Unit-1 Data warehousing

Case Study: Implementing a Data Warehouse in a Retail Company Company Background A large retail company, RetailCo, faced challenges managing and analyzing vast amounts of sales data across multiple stores and online platforms. The existing system was slow, leading to delayed insights and decision-making. Objectives 1. Centralize data from various sources. 2. Improve data accuracy and consistency. 3. Enhance reporting and analytics capabilities. 4. Reduce data retrieval time. Case Study: Building a Data Warehouse for HealthCare Inc. Background HealthCare Inc. is a hospital network struggling with fragmented patient data across various departments. They need a unified data repository to streamline data access, improve patient care, and enhance decision-making. Objectives 1. Data Centralization: Integrate data from electronic health records (EHR), lab systems, and billing. 2. Data Quality: Ensure accuracy, consistency, and completeness. 3. Advanced Analytics: Enable predictive analytics for patient outcomes and operational efficiency. 4. Compliance: Maintain data privacy and security as per regulations like HIPAA. Case Study: Building a Data Warehouse for RetailCo Background RetailCo is a mid-sized retail company with numerous stores across the country. The company has been collecting data from various sources, including point-of-sale (POS) systems, online transactions, customer loyalty programs, and supply chain management systems. The existing data management approach was fragmented, leading to inefficiencies and a lack of comprehensive insights. RetailCo decided to build a data warehouse to consolidate its data, enabling better business intelligence and decision-making. Objectives 1. Centralized Data Storage: Consolidate data from multiple sources into a single repository. 2. Improved Data Quality: Ensure data accuracy, consistency, and completeness. 3. Enhanced Reporting and Analytics: Provide robust tools for data analysis and reporting. 4. Scalability: Ensure the data warehouse can grow with the business. 5. Security: Protect sensitive data and ensure compliance with regulations. Case Study: Mapping a Data Warehouse for Global Retailer Background A global retail company, Global Retail, was struggling with fragmented data across various regions, leading to inconsistent reporting and inefficiencies. Objectives 1. Centralized Data Source: Integrate regional databases into a single data warehouse. 2. Consistent Reporting: Standardize metrics across regions. 3. Improved Analytics: Enable real-time data analysis for better decision-making. Case Study: Mapping a Data Warehouse for FinanceCorp Background FinanceCorp is a financial services company offering a range of products, including banking, investments, and insurance. The company collects data from various systems such as transaction processing systems, customer relationship management (CRM) systems, and external financial data providers. FinanceCorp decided to build a data warehouse to integrate these data sources, enhance data analysis, and support regulatory reporting requirements. Objectives 4. Consolidate Data: Integrate data from multiple sources into a unified data warehouse. 5. Support Regulatory Reporting: Ensure the data warehouse supports comprehensive and accurate regulatory reporting. 6. Enhance Analytics: Provide a robust platform for advanced analytics and business intelligence. 7. Improve Data Quality: Standardize and cleanse data to improve accuracy and reliability. 8. Scalability and Performance: Design for scalability to handle growing data volumes and ensure high performance for querying. Case Study: Designing a DBMS Schema for a University Management System Background A university needed a robust database management system (DBMS) to handle student records, course information, faculty details, and administrative functions efficiently. Objectives 1. Data Organization: Centralize all university data. 2. Consistency and Integrity: Ensure data accuracy and avoid redundancy. 3. Scalability: Accommodate growing data volumes. 4. User Access: Provide secure access to different user roles (students, faculty, admin). Case Study: Metadata Management in a Digital Library System Background A large digital library, DigitalLib, faced challenges in organizing and retrieving vast amounts of digital content, including books, journals, and multimedia. Objectives 1. Improve Searchability: Enhance the precision and recall of search results. 2. Organize Content: Categorize and classify content effectively. 3. Ensure Interoperability: Facilitate data sharing and integration with other libraries and systems. Case Study: Implementing Metadata Management for a Data Warehouse in PharmaCo Background PharmaCo, a pharmaceutical company, collects vast amounts of data from various sources, including research and development (R&D), clinical trials, supply chain, sales, and customer interactions. The company decided to implement a data warehouse to consolidate these data sources. To maximize the efficiency and utility of this data warehouse, PharmaCo recognized the importance of robust metadata management. Objectives 1. Enhance Data Discoverability: Ensure that users can easily find and understand the data they need. 2. Improve Data Quality: Maintain high data quality by standardizing definitions and ensuring data integrity. 3. Facilitate Compliance: Ensure that data usage complies with regulatory requirements such as FDA and GDPR. 4. Support Data Governance: Implement policies and processes for managing data as a valuable asset. 5. Streamline Data Integration: Simplify the integration of new data sources into the data warehouse. Case Study: Implementing Metadata Management for Data Warehouse at HealthTechCo Background HealthTechCo, a leading healthcare technology company, manages vast amounts of data from diverse sources including electronic health records (EHR), patient management systems, billing systems, and various health data sources. To enhance data integration, quality, and utility, HealthTechCo decided to implement a comprehensive metadata management system alongside its data warehouse project. Objectives 1. Improve Data Discoverability: Enable users to easily find and understand data assets. 2. Enhance Data Quality: Standardize data definitions and ensure data integrity. 3. Ensure Compliance: Maintain regulatory compliance with healthcare regulations such as HIPAA. 4. Support Data Governance: Establish policies for consistent data management. 5. Facilitate Data Integration: Simplify the integration of new data sources. Case Study: Implementing Star, Snowflake, and Fact Constellation Schemas in a Retail Analytics System Background RetailCorp, a large retail chain, needed a robust data warehousing solution to improve its sales and inventory analytics. Objectives 1. Centralize Data: Integrate sales, inventory, and customer data. 2. Enhance Reporting: Improve the efficiency and accuracy of reports. 3. Scalability: Support complex analytical queries. Case Study: Implementing OLAP in a Financial Services Company Background FinServCo, a financial services company, struggled with analyzing large volumes of transaction data and generating timely financial reports. They needed a solution to support complex queries and provide quick insights. Objectives 1. Enhanced Data Analysis: Improve the efficiency of data analysis processes. 2. Timely Reporting: Generate financial reports quickly. 3. Complex Queries: Support complex analytical queries across multiple dimensions. Case Study: Customer Segmentation for a Retail Company Introduction A major retail company aims to enhance its marketing strategies by segmenting its customer base. By understanding distinct customer segments, the company can tailor marketing campaigns, improve customer satisfaction, and optimize inventory management. Clustering algorithms provide an effective way to achieve this segmentation. Objectives 1. Identify distinct customer segments based on purchasing behavior. 2. Understand the characteristics of each segment. 3. Develop targeted marketing strategies for each segment. Case Study: Credit Card Fraud Detection Introduction A financial institution aims to enhance its fraud detection system to minimize losses due to credit card fraud. Classification algorithms are employed to distinguish between legitimate and fraudulent transactions, enabling timely intervention. Objectives 1. Develop a model to accurately classify transactions as either fraudulent or legitimate. 2. Reduce false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions not detected). 3. Improve the efficiency of the fraud investigation team. Case Study: Market Basket Analysis in a Retail Chain Introduction A large retail chain aims to optimize its product placement and promotional strategies by understanding the purchasing patterns of its customers. Association rule mining is used to uncover relationships between items that are frequently bought together. Objectives 1. Identify frequently purchased item sets. 2. Understand the association rules between different products. 3. Use insights to improve product placement, cross-selling, and promotional strategies. Case Study: Predictive Maintenance in Manufacturing Introduction A manufacturing company aims to improve its maintenance strategy to reduce downtime and maintenance costs. By leveraging data mining techniques, the company can predict equipment failures and schedule maintenance proactively. Objectives 1. Identify patterns and indicators of potential equipment failures. 2. Develop predictive models to forecast when equipment is likely to fail. 3. Optimize maintenance schedules to prevent unexpected breakdowns.

