Case Studyread The Following Articles And Incorporate Them Case Studyread The Following Articles And Incorporate Them Read the following articles and incorporate them into your paper. You are also encouraged to review additional articles as well. • Chen, J.-K., & Lee, W.-Z. (2019). An introduction of NoSQL databases based on their categories and application industries. Algorithms, 12(5), 106. Retrieved from • Georgiou, S., Rizou, S., & Spinellis, D. (2019). Software development lifecycle for energy efficiency: Techniques and tools. ACM Computing Surveys, 52(4), 1–33. Retrieved from • Mokokwe, L., Maabane, G., Zambo, D., Ralefala, T., Shulman, L., Ramagola-Masire, D., Tapela, N., Grover, S., & Ho-Foster, A. (2018). First things first: Adopting a holistic, needs-driven approach to improving the quality of routinely collected data. Journal of Global Oncology, 155. Retrieved from • Shichkina, Y. (2019). Approaches to speed up data processing in relational databases. Procedia Computer Science, 150, 131.
Paper For Above Instructions This paper explores strategies to enhance data quality, optimize database performance, and ensure secure, efficient multiuser operations by leveraging insights from recent scholarly articles and best practices within the Software Development Life Cycle (SDLC). The integration of these approaches aims to present a comprehensive framework for managing large datasets effectively while mitigating operational risks. Improving Dataset Quality Through SDLC Phases Enhancement of data quality is a critical aspect of database management. Three specific tasks, aligned with the SDLC phases—planning, development, and deployment—are recommended to systematically improve datasets. 1. Data Profiling During Planning In the planning phase, data profiling should be conducted to analyze data sources, identify anomalies, and understand data completeness and consistency. This task involves assessing data for accuracy, redundancy, and integrity, utilizing tools such as data quality dashboards and profiling software. By establishing initial data quality metrics, organizations can set realistic improvement goals and allocate resources effectively.