Data Science

Éditeurs invités

• Dries F. Benoit, Ghent University
• Kristof Coussement, IÉSEG School of Management
• Cem İyigün, Middle East Technical University
• Asil Oztekin, University of Massachusetts Lowell

Synthèse

This special section seeks to publish research that bridges data science and operations research, emphasizing both theoretical advances and practical organizational applications. The focus extends beyond technical innovation to address how analytics creates measurable value within organizations and drives necessary organizational change.

The collection invites contributions exploring the intersection of OR and analytics across multiple dimensions: ethical and governance considerations in data usage, challenges of applying OR techniques to big data and distributed systems, organizational barriers to analytics adoption, data quality and validation methods for large datasets, and the role of visualization and soft OR techniques in supporting data-driven decision making.

Thèmes proposés

• Ethics and governance issues in analytics: How should data be obtained? What are the ethical implications of using applications of analytics to influence behavior?
• Big data and analytics: What are the limitations and applications of optimization and other OR techniques to large datasets? What are the challenges for applications of OR methods within distributed systems? What is the possibility that OR models could in fact be the producers of big data, e.g., large-scale simulation models? What new methods/models in response to big data, e.g., sentiment mining, can be adopted by OR?
• Organizational issues in analytics adoption: What are the issues facing organizations trying to adopt analytics? What is the role of real-time applications of OR in organizations?
• Data quality and analytics: What methods can be used for hypothesis testing and model validation in large datasets? How can unstructured data be used effectively in OR models? What is the role of multi-methodology in business analytics? What opportunities do open data present for the OR discipline?
• Analytics and decision support: How can data visualization techniques be used across the breadth of OR? What role do problem structuring and "soft" OR techniques play in analytics and big data projects?