Predictive Analysis: Why It Matters

Data analytics includes five main types of analytics: descriptive, diagnostic, prescriptive, real-time, and predictive. Of those types, predictive analysis answers the question, “What are possible outcomes in the future?” By learning to use predictive analytics, you’re unlocking a powerful set of machine learning and statistical modeling tools you can use across banking, retail, government, and health care industries, among many others.

Explore the ins and outs of this method, including its definition, common techniques, and jobs where you might utilize this skill to imagine the possibilities predictive analysis offers. 

What is predictive analysis?

Predictive analysis uses historical data, or data gathered from past events, to make predictions about scenarios likely to occur in the future. In a way, predictive analysis is an important part of what it means to be human. Many people try to predict the world around us, whether reading a book and guessing at the end or driving a car and predicting what the other drivers will do based on their turn signals. 

Today, technologies like machine learning and data mining fuel robust predictive analysis to help companies solve complex problems. Under the umbrella of predictive analysis, you can find different predictive analytic techniques that can help you solve various issues. Within each type of predictive analysis, you can also find specific models. The assortment of tools available for predictive analytics allows for a wide range of applications and business purposes.  

Types of predictive analysis

Predictive analysis can provide insight into many different types of information. The main types of predictive analytics models include: 

• Classification: Classification models sort data into different categories based on historical data. You can use this model to answer yes or no questions or sort data into groups for additional analysis. For example, a classification model might help identify whether an email is spam or valid, detecting fraud. 

• Clustering: Clustering models also help organize information, but they sort them by attributes instead. For example, you can use this model to sort customers into profiles based on their attributes or preferences, which is helpful in marketing practices.

• Time series models: A time series model can help you predict what will happen in the future by looking at what happened in the past over a given period, with an emphasis on seasonality. For example, any business with seasonal features—such as a restaurant with a summer patio—will need time series models to predict their income over the next year. 

Predictive analytics techniques

Several predictive analytics techniques exist, including popular techniques like regression, decision trees, and neural networks. To decide which is right for you, you must understand what each algorithm does and when to use it. 

• Regression: Regression predicts relationships between variables by identifying key data in continuous and large data sets and looking for patterns to determine how factors influence the asset. For example, a regression model could predict how many customers will purchase an item at a given price point. 

• Decision trees: A decision tree documents your customer’s decision-making process and classifies the data into branching subsets. The predictive model must look for variables that separate data into natural subgroups to do this. Decision trees are popular because users can often easily understand the results, and they provide excellent visuals.

• Neural networks: Neural networks are a predictive analytic technique that uses AI to chart nonlinear data sets or data sets without defined mathematical relationships. They are a more sophisticated tool than simpler decision trees or regression methods, and those working in predictive analysis often use them to examine complex relationships. 

Predictive analytics tools

Predictive analytics tools, such as software and statistical programming languages, help analysts collect and manipulate large amounts of data meaningfully by blending artificial intelligence and business reporting. Two of the most common tools for predictive analysis are R and SAS, each with its own set of benefits and use cases. 

R

R is a programming language for storing, sorting, and manipulating data. It also offers graphical functionality to visualize the relationships between data. As an open-source tool, it is available to everyone for free, and you can adapt how you use R to suit your specific needs. It’s more accessible for individuals or smaller companies. Although R offers a complete environment for analytics, it’s a programming language at its core and can be integrated with other languages like C, C++, and Fortran.

SAS

SAS, or statistical analysis system, is a suite of tools designed to store, sort, understand, visualize, and present your data. As a business intelligence software, SAS includes a selection of tools specifically created for businesses, such as anti-money laundering software, that merge statistics and machine learning. SAS Viya is the newest version of SAS and brings all the system's functionality to a user-friendly platform that features artificial intelligence tools. Although SAS requires a licensing fee, many companies find the user-friendly nature of SAS to be worth the cost, especially for beginner programmers. 

What are predictive analytics used for?