Decision Schema

Star schema, Snowflake Schema, Fact Constellation Patient(Doctor.Location,Branch,Time)

Association

Apriori and FP growth Problems a) Given the following transactional database Tran ID Items Purchased I1 Strawberry, Litchi, Orange, I2 Strawberry , Butter fruit I3 Butter fruit, Vanilla I4 Strawberry, Litchi, Orange, I5 Banana, Orange I6 Banana I7 Banana, Butter fruit I8 Strawberry, Litchi, Orange, apple I9 Apple, Vanilla I10 Strawberry, Litchi (i) We want to mine all the frequent itemsets in the data using the Apriori & FP growth algorithm. Assume the minimum support level is 30%. (ii) Find all the association rules. The minimum confidence is 70%.

Classification

Consider the following training dataset and the original decision tree induction algorithm (ID3). Risk is the class label attribute. The Height values have been already discretized into disjoint ranges. Calculate the information gain if Gender is chosen as the test attribute. Calculate the information gain if Height is chosen as the test attribute. Draw the final decision tree (without any pruning) for the training dataset. Generate all the “IF-THEN rules from the decision tree. Gender Height Risk F (1.5, 1.6) Low M (1.9, 2.0) High F (1.8, 1.9) Medium F (1.8, 1.9) Medium F (1.6, 1.7) Low M (1.8, 1.9) Medium F (1.5, 1.6) Low M (1.6, 1.7) Low M (2.0, 8) High M (2.0, 8) High F (1.7, 1.8) Medium M (1.9, 2.0) Medium F (1.8, 1.9) Medium F (1.7, 1.8) Medium F (1.7, 1.8) Medium