## 5 Discussion  
A novel framework is proposed to integrate traditional engineering or knowledge-based approaches for visible problem solving, with techniques that originate from the field of data analysis for invisible problem solving, for the purpose of advancing industrial data analysis and reducing energy consumption. The Industrial Data Analysis Improvement Cycle (IDAIC), Figure 8 is a framework that simultaneously aims to provide short term value by reactively analyzing data using knowledge-based approaches, while also facilitating the transition to proactive analysis by accessing data quality and monitoring its improvement over time. The conventional data analysis approach must be supplemented with strong domain knowhow and therefore the CRISP-DM methodology is extended through the novel integration of data assessment techniques, expert systems and exploratory data analysis. The IDAIC framework begins with understanding-centric phases, whereby the domain and the data are assessed to determine the value that the problem-solving activities may realize. Data assessment is then performed to determine the quality of the dataset or the fitness of the dataset for AI & ML application. Data points that are deemed to be of concern are marked for further analysis in the operation assessment phase, with appropriate pre-processing activities performed in advance. The operation of the asset is assessed using knowledge-based FDD techniques, such as expert rules, to identify visible problems in the system. While problems that are identified may be caused by those bad quality data points marked from the data assessment phase, it may incentivize industrial practitioners to recalibrate or replace faulty sensors when highly interpretable expert rules cannot be applied. While the IDAIC framework aims to enable industry to perform advanced analysis, the overall goal is energy consumption reduction; therefore, the analysis must inform the physical changes that are made to the system. Commissioning may be defined as \"a quality-oriented process for achieving, verifying and documenting whether the performance of a building's systems and assemblies meet defined objectives and criteria\" [58]. While sensor faults are resolved in commissioning activities, the data quality demands of AI and ML solutions require vigorous data assessment and maintenance of the sensor. Therefore, the commissioning phase in the IDAIC framework not only performs the necessary maintenance activities to achieve energy efficient operation, but also aims at performing the necessary maintenance activities to collect the highest quality of data possible. At this point in the IDAIC framework, domain knowledge has been sufficiently integrated to solve visible problems and the asset or system is operating in a faultless, energy efficient state with good quality data continuously being collected. Industry may then switch from a reactive maintenance approach to a proactive maintenance approach with data-driven techniques. However, while domain expertise informed the inner goal-oriented circle, the outer circle is data-oriented and exploratory in nature. Similar to the DST map in Figure 3b proposed by [PERSON] et al. [31], the invisible problem solving and avoidance begins with goal, or domain, exploration. Strong collaboration is required between data analysts and domain experts in this phase to elicit potential areas of interest to apply data-driven algorithms. For example, the cause of a recurring fault or failure may be unknown and an unsupervised ML algorithm may provide insight through pattern recognition or clustering techniques. Similarly, ML may be used to anticipate a particular fault that is deemed critical by the domain expert, providing an estimate of the remaining useful life in a predictive maintenance strategy. Once a goal has been selected, the data exploration takes place to uncover if all relevant information is available and