3.1 Defining the optimal data maintenance effort
The first proposition of this paper is shown in Figure 1. The vertical axis indicates the incurred, aggregated costs of dealing with poor quality data. The second and horizontal axis deals with the quality of data. The two curves in the figure represent costs inflicted by poor quality data and the costs of maintaining high data quality, respectively. The costs inflicted by poor quality data are for example faulty decisions based on poor data quality, whether this is of operational or strategic character. The costs of ensuring and maintaining high data quality simply refer to the work of assurance or improving data quality. The total costs associated with data quality are the aggregated cost of the two explained curves. There are two basic assumptions associated with Figure 1. Firstly, during data maintenance the focus is on the most critical data (i.e. the ones with the highest payoff per resources spent) before moving on to less critical ones. This implies that the first work of assuring data quality would have the greatest effect, i.e. the costs inflicted by poor quality data decreases exponentially. The second assumption is that the costs of the efforts to ensure high data quality are not causally related to the their importance, i.e. focusing on a set of poor quality data with great impact on costs is not necessarily cheaper than focusing on data with little impact on costs. Thus, the costs of assuring data quality is a linear relationship between data quality and assurance costs.
3.1 Defining the optimal data maintenance effortThe first proposition of this paper is shown in Figure 1. The vertical axis indicates the incurred, aggregated costs of dealing with poor quality data. The second and horizontal axis deals with the quality of data. The two curves in the figure represent costs inflicted by poor quality data and the costs of maintaining high data quality, respectively. The costs inflicted by poor quality data are for example faulty decisions based on poor data quality, whether this is of operational or strategic character. The costs of ensuring and maintaining high data quality simply refer to the work of assurance or improving data quality. The total costs associated with data quality are the aggregated cost of the two explained curves. There are two basic assumptions associated with Figure 1. Firstly, during data maintenance the focus is on the most critical data (i.e. the ones with the highest payoff per resources spent) before moving on to less critical ones. This implies that the first work of assuring data quality would have the greatest effect, i.e. the costs inflicted by poor quality data decreases exponentially. The second assumption is that the costs of the efforts to ensure high data quality are not causally related to the their importance, i.e. focusing on a set of poor quality data with great impact on costs is not necessarily cheaper than focusing on data with little impact on costs. Thus, the costs of assuring data quality is a linear relationship between data quality and assurance costs.
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