High Quality Data Quality Analysis

1427 WordsMay 18, 20176 Pages
3.1 Introduction High-quality data facilitate a precise analysis and the resulting statistics. Hence, high-quality data assist the organization to increase its business value. This chapter demonstrates the concept of data quality, the effects of inaccurate data, and the factors that cause low-quality data. 3.2 Data Quality The executive and the top management in organizations seek comprehensive reports and dashboards to enable them to understand on going processes and facilitate the decision- making that improves their business. However, the decision-making process may be influenced by various factors. Data quality is a critical factor because when the quality of the data is inferior, poor decisions could be made [39]. In addition, data…show more content…
Hence, several parties share the responsibility for the quality of data. In addition, J. E. Olson (2003) observed that the poor data quality resulted from the rapid growth of information system technology and the prompt evolution in system implementation and frequent changes that complicate and hamper the quality control process [44]. According to C. Boulton (2016), 57.5% of poor data are caused by users, followed by 47% caused by data migration and integration, which usually lead to gaps or duplicate information, and 43.5% caused by changes to source systems (Figure 5) [45]. Figure 5: Causes of poor data quality [45] Jack E. Olson (2003) defined data quality as data that are linked to their fit for use. In other words, high data quality is obtained when the data fulfill the requirements and the criteria of its intended usage. In contrast, poor quality results when the data do not fulfil their requirements [44]. 3.3 Dimensions of Data Quality In previous research, data quality was divided into two main categories: intrinsic and contextual. In the intrinsic category, value resides within the data, which refers to objective attributes. In the contextual category, the attributes of the data mainly depend on the context in which the data are present, used, as well as the situation or problem. Data in the contextual category include the dimensions of relevance and believability [39],
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