Data that was once difficult and expensive to obtain and maintain is quickly becoming commoditized in our modern, connected society. Consumers and firms have both reached a point where data is almost taken for granted. Businesses can easily obtain customer purchase data through inexpensive point-of-sale systems and loyalty programs. Individuals can access their own health and exercise data through fitness trackers and smart watches. Google (for better or worse) knows my driving habits and locations that I frequent and proactively pushes traffic data to me before I even get in my car. 
 
These are just a few examples of how consumers and businesses are increasingly becoming “data rich.” However, with this abundance of data comes a set of challenges. Fortunately, business analytics tools, in the hands of well-trained professionals, can confront these challenges and allow businesses and individuals to transform data into value-added information. This information leads to insights that can then be used to drive decision-making. For example, a retailer may become aware of their customers’ purchasing patterns and be able to more accurately target them with relevant advertisements and promotions. An individual may become aware of long periods of inactivity and implement an exercise program.   
 
One of the initial challenges associated with an abundance of data is data cleaning. Data that is collected by a business can be “dirty” in the sense that it can be incomplete or inaccurate. A variety of analytical techniques exist to clean data by identifying potentially inaccurate data and imputing or estimating missing values. Data, once cleaned and organized, becomes information. The challenge then becomes how to extract actionable value from the information. Visualization and statistical techniques can be employed to succinctly present and summarize information. For example, seasonal and other patterns in the information may be readily observed when visualized. Machine learning methods can also identify patterns in the information. For example, Internet advertisements may be served to website visitors through an automated auction approach based on machine learning techniques that seek to maximize desired advertising objectives.  
 
With firms of all sizes and individual consumers seeking the benefits of business analytics, the need for skilled business analytics has never been greater. Fortunately, a wide variety of training is available from traditional and online education outlets. At UNCW, business analytics is a major component of the operations management undergraduate degree program in the Cameron School of Business. For more information on business analytics, contact Dr. Stephen Hill at [email protected]. Furthermore, UNCW’s Swain Center provides affordable professional development and continuing education opportunities for businesses and individuals in southeastern North Carolina. Among these opportunities is our Data Analytics Using Excel class that is scheduled to be taught in October. For more information, please contact the Swain Center’s Diane Badakhsh at [email protected].
 
Dr. Robert T. Burrus, Jr. is dean of the Cameron School of Business at the University of North Carolina Wilmington. Before taking on the role, Burrus was the department chair for economics and finance and a professor of economics. He has been on Cameron’s faculty since 1998. The Cameron School of Business has 90 full-time faculty members and 29 administrative and staff members. The school hosts approximately 2,000 undergraduate students and 170 graduate students. International students come to study at Cameron from all over the world. The Cameron School of Business is AACSB accredited; offers capstone experiences; houses a Financial Trading Markets Room; provides for overseas learning opportunities; and is a founding member of the Trans-Atlantic Business School Alliance. To learn more about the Cameron School of Business, please visit http://csb.uncw.edu/. Questions and comments can be sent to [email protected].