Master Data Quality Management Framework: Content Validity

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Azira Ibrahim
Ibrahim Mohamed
Nurhizam Safie Mohd Satar
Mohammad Kamrul Hasan


Organizations rely on high quality master data as a critical component in achieving their operational and strategic performance. To accomplish high quality master data, they need to be managed properly through a systematic and holistic framework. However, prevalent master data quality management frameworks lack in providing comprehensive management practice in assuring the quality of master data. Hence, stimulates the need to develop an improved master data quality framework. Prior to the development of the framework, the identification and validation of factors that contribute to the management of master data quality must be performed. Thus, this paper underlined four elements and seven factors affecting master data quality management. Further, the identified factors were validated using a questionnaire as the validation instrument. The questionnaire consists of 95 items representing the identified seven factors that were derived from previous studies in the domain of total quality management, data quality management, and master data. Since the items are derived from the different contexts of study, content validation is a need. Previous research has suggested several techniques for performing content validation, covering both quantitative and qualitative approaches. The quantitative approach employed objective assessment and the result was statistically analysed. While the qualitative approach adopted subjective assessment such as comments, ideas, or respond. In this paper, the quantitative approach is selected over the qualitative approach, considering the effort in analyzing several items (95 items) is less complex compared to the qualitative approach which is more difficult to interpret and account for biased results. The selected panel of experts validate the instrument using a three-point scale namely “1 = not relevant”, “2 = important (but not essential)”, and “3 = essential”. Later, using the technique proposed by Lawshe, the value of the content validity ratio (CVR) is calculated. As a result, 92 items are accepted, and 3 items are rejected. The elimination of the 3 items is due to the unsuitableness to be used in the context of the public sector. The validated items can be used as an instrument to validate the factors affecting master data quality management. The proposed factor would support the organization in managing master data quality more effectively.


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Special Issue - Soft Computing & Artificial Intelligence for wire/wireless Human-Machine Interface Systems