Introduction 1.2

Business Values of Data

• Business value of data mining
•  BI Frameworks and Architecture
• Role of data warehouses (ETL, OLAP, data marts)

1. Improved Decision-Making

• Organizations to extract patterns, correlations, and insights from large datasets. By uncovering hidden relationships, businesses can make data-driven decisions rather than relying solely on intuition.
• Example: Retailers predict which products to stock in specific regions based on past sales and customer demographics.

2. Enhanced Customer Understanding

• By analysing customer behaviour, preferences, and feedback, businesses can build detailed customer profiles and predict future needs.
• Value: Improves customer segmentation, personalization, and targeting.
• Example: E-commerce platforms recommend products tailored to individual users, increasing sales and engagement.

3. Operational Efficiency

• Data mining reveals inefficiencies in processes and helps optimize resource allocation.
• Value: Reduced costs, improved supply chain management, and optimized inventory.
• Example: Logistics firms use data mining to plan efficient delivery routes and reduce fuel consumption.

4. Fraud Detection and Risk Management

• Analyzing unusual patterns in transactions can identify fraud or security breaches.
• Value: Reduces financial losses and strengthens trust.
• Example: Banks detect abnormal credit card transactions in real time.

5. Market and Competitive Advantage

• Data mining helps organizations anticipate market trends, monitor competitor strategies, and adapt quickly.
• Value: Gains first-mover advantage in new products, services, or pricing strategies.
• Example: Telecom companies use churn prediction models to retain customers before they switch to competitors.