Track 8: Data Analytics and Visualisation in Decision Making
Data analytics and visualisation concerns the techniques, technologies, systems, practices, methodologies, and applications that analyse critical business data and deliver actionable information and insights to managers. This includes both internal organisational data and new external sources of data, including social media data, open data and data collected from sensors, wearables and mobile devices. The insights help managers to better understand their business and market and make timely business decisions. Industry trend studies indicates that analytics has topped CIOs’ agenda for the last decade. Not surprisingly, ‘Data Analytics’ and ‘Visualisation’ have stirred the interest of researchers to study large scale and heterogeneous data and the analytical approaches to improve decision-making processes.
This track invites papers on various aspects of data analytics and visualisation particularly focusing on use, value, innovation and decision making that advance the theory and practice of data analytics and visualisation, as well as associated ethical, cultural and governance issues. Conceptually rigorous, practice-oriented papers that describe analytics and visualization best practices, and provide insights of general relevance, are also welcome.
Potential topics include (but are not limited to) the following:
- Data analytics use and value generation
- Visualization and decision making
- Data analytics and firm performance
- Big data analytics and innovation
- New approaches for performance measurement using data analytics
- Organisational culture and governance issues for data analytics
- Ethics, risks and unintended consequences of big data analytics
- Big data quality criteria and assessment
- Social media analytics
- Emerging methodologies for data visualisation
- Data and text mining for emerging analytics applications
- Data analytics applications such as CRM, HRM, SCM, etc.
- Applications of data analytics in novel and interesting domains such as sports, healthcare, cybersecurity, learning, etc.
- Applications of visualisation techniques on real-time and unstructured data
- Implications of data analytics at the individual, organisational and societal level
- Mobile analytics, cloud analytics, internet-of-things and other trends in analytics research
- Data analytics in small and medium enterprises (SMEs)
- Data analytics best practices