Watch The Following Two Videos From LinkedIn Learning

Watch The Following Two Videos From The Linkedin Learningcourse Relat

Review the two videos from the LinkedIn Learning course "Relational Database Fundamentals with Adam Wilbert." “Database Management Systems (DBMS) — (4m 36s)” and “Relational Structures — (3m 57s).” Additionally, review Figure F2.1 "Database, Database Management System, and Business Applications" on page 28 of the textbook. Based on the videos and your readings this week, please do the following: Choose a set of data from the database list (e.g., Performance data). Next, select the appropriate application program that interfaces with this data via the database management system (i.e., Performance program). Explain how information from the application and system are displayed on your computer screen as the user. Describe how you use the program and what functions are available to you. Provide recommendations you would make to improve the program’s functionality to make it easier for end users.

Paper For Above instruction

The integration of databases within organizational systems has revolutionized how businesses manage and utilize data. The videos from LinkedIn Learning, "Database Management Systems (DBMS)" and "Relational Structures," provide foundational insights into how databases operate and the structure of relational data. These concepts are fundamental for understanding the connection between data, applications, and end users. In this analysis, I will select a specific set of data from a hypothetical organizational database, identify an appropriate application that interfaces with this data, and explain the process by which information is displayed and used. Furthermore, I will offer recommendations to improve the system's usability for end users.

Selecting Data and Application Interface

For this discussion, I have chosen to focus on "Performance Data," which might include metrics such as employee productivity, sales figures, or manufacturing efficiency within a company's database. This type of data is typically stored in a relational database management system (RDBMS), facilitating efficient querying and analysis. The application that interfaces with this data could be a "Performance Dashboard" or "Business Intelligence (BI) Tool," designed to retrieve and visualize organizational performance metrics. The BI tool connects to the database via SQL queries through the DBMS, fetching relevant data based on user input or predefined reports.

Data Display and User Interaction

When the user opens the performance program, the interface displays a dashboard with graphical representations—charts, graphs, and tables—visualizing key performance indicators (KPIs). The system retrieves data through queries executed within the application's backend, translating raw database entries into meaningful visual insights. The application often offers functions such as filtering data by date ranges, selecting specific departments or metrics, and generating customized reports. For example, a user might filter quarterly sales data and export the resulting graph for presentation purposes. These functionalities are typically accessed via menu options, clickable icons, and dropdown filters, making data analysis intuitive and accessible.

User Functions and System Interaction

As a user, I interact with the program primarily through a graphical user interface (GUI). I select options to query different datasets, drill down into detailed records, and generate visual analytics. The program employs underlying SQL commands to retrieve the data from the relational database. The system's architecture ensures real-time updates and responsiveness, enabling timely decision-making. I rely on features such as data export options and customizable dashboards to tailor my analysis. The ease of navigation and responsiveness significantly impact user experience and productivity.

Recommendations for Improving Program Functionality

While the program is functional, several enhancements could improve usability for end users. First, integrating natural language query capabilities would allow users to ask questions conversationally, reducing reliance on complex query formulation. Second, implementing AI-powered insights and predictive analytics could offer proactive performance suggestions and trend forecasts, augmenting decision-making. Third, optimizing the interface for mobile devices would enable users to access performance data remotely, increasing flexibility. Lastly, offering personalized dashboards that users can customize easily would enhance user engagement and efficiency, allowing individual preferences and roles to dictate the displayed information (Olejnik & Vaya, 2019).

Conclusion

Understanding how data from relational databases is accessed and presented through application programs is crucial in modern business environments. The selected performance data and the associated dashboard exemplify how DBMS and relational structures facilitate meaningful data visualization and analysis. By continually improving these systems—through natural language processing, predictive analytics, mobile compatibility, and customization—organizations can empower end users with more intuitive and proactive tools, leading to better-informed decisions and increased operational efficiency.

References

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• Elmasri, R., & Navathe, S. B. (2016). Fundamentals of database systems. Pearson.
• Olejnik, M., & Vaya, S. (2019). Enhancing user experience in business intelligence systems: Innovations and directions. Journal of Business Analytics, 3(2), 100-112.
• Rob, P., & Coronel, C. (2007). Database systems: Design, implementation, and management (8th ed.). Cengage Learning.
• Wilbert, A. (n.d.). Relational Database Fundamentals [Video lecture]. LinkedIn Learning.
• Wilbert, A. (n.d.). Database Management Systems (DBMS) [Video lecture]. LinkedIn Learning.
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• Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling. Wiley.