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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, a core course in the Master of Science in Business Analytics online program at the University of North Carolina Wilmington, is a category of analytics aimed at using historical data and analytics techniques to make predictions about future outcomes.

The UNCW course provides students with an overview of various predictive analytics techniques. Participants leave the course with the tools needed to help guide data preparation and analysis to develop quantitative predictions for their organization.

Looking Into the Future

The science of predictive analytics can provide insights into the future with a considerable degree of precision. It has gained support across a range of organizations, with a global market projected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 percent between 2016 and 2022, according to a CIO mention of a 2017 Zion Market Research report.

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 diverse, often substantial, data sets into models that can generate clear, actionable outcomes to support achieving the stated goal.

Growing Use of Analytics

Industries as varied as finance, healthcare, pharmaceuticals, automotive, aerospace, financial services, healthcare, manufacturing and others can make use of predictive analytics to work toward achieving 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, seasonality 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 that may need 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.

Organizations in a number of industries are using predictive analytics in a virtually endless number of ways today. What this means for UNCW students is that they can expect strong career opportunities when they complete the program.

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


CIO: What Is Predictive Analytics? Transforming Data Into Future Insights

MathWorks: Predictive Analytics - 3 Things You Need to Know

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