Online Business Intelligence Fall 2024

Understanding the Lecture Material The first lecture introduces the concept of web analytics, defined as the measurement, collection, analysis, and reporting of internet data to optimize web usage. Avinash Kaushik's Web Analytics 2.0 framework emphasizes analyzing both qualitative and quantitative data to enhance customer experiences and achieve desired outcomes. The lecture explains the types of web traffic—direct, organic, referral, and campaign—and key metrics like bounce rate, exit rate, and the number of visitors. Moreover, it discusses Key Performance Indicators (KPIs), such as conversion rate and task completion rate, which are crucial for measuring the success of online goals. The second lecture shifts focus to Google Analytics, a comprehensive tool for capturing website traffic, user behavior, and conversions. It highlights features like dashboards for real-time data, audience analysis, and traffic source categorization. Emphasis is placed on visitor behavior, such as e...

Data Warehouse Design Cycle The focus of this lecture was on data warehouses—central repositories of integrated, high-quality information used for decision-making. We explored the differences between OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems. OLTP systems handle day-to-day operations with real-time data, while OLAP systems focus on analyzing both current and historical data. Key steps in the Data Warehouse Design Process : 1. Extract, Transform, Load (ETL) : Data is extracted from operational databases, transformed into a usable format, and loaded into the warehouse. 2. Data Staging : Ensures data consistency and quality before it enters the warehouse. Reflection: This lecture emphasized the strategic value of data warehouses for organizations. They provide the foundation for effective dashboards and analytics. Balanced Scorecard We learned about the Balanced Scorecard (BSC), a performance management framework linking organizational str...