## 4 Discussion  
This paper reviewed existing research into frameworks or approaches for big-data analytics as applied to three levels of projects, with increasing degrees of precision or detail. The first level reviewed was a high-level look at frameworks for general industrial applications. The second level focused specifically and local, lower-level smart manufacturing applications of fault detection and fault prediction. Finally, the third and most specialized level looked at approaches specifically oriented towards data reduction. In each section, articles were discussed individually as they pertain to the motivation for this research and their applicability to the CE. At the end of each section, discussion followed to summarize any observations across the set of articles within the section and relate them to each other. The final level of discussion is to look at the full picture and identify any observations, trends, or commonalities that span the three levels. The first observation is that there is a dichotomy in how the same verbiage can be applied to different contexts. Terms like 'framework', 'big data', and 'predictive analytics' in some cases are contextualized as architecture required to build organizational competencies and in other cases as approaches or methodologies to build individual competencies. \begin{table}
\begin{tabular}{l l} \hline \multicolumn{1}{c}{**Author(s)**} & \multicolumn{1}{c}{**Focus**} \\ \hline [PERSON], et al. [87] & High level/Institutional framework \\ \hline [PERSON] et al. [90] & Feature selection meta-heuristic (simulated annealing) \\ \hline [PERSON], [PERSON], and [PERSON] [93] & Sample reduction \\ \hline [PERSON] and [PERSON] [89] & Shape preservation with data reduction for 3D surface points \\ \hline [PERSON], [PERSON], and [PERSON] [94] & Feature extraction from streaming signal data \\ \hline [PERSON], [PERSON], and [PERSON] [95] & Feature extraction and selection from time series data \\ \hline [PERSON] et al. [99] & Clustering algorithms to extract representative data instances \\ \hline [PERSON], [PERSON], and [PERSON] [102] & Instance reduction based on distance from class boundaries \\ \hline \end{tabular}
\end{table} 
Table 4: Frameworks for data reduction. In the context of organizational competencies for big data analytics, much consideration is made as to the architecture for where the data exists, how it moves from one location to the next, and at which level or echelon the analysis takes place. In general, there is some layer or module at which the initial data is generated or collected but then options for what to do with it. Data may be migrated to a cloud-based data center and analyzed in a consolidated location, or it may be analyzed at local nodes. Whether to analyze at the edge or in the cloud will typically be a function of resources and of the time window available to perform corrective action. Actions that require real-time processing for quick action might not be performed at the cloud level because, by the time data is captured, cleaned, pre-processed, and run through a model, the window to correct an identified fault may have already passed. On the other hand, if there is sufficient time between the data collection point and the decision point, such as a manufacturing process in which there might be a gap of hours or days between assembly line procedures and testing, then analysis at the cloud level might be suitable. It should be noted that, from an organizational competency perspective, the infrastructure is a prerequisite to the development of individual competencies in the form of data scientist best practices. However, it is those data scientist best practices that become contributing factors to other organizational competencies such as knowledge management and decisions on long term data retention. There is an iterative and cyclic relationship such that organizational competencies produce individual competencies which then build and reinforce other organizational competencies.