The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Data Mining and Knowledge Discovery
Class description using partial coverage of subspaces
Expert Systems with Applications: An International Journal
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
AnyOut: anytime outlier detection on streaming data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Mining big data: current status, and forecast to the future
ACM SIGKDD Explorations Newsletter
| Hi-index | 0.00 |
Business analytics, occupying the intersection of the worlds of management science, computer science and statistical science, is a potent force for innovation in both the private and public sectors. The successes of business analytics in strategy, process optimization and competitive advantage has led to data being increasingly recognized as a valuable asset in many organizations. In recent years, thanks to a dramatic increase in the volume, variety and velocity of data, the loosely defined concept of "Big Data" has emerged as a topic of discussion in its own right -- with different viewpoints in both the business and technical worlds. From our perspective, it is important for discussions of "Big Data" to start from a well-defined business goal, and remain moored to fundamental principles of both cost/benefit analysis as well as core statistical science. This note discusses some business case considerations for analytics projects involving "Big Data", and proposes key questions that businesses should ask. With practical lessons from Big Data deployments in business, we also pose a number of research challenges that may be addressed to enable the business analytics community bring best data analytic practices when confronted with massive data sets.