## 3 The SemDaServ Approach
### Artifacts
#### 3.3.3 Data Decision Matrix  
The data decision matrix supports and documents the decision as to which data from which sources should ultimately be consumed by the smart service. To do so, the system takes technical (data quality) and economic (cost-benefit) aspects of the data collection requirements into account. Filling out the data decision matrix requires an upstream assessment of the gap between the defined data needs and the quality of existing data, as well as an estimation of what costs will be incurred to sufficiently cover the data needs. For the smart service development project to be successful, the resulting smart service must be profitable. For this reason, the data decision matrix includes information about the cost-benefit ratio of data collection. Methodologically, this is done through the cost-benefit analysis presented in Figure 9. The goal is to maximize the cost-benefit ratio. The economic benefit of the smart service is usually already determined and thus known before the start of the data specification process in the context of business model development and analysis through appropriate SSS requirements. The costs of developing and operating the smart service result from data requirements, IT infrastructure, the required quality of the AI models, and data quality. Data requirements result in costs for adapting or redevelopping physical product components for product development, manufacturing and operation. Furthermore, the IT infrastructure must be adapted due to additional data streams: Again, this includes development, initial deployment, and operating costs. The data requirements of the smart service can be partly covered by synthetic data from simulation models. This reduces costs in the area of physical product components but generates modeling costs if the existing simulation models have to be adapted, created, or expanded. Once the technical and economic assessment of the data requirements is available, the data decision matrix shown in Table 3 can be filled in by the actors involved in the decision-making process based on the results of earlier steps in the data specification process. To do this, the variables specified in the data model are entered in the first column. This is followed by the decision of the Project Sponsor whether the variable located in the respective row should be measured in real terms or generated synthetically by simulation models. Data requirements and the existing data quality are entered in the following columns. This is done by first assessing the relevance of the information content (\(RI\)) on a scale from 0 (information content of the variable is irrelevant) to 6 (the smart service cannot be developed according to the requirements without this variable). The Data Quality Score (\(DQS\)) is determined by assessing the existing data quality using the data quality dimensions based on [34,44] and shown in Figure 10. To do this, each major criterion is qualitatively estimated on a scale from 0 to 2. The meaning of the scale is defined as follows:  
1. Existing data probably satisfy the data needs of this data quality factor. 2. Existing data satisfy the data needs of this data quality factor. The rating for the main criterion of content data quality is the arithmetic average of the four sub-criteria but can be determined directly for efficiency reasons. The data quality value results from the sum of the ratings of the three main criteria and is thus at most six (existing data quality fulfills the data needs in all factors) and, at least zero (existing data quality fulfills the data needs in no factors). Thus, the Relevance to Quality Score (\(RQS\)) can be calculated according to Formula (1):  
\[RQS=\frac{RI}{1+\Delta DQS}=\frac{RI}{1+6-DQS}=\frac{RI}{7-DQS}. \tag{1}\]  
The \(RQS\) is thus a means of focusing on the most relevant variables, which are already present in the highest quality.