The completeness of the available data set is low, since the combinations of the variables are limited to a narrow range and the feature space is not fully utilized. Consequently, the data quality is low. Besides the issue of an incomplete data set, the available data reveals an imbalanced distribution. Some areas of the feature space contain many observations, while other areas only contain few observations (cf. density in Figure 1b). This unbalance raises problems in the training of data-driven models. The imbalanced distribution of the data may bias the training to more frequently occurring observations [7]. Several works have shown the effects of incomplete or unbalanced data on data-driven applications, e.g., [PERSON] and [PERSON] 2011 [8] reveal a decreased classification result with decreasing completeness and [PERSON] 2006 [9] shows the impact of inaccurate and incomplete data on query results. In the specific example of condition monitoring of control valve (cf. Figure 1), a data-driven prediction model for the valve's flow cannot achieve accurate results for a stroke of, e.g., 75% and a pressure of 20 bar. Inaccurate results of the data-driven model may cause wrong decision making though. Inaccurate results may raise a maintenance alarm initiating maintenance actions unnecessarily. For this reason, it is necessary to observe and examine in detail the completeness and balance of the training data set. In particular, in multi-dimensional data sets, a simple evaluation based on histograms or scatter plots is impossible. A metric is required to assess data quality and a visualization is demanded [10], allowing for an evaluation by experts of the aPS. This expert evaluation is inevitable since incomplete and unbalanced data sets do not necessarily impair the data's utility. Particular combinations of variable values might not be feasible physically, so that missing data in this area of the feature space does not affect the quality of the data-driven prediction model. The pressure difference before and after the valve, for example, cannot be higher than the absolute pressure before the valve. The feature space will be empty for \(\Delta p>p\). However, the quality of the data is not impaired. The paper's main contribution consists of three aspects: First, a data quality attribute describing the completeness and balance of the feature space in multi-dimensional data sets, namely the data set completeness, is defined. This includes the discussion of the different interpretations and measures of completeness in literature. Second, a metric is introduced measuring the data set completeness. Since this data quality dimension is dependent on the use case and the experts' evaluation, a visualization is further proposed supporting experts in assessing the quality of their data. As a running example and for evaluation, the introduced condition monitoring use case is considered. However, the metric and visualization are applicable to every set of independent, numeric variables, which are provided as input variables for regression models. The remainder of the paper is as follows: In Section 2, the background of data quality and condition-based maintenance is given. In Section 3, related work in the field of data quality, in particular for completeness, is discussed. Exploring the attributes of completeness in the literature reveals the gap for a metric for data set completeness. In order to fill this gap, a metric and visualization are introduced in Section 4. The evaluation  
Figure 1: Data of a control valve in a timeframe of 3 weeks of normal operation in an industrial process plant (**a**) histogram of stroke and (**b**) scatter plot of stroke and pressure. is carried out based on a data set for condition monitoring of control valves. This data set is introduced in Section 5, including descriptive statistics and a random forest regression model for condition monitoring. Manipulations of this data set are used to reveal the significance of data set completeness and its impact on model accuracy.