Abstract:

The strategic alignment with the primary commercial purposes of data warehouse (DW) systems are a continuous challenge of data management. Conventional requirement engineering (RE) approaches do not tend to achieve success in translating high-level business requirements into unambiguous and implementable specifications needed to produce the best DW design, creating such systems that are functional but strategically uncomfortable and strategically inadmissible. The current paper eliminates this major distinction by the introduction of a new target-driven requirement engineering (TDRE) structure. The structure offers a methodical role to extract, break up and execute purposeful business aims and significant performance measures (KPI) in verification DW specifications. This encompasses the systematic breakdown model, semi-automatic natural language processing (NLP) need analysis, and a modified failure mode and impact analysis (FMA) rooted on the risk-inconstant preference determination mechanism. At the core of the structure are the suggested target-run requirement decomposition and adequacy (TDRD-T) algorithms, which articulately render the process of transforming business goals into a tragic requirement list. The efficiency of this framework is presented in the form of a comparative case study in the organizational setting of the real world. The findings imply that the TDRE framework reduces the need to 71.9% and covers a greater part of trackability by 98%. Moreover, the DW created with the help of this framework outperformed and achieved the key objectives of query response time and data innovation; it was rated 40 percent higher by users. Both the theoretical input to the DW design approach and the practical, tested instrument that is offered in this research enables practitioners to ensure that DW investments achieve their desired business value.

Key words: data warehouse design, requirements engineering, target-driven development, business-IT alignment, traceability matrix