# Value Creation with Digital Twins: Application-Oriented Conceptual Framework and Case Study
## 4 Application-Oriented Digital Twin Framework
### From Data to Value  
This chapter describes how value is created from data with the help of DTs. First, the different categories of data will be explained, then data preparation, analysis, and interpretation will be examined, and finally, it will be described how the resulting services create value. Figure 5: Things as real-world counterpart data sources of digital twins. **Data categories**--Product, customer, and context data serve as the key to value creation, while practical data analytics tools enable its success [70]. DTs represent a product [11], and its processes [71] and therefore consist of datasets describing the state and behavior of the real-world-counterpart of the DT. [PERSON] and [PERSON] [11] elaborate on what data can be contained in a DT. Depending on the use case, they state a complete 3D model of the physical instance and its components, a bill of materials that lists current and replaced components, and a bill of processes that lists operations and processes that can be included. In addition, the results of any measurements and tests, a service record, and all available sensor data from the things should be added to the DT. In our framework, all these data are included under the term \"product data\". Another frequently described category that serves as a foundation for knowledge-based decisions is contextual data (e.g., [15; 20; 23; 72; 73; 74]), such as environment data (e.g., temperature, humidity) and any other contextual data relevant for the intended value creation (e.g., exchange rates, weather data, market data) which is not directly related to the state or behavior of the product or asset itself. Academia and practice show that the value of digitized products for the user of a DT increases with access to data from the surrounding ecosystem [3; 4]. The third category that is distinguished in our framework is customer-related data, such as data about the actual owner, configuration and usage preferences, and other relevant customer relationship management data [75]. This customer-related data is gathered before the purchase, as the customer experience life cycle already provides valuable data during the earlier phases. By combining all the data obtained, it is possible to potentially unveil completely new insights that rend enormous business opportunities, as these comprehensive DTs illustrate the current lifestyle of users [43] and ultimately unexploited or future customer needs and demands. Hence, service-oriented DTs can be the source of several new knowledge-driven business opportunities [76]. **Data analysis**--To create value, the data must be structured and interpreted to achieve DT-based decision- and sense-making. According to the data life cycle for DTs defined by [PERSON] et al. [19], eight different steps can be distinguished: Data collection, data transmission,  
Figure 6: Internal and external systems as complementary data sources for digital twins. data storage, data integration, data processing, data cleaning, data analysis, and data mining. After collecting, transmitting, and storing the data, the integration, processing, and cleansing of data are the crucial steps to achieve compatible, structured data formats for the analysis and interpretation phases. In the reference framework, this process is simplified to the three main categories of unstructured, structured, and interpreted data (following [8; 20; 44; 64]). The data processing is referred to simply as \"Smart Analysis\", which is carried out utilizing tools, algorithms, rules, and models (see Figure 7). Unstructured data is raw data, often obtained from the sensors used to measure the real-world counterpart of the DT. Semi-structured data are either preprocessed unstructured data, for example, the transcripts of recordings, or have origin in external systems using different structuring standards, for example, customer reviews, comments, and complaints in the web or equipment maintenance orders.