Business Intelligence and Analytics

This course will introduce current technologies and methods in business intelligence (BI) and analytics, including the management of the delivery of information and analytics to support reporting and management decision making. 1) Motivation for BI: The challenge of turbulent business environments: overview, major issues and needs for business intelligence and analytics; The need for analytics and data mining technologies in competitive business environments. 2) Theory behind BI and Analytics: The BI lifecycle model and development approaches, the costs, benefits, return on investment and user community; Business analytics and business performance management: linking strategy to execution, the link between corporate and BI strategy, differences between performance management and measurement; Privacy, ethical and legal issues. 3) Applied BI and analytics: The components and comparison of various common and emerging BIA system architectures; Data integration and the extraction, transformation, and load (ETL) processes, administration and security issues; Data Warehouse modelling and implementation success factors; Review of contemporary BI applications in various industries; Market basket analysis need for analytics and data mining and association rule mining; Modelling customer behaviours and predictions; Clustering analysis and outlier detections; Use of BI and analytics tools in data analysis and knowledge discovery; Introduction into the CRISP-DM framework.