Predictive analytics is an important part of business intelligence technology, but its uses go far beyond your company's bottom line. Predictive analytics has real-world applications across many different businesses, including retail, health care, aviation, and more. 

Below are some specific ways established corporations use predictive analytics to improve their business’s success and outcomes. 

Improve performance with Georgia-Pacific.

Georgia-Pacific used predictive analytics to improve their factory performance in April 2020, when many parts of the US experienced a toilet paper shortage due to supply chain breakdowns during the global pandemic. 

To increase production quickly, Georgia-Pacific used analytics to improve equipment efficiency and produce more paper. The company also used predictive analytics to help manage its logistics department when demand for paper products and cleaning supplies shifted from previous major points of demand, such as hotels, to household demand. 

Optimize marketing campaigns and predict consumer behavior with Ulta Beauty.

Ulta Beauty uses predictive analytics to personalize its digital marketing campaigns and offer customers experiences that are more likely to delight them. It leveraged the data it collected from its customer loyalty and rewards program to make predictions about other products its customers might like. 

Ulta Beauty also uses predictive models to determine the effectiveness of its print marketing campaigns, allowing it to cut costs without sacrificing the program's effectiveness. 

Reduce risk with Lockheed Martin.

Aerospace company Lockheed Martin uses predictive analytics to manage airline maintenance. Predictive analytics can help them determine when parts will need replacing or service, creating a safer, more reliable aircraft fleet. It slo helps manage the cost of unexpected repairs. 

Improve health care diagnostics with Healthy Nevada Project.

The Healthy Nevada Project uses predictive analytics to improve health care diagnostics through its community-based programs. This group provides DNA testing to its community to predict and take preventative action against negative health outcomes associated with genetics and other health factors, such as access to health care. 

Who uses predictive analytics and how to start in the industry

Predictive analysis is relevant to a wide range of industries and applications. If you are interested in a career in predictive analytics, three potential job titles include operations analyst, data scientist, and health care data analyst. 

1. Operations analyst

Average annual base salary: $68,038 [1]

Job outlook (projected growth from 2022 to 2032): 23 percent [2]

Requirements: To become an operations analyst, a bachelor’s degree is typically required (and sometimes a master’s degree), often in business, math, or computer science. 

As an operations analyst, you can help make decisions to improve business operations performance. You can work with data to understand current and emerging business trends and make data-driven predictions about the future. In this role, you may work directly to collect data and develop models to understand and interpret the data, then communicate your findings with senior leadership. 

2. Data scientist

Average annual base salary: $112,419 [3]

Job outlook (projected growth from 2022 to 2032): 35 percent [4]

Requirements: A bachelor’s degree in math or statistics is typically required to become a data scientist. 

As a data scientist, you can collect and analyze data. You’ll also use your results to make recommendations. In this role, you may create algorithms to interact with data. You may also be responsible for data visualization, such as creating charts and other graphic interpretations of the data. Data scientists work to make data accessible to different stakeholders. 

3. Health care data analyst

Average annual base salary: $93,271 [5]

Job outlook (projected growth from 2022 to 2032): 35 percent [4]

Requirements: Health care data analysts typically require a similar degree as a data scientist, such as a bachelor’s degree in math or statistics. A master’s degree is sometimes preferred. 

As a health care data analyst, you can use health care data to improve costs, patient outcomes, and quality of care. In this position, you’ll collect and analyze data to make recommendations for what actions hospital administrators can take to improve performance. Health care data analysts help manage the vast amount of data generated in a health care setting and make it actionable and interactive. 

Learn more with Coursera

Predictive analytics is a powerful data analysis technique that can be used in any industry to predict the likelihood of future events. By analyzing historical data and trends, predictive analytics can provide insights into future sales, stock prices, patient outcomes, and more.

If you want to pursue a career in data analytics, take the next step and earn your Google Data Analytics Professional Certificate, offered on Coursera. You can learn valuable skills, including R programming, data cleaning, and presenting data findings in as little as six months, and build relevant skills for a junior or associate data analyst career.