The unprecedented increase in the amount of data available for processing has created novel innovative opportunities for individuals, organizations, and society. This is creating a huge impact across industries (e.g. healthcare, finance, energy, retail and sports) when engaging in complex analytical tasks. The ability to manage big data and generate insights is also leading towards significant organizational transformation. At a higher level, big data and analytics applications are driving positive impact in society in areas, such as health and well-being (e.g. in the fight against Covid19), poverty mitigation, food security, energy, and sustainability. Organizations are allocating greater resources to enhance and develop new innovative applications of advanced analytics capabilities. As organizations transform into data and analytics centric enterprises (e.g. health insurance companies, automobile companies), more research is needed on the technical, behavioral, and organizational aspects of this progress. On one hand, research focused on the creation and application of new data science approaches, like deep learning and cognitive computing, can inform different ways to enhance decision making and improve outcomes. On the other hand, research on organizational issues in the analytics context can inform industry leaders on handling various organizational and technical opportunities along with various challenges associated with building and executing big data driven organization. Examples include data and process governance and ethics and integrity issues, management and leadership, and driving innovation and entrepreneurship.

The track “Data Science and Analytics for Decision Support” seeks original research that promotes technical, theoretical, design science, pedagogical, and behavioral research as well as emerging applications in analytics and big data. Topics include (but are not limited to) data analytics and visualization from varied data sources (e.g. sensors or IoT data, text, multimedia, clickstreams, user-generated content) involving issues dealing with curation; management and infrastructure for (big) data; standards, semantics, privacy, security, legal and ethical issues in big data, analytics and KM (knowledge management); intelligence and scientific discovery using big data; analytics applications in various domains such as smart cities, smart grids, financial fraud detection, digital learning, healthcare, criminal justice, energy, environmental and scientific domains, sustainability; business process management applications such as process discovery, performance analysis, process conformance and mining using analytics and KM, cost-sensitive, value-oriented, and data-driven decision analysis, and optimization. Visionary research on new and emerging topics that make innovative contributions to the field are also welcome.

Track Chairs:
Ciara Heavin, University College Cork, c.heavin@ucc.ie
Aleš Popovič, University of Ljubljana, School of Economics and Business, ales.popovic@ef.uni-lj.si
Vic Matta, Ohio University, matta@ohio.edu