Track 2: Big Data Analytics for
Business Insights
Organisations generate data at a scale and speed
that now shape how managers analyse performance, allocate
resources, monitor risk, understand customers and design
operations. Data volume alone does not improve decisions.
Business value depends on sound analytical methods, reliable
data, clear governance, transparent models and a direct link
between evidence and managerial action.
The track provides a forum for research on the use of big data
analytics in business decision-making. It welcomes empirical
studies, methodological papers, design-oriented research,
applied models and case studies covering predictive analytics,
data mining, real-time analytics, business intelligence,
decision support, data governance and data quality. Work linking
analytics with finance, accounting, risk management, marketing,
operations, supply chains, digital platforms, entrepreneurship
and public-sector management fits the track.
Particular attention goes to reproducible analytical workflows,
model validation, explainability, privacy, responsible data use,
integration of structured and unstructured data, forecasting,
anomaly detection, scenario analysis and decision dashboards.
Cross-disciplinary studies that connect technical methods with
measurable organisational outcomes are especially welcome.
Authors should state the decision problem, data source,
analytical design, validation process and managerial
contribution with precision.
Researchers and practitioners are invited to submit work on the use
of big data analytics for business decisions, forecasting, control,
risk assessment and operational performance. The track welcomes
empirical, methodological, design-oriented and applied studies with
a clear decision context. Topics of interest include, but are not limited to:
- Predictive analytics, forecasting and data mining
- Real-time analytics and event-driven decision systems
- Business intelligence, dashboards and decision support
- Data governance, data quality and data lineage
- Machine learning for business prediction and classification
- Customer, marketing and behavioural analytics
- Financial, accounting and risk analytics
- Supply-chain, operations and logistics analytics
- Anomaly, fraud and exception detection
- Text, document and unstructured-data analytics
- Model validation, explainability and analytical controls
- Privacy, ethics and responsible data use
Submission System:
https://www.zmeeting.org/submission/ICSEB2026.
Submission due: 15 October 2026;
Notification due: 15 November 2026;
Registration due: 25 November 2026.
(Please log into the submission system and select Track 2 for
your submission)