Assignment 3: Business Intelligence And Data Warehous 245932

Assignment 3 Business Intelligence And Data Warehousesbusinesses Toda

Write a two to three (2-3) page paper in which you: 1. Outline the main differences between the structure of a relational database optimized for online transactions versus a data warehouse optimized for processing and summarizing large amounts of data. 2. Outline the main differences between database requirements for operational data and for decision support data. 3. Describe three (3) examples in which databases could be used to support decision making in a large organizational environment. 4. Describe three (3) examples in which data warehouses and data mining could be used to support data processing and trend analysis in a large organizational environment. 5. Use at least three (3) quality resources in this assignment.

Paper For Above instruction

In the contemporary business landscape, organizations rely heavily on diverse database systems to facilitate efficient operations and strategic decision-making. Two primary types of databases—relational databases optimized for online transaction processing (OLTP) and data warehouses designed for online analytical processing (OLAP)—serve different organizational needs by differing substantially in structure and purpose.

Differences Between Relational Databases and Data Warehouses

Relational databases, primarily used for OLTP, are structured to support daily transactional activities such as order processing, inventory updates, and customer management. They are optimized for fast, reliable transaction processing, with a normalized data structure that minimizes redundancy and ensures data integrity. These databases typically feature intricate relationships between tables, facilitating quick data retrieval and updates necessary for operational efficiency (Stonebraker & Çetintemel, 2005).

In contrast, data warehouses are designed for OLAP and are optimized for large-scale data analysis, reporting, and trend identification. They employ a denormalized, often dimensional schema (e.g., star or snowflake schemas), which consolidates data sources to enable complex queries and aggregation across vast datasets. This structure enhances query performance for analytical purposes but sacrifices some level of data normalization and real-time transaction support (Kimball & Ross, 2013).

Database Requirements for Operational Data versus Decision Support Data

Operational databases require high availability, fast transaction processing, and real-time data updates. They are designed to handle many concurrent users performing insert, update, and delete operations without significant delays, ensuring the smooth running of daily business activities. Data integrity, consistency, and recovery mechanisms are crucial in operational databases (Coronel & Morris, 2015).

Decision support databases, on the other hand, prioritize query efficiency over transaction speed. They support complex, long-running queries, data aggregation, and historical data analysis. These databases are optimized for read-heavy operations, often updating data periodically through ETL (Extract, Transform, Load) processes rather than continuous real-time updates, thus enabling informed strategic decisions (Inmon, 2005).

Examples of Database Support for Decision Making

Firstly, customer relationship management (CRM) systems use operational databases to store customer interactions, which then inform targeted marketing campaigns. Secondly, enterprise resource planning (ERP) systems utilize transactional data to monitor supply chain efficiency, aiding decision-makers in inventory management. Thirdly, financial reporting applications aggregate transactional data to analyze profit margins and financial health, guiding strategic planning (Davenport, 2013).

Examples of Data Warehouses and Data Mining for Trend Analysis

One example includes retail companies analyzing sales data across multiple stores to identify buying patterns and seasonal trends, thereby optimizing stock levels. Another instance is healthcare organizations examining patient data to discover correlations between treatments and outcomes, supporting clinical decisions. A third example involves financial institutions analyzing transaction data to detect fraudulent activities and predict future risks through predictive modeling (Laney, 2001; Han, Kamber & Pei, 2012; Power, 2002).

Data warehouses facilitate such comprehensive analysis by consolidating disparate data sources, while data mining techniques like clustering, association rule learning, and classification extract actionable insights from large datasets (Fayyad, 1996). These practices help organizations in long-term strategic planning, risk management, and operational improvements.

Conclusion

Understanding the structural and functional differences between operational databases and data warehouses is vital for leveraging data effectively in organizations. Operational systems support day-to-day transactions, emphasizing speed and data integrity, whereas data warehouses enable complex analytical processes that unlock strategic insights through data mining. Combining these systems enhances decision-making capabilities, allowing organizations to remain competitive in a data-driven world.

References

• Coronel, C., & Morris, S. (2015). Database Systems: Design, Implementation, & Management (11th ed.). Cengage Learning.
• Davenport, T. H. (2013). Analytics at Work: Smarter Decisions, Better Results. Harvard Business Review Press.
• Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. In AI magazine, 17(3), 37-54.
• Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
• Inmon, W. H. (2005). Building the Data Warehouse (4th ed.). Wiley.
• Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.
• Laney, D. (2001). Data Warehouse Trends. Data Management Review, 13(2), 24-28.
• Power, D. J. (2002). Decision Support, Analytics, and Business Intelligence. Communications of the ACM, 45(11), 56-60.
• Stonebraker, M., & Çetintemel, U. (2005). "One Size Does Not Fit All": clients, servers, and database architecture. In Proceedings of the 21st International Conference on Data Engineering (ICDE), 2-11.