The primary privacy issue with data mining is if results enable a user to identify the person to which the data belongs. The degree of risk of that happening depends on the extent to which the organization stores personally identifying data and how protected its system is from fraudulent breaches that allow others to use the data for malicious purposes. Such breaches have been substantiated causing increasing concerns over how patterns revealed over time may also lead to identification even if no personally identifying data is stored. As predictive models used by analysts become increasingly more precise at supporting market strategizing decisions, it stands to reason that wrongdoers will avail themselves of the same tools and devise more sophisticated ways to use them maliciously.
A long time CPA turned online professor, Kathleen’s life was transformed upon completion of her dissertation An Investigation of the Factors that Influence Faculty and Student Acceptance of Mobile Learning in Online Higher Education.
Her statistical analyses was called ”pioneering” by her committee chair Dr. Marlyn K. Littman and brought Kathleen full circle back to her number-crunching roots inspiring her to earn a second master’s in Business Intelligence.
Kathleen plans to continue her studies of contemporary issues related to teaching, learning, and technology and loves to help undergrad and grad students achieve their academic and professional goals. As a lifelong learner she also plans on continuing her quest to understand the problems posed by mobile and micro learning formats and find innovative ways of helping people maximize the benefits these emerging technologies afford.