priced accordingly. In addition, when predictive analytics is applied to Census data, it can provide valuable insights into homebuyers who may soon wish to sell their properties due to changes in their life circumstances.
- Retail: Large retail chains use predictive analytics to learn everything they can about customers. They are interested in customers’ buying habits at various points in time, such as during the holidays or around natural disasters. Amazon incorporates predictive analytics to offer its customers personalized recommendations that are generated based on what they have previously purchased.
- Cybersecurity: Each year, billions of dollars are lost due to fraudulent activities. With the help of predictive analytics, activities that are deemed potentially fraudulent can be analyzed, and then predictive models can be generated to detect anomalies. This process allows fraud to be detected earlier based on patterns of suspicious financial activity.
- Weather: Over the past several decades, weather forecasting has become much more accurate thanks to the use of predictive analytics. It’s now possible to provide weather forecasts a month in advance by analyzing historical data and satellite imagery. In addition, predictive analysis is a powerful tool for helping humans understand how global warming is impacting the planet. When paired with data visualizations, it’s possible to visually depict such trends as rising carbon dioxide and sea levels, as well as to forecast where these levels are headed. If enough information is gathered and interpreted, action can be taken to counter adverse effects.
- Entertainment content: Within the entertainment industry, digital entertainment options often use predictive analytic techniques to help transform viewer experience. Netflix uses predictive models to offer suggestions about which shows customers may be most interested in based on those they have previously watched.
- Sports: In order to stay competitive in professional sports, predictive analysis can be applied in a variety of ways. It can be used to forecast how valuable a player may be in the future, as well as to inform a team about how best to maximize their budget.
- Healthcare: Perhaps the most widespread use of predictive analysis is in the healthcare industry. Health data plays a vital role in patient care; it helps doctors and healthcare providers get a full picture of a patient’s medical history, as well as any current illnesses they may be battling. This information plays an integral role in helping to diagnose patients. By looking at specific health factors, predictive analysis can be used to identify the underlying cause of a disease. This allows for early treatment options, which mitigate the negative health effects that can occur from waiting too long to diagnose and treat serious illnesses.
Benefits & Drawbacks of Using Predictive Analytics
There are both benefits as well as drawbacks to using predictive analysis:
- Predictive analysis offers managers and executives a powerful tool for making informed decisions about sales, product development, and revenue forecasting.
- This form of analytics can help an organization save time and money.
- It can lead to earlier diagnosis of illnesses and earlier detection of major weather events, which improve overall quality of life for stakeholders.
- Predictive analysis offers insights into early signs of customer dissatisfaction, which can be used by a seller to apply corrective measures to address the problem and retain the customer.
- In order for an organization to make data-driven decisions, it’s important for them to have access to as much data as possible. However, it’s sometimes difficult to acquire large datasets.
- Not all predictive models can account for aspects of human behavior or other unexpected factors, such as sudden fluctuations in weather patterns, interpersonal relationships, and moods.
- Because customer behavior changes over time, it’s important to update predictive models so that disasters such as the 2008-2009 financial crisis can be avoided.
- Privacy is one of the main issues concerning predictive analysis. Although regulations and laws are in place to help ensure privacy is maintained, large corporations still collect private data.
Despite the drawbacks to using predictive analysis, it remains a powerful tool for Data Analysts who are looking to make accurate decisions based on past and current events, trends, and data.
Hands-On Data Analytics Classes
Do you want to learn more about Data Analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data.
In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to six months and cost from $219 to $27,500. Students can study from the comfort of their own home or office space and still receive industry-relevant data analytics training.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. 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.
For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.