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What Is Predictive Analytics?

There’s been a lot of buzz about predictive analytics in recent years, largely due to advances in supporting technology. Predictive analytics is the central focus of a core course in the Master of Science (M.S.) in Business Analytics online program at the University of North Carolina Wilmington (UNCW). Predictive analytics is a category of data analytics aimed at using historical data and analytics techniques to predict future outcomes.

The Predictive Analytics course gives students an overview of various predictive analytics techniques. Participants leave the course with the tools to help guide data preparation and analysis to develop quantitative predictions for their organizations.

Looking Into the Future

The science of predictive analytics can provide insights into the future with a considerable degree of precision. As a result, predictive analytics has gained support across a range of organizations and industries. The global market for predictive analytics is projected to reach approximately $40.3 billion by 2027, growing at a compound annual growth rate (CAGR) of 18.4% between 2020 and 2027, according to GlobeNewswire.

Predictive analytics tools and models allow any organization to use past and current data to consistently forecast future trends and behaviors — whether the future is mere milliseconds, days or years away. The use of historical data to build a mathematical model that captures important trends is a typical example. That predictive model is then applied to current data to forecast what will happen next or to suggest actions to take to achieve the best results.

Applying predictive analytics starts with a business goal, such as using data to reduce waste, save time or cut costs. The process harnesses and aggregates diverse, often substantial data sets and, using technologies like machine learning, analyzes those data sets to generate clear, actionable insights which support decision-making and desired goals and outcomes.

Growing Use of Analytics

Industries as varied as finance, healthcare, pharmaceuticals, automotive, aerospace, financial services, healthcare, manufacturing and others can use predictive analytics to achieve their goals.

There is a wide range of predictive analytics applications in an equally wide range of industries:

  • Energy production — To forecast electricity price and demand. Complex forecasting apps use models that monitor plant availability, historical trends, maintenance needs and weather. The impact of weather events, equipment failure, regulations and other variables on service costs can also be predicted.
  • Aerospace — To gauge the impact of specific maintenance operations on aircraft reliability, fuel use, availability and uptime.
  • Automotive — To incorporate records of component sturdiness and failure into upcoming vehicle manufacturing plans or to study driver behavior to develop better driver assistance technologies and, eventually, autonomous vehicles.
  • Financial services — To develop credit risk models and forecast financial market trends. In addition, the impact of new policies, laws, and regulations on businesses and markets can also be predicted.
  • Manufacturing — To predict the location and rate of machine failures and to optimize raw material deliveries based on projected future demands.
  • Law enforcement — To analyze crime trend data to identify neighborhoods needing additional protection at certain times of the year.
  • Retail — To follow an online customer in real-time to determine whether providing additional product information or incentives will increase the likelihood of a completed transaction.

Modern organizations in various industries are using predictive analytics in a virtually endless number of ways. Given this, UNCW students can expect strong career opportunities when they complete the advanced analytics degree program.

Learn more about UNCW’s online M.S. in Business Analytics program.

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