## 4 IDAIC Framework
However, data in the industrial domain poses some extra difficulties, as mapped in Figure 2, which require more guidance for practical implementation. The trend that emerges is that while data science methodologies as such CRISP-DM provide sufficient structure to perform data analysis, they do not provide sufficient domain understanding to tackle visible problems in the industrial setting. The main reason CRISP-DM needs to be adapted is to address this lack of domain understanding, by integrating domain specific methodologies, such as those developed in the area of FDD. Secondly, the lack of necessary data and insufficient data quality is limiting the value of data-driven analysis in the real-world industrial setting. Hence, the training data for ML application is not sufficient in terms of quantity or quality to realize the benefits of data-driven approaches such as predictive analysis. Therefore, more emphasis on these issues is required to obtain an AI/ML ready dataset than \"as is\" CRISP-DM implementation provides. In Figure 7, the CRISP-DM phases in Figure 3a and the activities to transition from visible to invisible problem solving in Table 2 are gathered. Based on the comparative study of popular data mining processes (KDD, SEMMA and CRISP-DM) by [PERSON] and [PERSON] [53], a description of each CRISP-DM phase is also included in the gathering section. The data understanding phase is therefore repeated to describe the activity of acquiring the relevant data (2) and the activity of searching for unanticipated trends and anomalies (3). In the following section, similar activities are grouped. For example, the activities of determining data quality (C), determining the value of additional data (B) and determining the value of better quality data (D) may all be achieved in a data assessment phase. While the data understanding phase of CRISP-DM contains a \"verify data quality\" task [54], it does not provide sufficient detail to achieve tasks B, C and D. The data understanding phase (2+3) is therefore broken into two phases: data contextualization (2+A) and data assessment (3+B+C+D). Data contextualization integrates the activity of acquiring the data (2) and determining if the dataset is sufficient (A). Lastly, in alignment with the DST map proposed by [PERSON] et al. [31], the phases are clustered to achieve the goals of visible (goal-oriented) and invisible (data-oriented) problem solving. Figure 7: Framework Development. The framework is illustrated in Figure 8, retaining a similar structure to that of CRISP-DM and the DST map. The framework is named the Industrial Data Analysis Improvement Cycle (IDAIC), as the purpose of the framework is to improve the analysis of industrial data so that industry may transition from reactive to proactive decision making. The main contributions made to CRISP-DM is summarized as follows:  
* Extension of Data Understanding phase to Data Contextualization and Data Assessment. These phases aim to provide greater guidance on interpreting the meaning and sufficiency of the dataset and measuring the value of the data, respectively. * Addition of an Operation Assessment phase to integrate domain knowledge for the identification of known faults that would assess the health status of the asset. * Addition of a Domain Exploration phase to elicit the tacit knowledge of experts for the guided exploration of areas of improvement through invisible problem solving. * Addition of a Data Exploration phase to determine if the invisible problem-solving aspirations may be realized with the available dataset. * Addition of a commissioning phase to bridge the gap between domain knowledge-reliant goal-based visible problem solving and data-reliant exploratory-based invisible problem solving. The commissioning phase also incorporates the task of implementing the analysis for value creation that has previously been satisfied by the Deployment phase in CRISP-DM. The Business Understanding, Modelling and Evaluation phases are renamed to Domain Understanding, Algorithm Selection and Result Exploration.