Perhaps one of the most dramatic results of the Fourth Industrial Revolution is the amount of data that is being created and collected every day. From 2010-2020, the creation, capturing, copying, and consumption of data rose by 5000%. In the article Business Analytics: Why it's Important for Businesses and Professionals we looked at the organizational benefits of data analytics and the importance of having a data analytics strategy. This article examines a specific type of data analytics - predictive analytics.

Predictive analytics is a branch of advanced analytics that uses data mining techniques, statistic modeling, and machine learning to look at current and historical data to detect trends and patterns. Identified patterns can be used to forecast future events more accurately and reliably than previous tools. Many companies utilize predictive analytics to identify risks and opportunities, save money, increase revenue, learn from previous business tactics, and more. A large range of organizations have adopted predictive analytics and the industry is expected to grow. The global market is projected to reach approximately $10.95 billion by 2022. 

Advantages of Using Predictive Analytics  

Predictive analytics is a core practice necessary for competing in our data-driven world. According to a whitepaper by Prediction Impact Inc. and IBM, seven strategic objectives can only be achieved by utilizing predictive analytics - compete, grow, enforce, improve, satisfy, learn and act.  

  • Compete - Business intelligence helps companies improve business processes and customer experience. It also informs companies of their competitors' offerings and marketing tactics along with their strengths and weaknesses. Both factors enable businesses to gain and maintain a competitive advantage.
  • Grow - Marketing and sales applications are a flagship benefit of predictive analytics capabilities. Improved marketing and sales tactics reduce cost, increase sales and help organizations attract and retain customers. 
  • Enforce - Predictive modeling technologies help identify fraud faster and more accurately than a team of investigators. This protects a companies integrity and saves them money. 
  • Improve - Predictive analytics helps organizations improve their product and the efficiency in which it is manufactured and/or delivered.
  • Satisfy - Customer experience is one of the greatest markers for business success. Predictive analytics helps provide customers with better products more easily and more reliably, for less money.
  • Learn - Predictive analytics is the most robust and advanced analytics tool with the capacity to extrapolate lessons from historical data to serve a specific goal at hand. 
  • Act - Unlike standard forms of business intelligence, predictive analytics is specifically designed to provide conclusive, actionable insights and imperatives.  

Use Cases  

Predictive analytics models can be used in any industry, but here are a few examples of how it can be applied: 

  1. Aerospace - Predict the impact of specific maintenance operations on aircraft reliability, fuel use, and uptime. Commercially, most large airlines use current data trends and historical data to predict travel patterns to set ticket prices and flight schedules as well as predict the impact of things like price changes, policy changes, and cancelations. 
  2. Energy - The energy sector uses predictive analytics to forecast supply and demand ratios and predict the impact of equipment costs, outages, and other variables. It can also make a big impact on renewable energy. Renewable energy is a relatively unpredictable energy source because it relies heavily on airflow, water, and sunlight - all of which are controlled by unforeseeable weather conditions. Predictive models look at current and historical weather data to provide more accurate forecasting. Companies can manage their intake and output based on these predictions, making it more efficient and effective. 
  3. Financial services - Big data, artificial intelligence, and machine learning have revolutionized the financial services industry over the last few years. The data from these technologies can provide early fraud detection and forecast the impact of market trends, laws, and regulations. Predictive banking is also completely reshaping the customer experience. Customers can sign up for different mobile app prompts to notify them if they've had higher than usual recurring billing payments, remind them to transfer money to their savings if they have a higher than normal balance in their checking, prompt them to set up travel plans if they've just purchased a plane ticket and much more. 
  4. Healthcare - Predictive models detect early signs of patient deterioration in the hospital, identify at-risk patients and deliver predictive care at home, and prevent downtime of equipment. John's Hopkins University researchers recently developed a machine-learning algorithm that can identify suicidal thoughts and behaviors in adolescents. The team analyzed data from a survey of high schoolers in Utah over seven years and they were able to predict with 91% accuracy which students' answers indicated suicidal thoughts and behaviors. The findings can be used in suicide prevention programs and policies and hopefully improve the rate of suicide among adolescents. 
  5. Manufacturing - Using predictive analytics, companies can predict the impact of machine failure, forecast material demand, improve supply chain operations and understand customer behavior. Manufacturers are also using advanced data analytics to predict the risk of unionization - something that keeps many CEOs up at night. The predictive models can be quite complex. Reportedly, Whole Foods used more than two dozen variables including sales performance, employee diversity index, and team member satisfaction to create a store-level unionization risk score. Joseph Brock, a former union president turned labor relations consultant believes that this data can enable organizations to hear their employees' concerns and grievances to make positive changes.  
  6. Retail - The COVID-19 pandemic has led to accelerated digital innovation in the e-commerce space which has led to an increase in many technologies, including predictive analytics. Retailers are better able to follow customers in real-time, deliver targeted marketing and incentives, forecast inventory requirements, and configure their website (or store) to increase sales.


There are many technologies and tools on the market that give companies deep, real-time insights into any business activity. Almost all organizations use a third-party tool that is tailored to meet their specific needs. Some of the common predictive software and service providers include: 

  • Acxiom
  • IBM
  • Information Builders
  • Microsoft
  • SAP
  • SAS Institute
  • Tableau Software
  • Teradata
  • TIBCO Software

Is Predictive Analytics the Same as Data Mining?  

If you've heard of predictive analytics, you've probably heard of data mining. But what is the difference and how do they relate? Although data mining is often confused with data analytics, data governance, and other data processes, it is a very specific analytics technique and is used in congruence with predictive analytics. Data mining involves cleaning and preparing data, creating models, testing models against hypotheses, and publishing models for analytics or business intelligence projects. Predictive analytics then uses the information to make predictions.  

Careers in Predictive Analytics 

Predictive analytics careers are on the rise and professionals in this field have a high earning potential. According to ZipRecruiter, the average salary for someone in predictive analytics ranges from $90k to $180k. However, professionals in any analytics position can benefit from having predictive analytics and data mining knowledge and experience.  

At Thunderbird, we recognize the growing importance of data analysis and are proud to offer an exclusive, online continuing education program in Data Mining and Predictive Analytics. The program is taught by W. P. Carey Master of Science in Business Analytics (MS-BA) program faculty with real-world experience and expertise. Through the program, students will: 

  • Learn how data mining changes the innovation equation in organizations.
  • Understand the drivers and determinants of disruptive innovation and how best to leverage data mining.
  • Develop a practical, business-focused understanding of the three different orientations to data mining: exploratory, predictive, and forensic.
  • Understand how to develop new business opportunities or drive innovation in organizations by leveraging data mining.
  • Establish an operational (hands-on) understanding of data-driven decision-making using data-mining tools and techniques to assist managers to take one of three (exploratory, predictive, forensic) perspectives to data.

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