## 2 Background and Related Research
Uncertainty with regards to the meaning behind the data leads to the prevalent challenges of selecting an appropriate algorithm and evaluating its results. If the meaning behind the data is not well understood, the value of data-driven analysis diminishes as the results cannot be adequately interpreted. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Classification**} & \multirow{3}{*}{**Challenge**} & \multicolumn{4}{c}{**General Industrial**} & \multicolumn{3}{c}{**HVAC-Specific**} \\ \cline{3-8}  & & **[PERSON] and** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON] and** & **[PERSON] et al.** \\  & & **[PERSON]** [22] & **et al.** [23] & **et al.** [24] & **et al.** [25] & **[PERSON]** & **[PERSON] et al.** \\ \hline Broken & Data Availability & X & X & X & X & X & \\ \hline Bad Quality & Noisy Data & X & & & & & X \\ \hline \multirow{3}{*}{Background} & Algorithm Selection & X & X & X & X & & \\  & Evaluation of Results & X & X & X & X & & \\ \cline{1-1}  & Distinguishing between & & & & & & \\ \cline{1-1}  & sensor and component faults & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification and comparison of the issues identified in systematic reviews in the area of advanced data analysis in the industrial domain. The \"3B\" data issue taxonomy provides a classification for the plethora of data-related challenges in the industrial setting that extend beyond the challenges outlined in Table 1. We interpret the nature of the data issues as in Figure 2, such that data challenges may be classified in a number of regions in the Venn diagram. For example, the challenge of distinguishing sensor faults from component faults is a key challenge in HVAC applications [18], which may be caused by faulty sensors (Bad quality) or the physical failure of mechanical components (Background). Therefore, rather than prescritively resolving a myriad of specific data challenges, a means of managing the challenges that lie within the Venn diagram is a potentially more sustainable solution. The inadequacy of real-world industrial data appears to be a main barrier to the implementation of data-driven predictive maintenance systems; therefore, managing the 3B's is a requirement for successful digital transition. In summary, we are of the opinion that energy reduction-related decision making in the HVAC domain would be aided if knowledge-based and data-driven methods were integrated. There are two main research gaps that are impeding the realization of the benefits of an integrated approach. Firstly, the industry is stuck in reactive mode regarding its maintenance strategy and there is a lack of direction in terms of the transition to proactive methods. Secondly, the pervasive nature of real-world data issues composes a significant barrier to practical application of data-driven approaches and there is a lack of a means to manage the \"3B\" data issues in the HVAC domain. The evolution of data-driven analysis is reviewed in the following section, with particular attention given to the value data-driven analysis provides in the industrial setting and the gaps that need to be filled to ensure this value is realized. Figure 1: Illustrations of (**a**) drift, (**b**) bias, (**c**) precision degradation, (**d**) spike, and (**e**) stuck faults. Reprinted from [PERSON] et al. [28], with permission from Elsevier, 2020.