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Businesses Today Are Extremely Reliant On Large Amounts Of Data For Ma

Businesses today are extremely reliant on large amounts of data for making intelligent business decisions. Likewise, the data warehouses are often structured in a manner that optimizes processing large amounts of data. 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 large organizational environment. 5. Use at least three (3) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources. Your assignment must follow these formatting requirements: · Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.

Paper For Above instruction

Introduction

In today's data-driven business environment, organizations heavily depend on various types of databases to facilitate efficient operations and strategic decision-making. Understanding the structural differences between databases optimized for transactional processing and those designed for analytical processing is crucial for implementing effective information systems. This paper explores the contrasting structures of relational databases for online transactions and data warehouses, the differing requirements for operational versus decision support data, and exemplifies how databases and data warehouses are instrumental in supporting organizational decision-making and trend analysis.

Differences Between Relational Databases for Transactions and Data Warehouses

Relational databases designed for online transaction processing (OLTP) prioritize speed, efficiency, and reliability for day-to-day operations. They are structured to handle a large volume of simple, short transactions simultaneously. These databases employ normalization techniques to reduce data redundancy and ensure data integrity. The schema is optimized for insert, update, and delete operations, facilitating quick access to individual records (Silberschatz, Korth, & Sudarshan, 2019). Typically, they feature a highly normalized structure with multiple tables linked through primary and foreign keys, enabling quick transactional queries.

In contrast, data warehouses are optimized for analytical processing and reporting, often implemented within online analytical processing (OLAP) systems. Their primary goal is to process and summarize large volumes of historical data efficiently. To facilitate complex queries and aggregations, data warehouses employ denormalization—storing redundant data to reduce the number of joins required during queries. The schema design often follows a star or snowflake model, emphasizing query speed over data redundancy minimization (Inmon, 2005). Data warehouses are structured to support read-intensive operations, such as data mining and trend analysis, which require comprehensive access to consolidated data.

Operational Data vs. Decision Support Data

Operational data pertains to the routine transactions and day-to-day business activities. It requires high availability, fast processing times, and integrity to support functions such as order processing, inventory management, and payroll. The database requirements focus on real-time data capture, accuracy, and transactional consistency (Coronel & Morris, 2019). The tables are normalized to prevent data redundancy and ensure data integrity during frequent updates.

Decision support data, on the other hand, is historical or summarized data used for strategic analysis. It supports functions like sales forecasting, market analysis, and performance monitoring. Data support systems for decision-making require large data volumes that are often historical and aggregated, optimized for complex read operations rather than fast transaction processing. Denormalization, indexing, and partitioning techniques are used to enhance query performance (Kimball & Ross, 2013). The data is often stored separately from operational databases to prevent performance degradation of routine processes.

Examples of Database Support for Decision Making in Large Organizations

1. Customer Relationship Management (CRM) Systems: These databases store detailed customer information, interactions, and purchase histories. Analyzing this data helps organizations tailor marketing strategies, improve customer service, and identify high-value customers, supporting strategic decision-making (Buttle & Maklan, 2019).

2. Supply Chain Management Systems: These databases track inventory, orders, suppliers, and logistics data. Analyzing this information enables organizations to optimize inventory levels, forecast demand, and enhance supplier relationships, ultimately reducing costs and improving efficiency.

3. Financial Databases: These store transaction records, budgets, and financial reports. Financial managers analyze this data to make investment decisions, budget allocations, and financial planning, supporting the organization's long-term strategic goals.

Examples of Data Warehouses and Data Mining Supporting Trend Analysis

1. Sales Data Warehouses: Large retail organizations utilize data warehouses to aggregate sales data across stores and regions. Data mining techniques extract trends such as seasonal purchasing behaviors, product popularity, and customer preferences, enabling targeted marketing campaigns.

2. Customer Behavior Analysis: Data warehouses collect customer interaction and transaction data, which can be mined to identify emerging trends in customer preferences, preferences shifts, and churn prediction, aiding in proactive marketing strategies.

3. Fraud Detection Systems: Financial organizations use data warehouses combined with data mining algorithms to detect anomalies indicative of fraud. Pattern recognition and anomaly detection techniques reveal suspicious activities based on historical transaction data, supporting risk management.

Conclusion

The structural differences between relational databases for transactional processing and data warehouses for analytical purposes are fundamental to supporting the varied needs of modern businesses. Operational databases ensure transaction integrity and speed, whereas data warehouses facilitate comprehensive data analysis and trend identification. Both types of systems are integral in enabling organizations to make informed decisions, adapt to market changes, and maintain competitive advantage. As data continues to grow in volume and complexity, leveraging the right database structures, along with advanced data mining techniques, becomes essential for strategic success.

References

Buttle, F., & Maklan, S. (2019). Customer Relationship Management: Concepts and Technologies. Routledge.

Coronel, C., & Morris, S. (2019). Database systems: Design, implementation, & management (12th ed.). Cengage Learning.

Inmon, W. H. (2005). Building the Data Warehouse (4th ed.). John Wiley & Sons.

Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.

Silberschatz, A., Korth, H. F., & Sudarshan, S. (2019). Database System Concepts (7th ed.). McGraw-Hill Education.