Data science and analytics is an interdisciplinary field of methods, processes and systems that extract business insights from large volumes of data in a variety of forms. It creates value for businesses, governments and individuals by helping them make better and smarter decisions. In addition to data volume and variety, the challenges of generating value from data stem from the issues associated with the unprecedented speeds at which analytics need to be generated and delivered (velocity), incomplete and inconsistent data (variability), biased, uncertain or ill-intended data (veracity), and protection of private data. When addressing these challenges, value-oriented data science and business analytics research should also consider how to represent, model, analyze, evaluate, optimize and sustain value created by data and analytics.
For this track, we invite paper submissions that propose novel data science methods, techniques, or implementations based on principles in data mining, statistical and machine learning, econometrics, and other computational or quantitative fields. We welcome papers that address important business or decision problems with innovative, value-oriented business analytics in a wide range of contexts, including marketing, healthcare, social network and media, financial services, cyber and homeland security, transportation, and energy, environment and engineering management. We are less interested in existing methods applied to existing problems but with large data sets, conceptual papers on the role of big data, or anecdotes of how big data alone provided managerial insights.