# Big Data Analytic Framework for Organizational Leverage
## 5 Discussion
### Data Consumption by End Users  
The data clients or end users receive the big data system output focused on their decision-making requirements. Like data sourcing, data output can be consumed by end users or other application systems in the form of business insights. Activities performed by data clients/end users typically include search/retrieval, download, local analysis, report generation, visualization, and, finally, decision-making based on predetermined goals. End users source the information that they are interested in using services or interfaces provided by big data service providers, including data rendering, data retrieval, and data reporting. End users also interact with big data service providers through data sourcing activities to perform the data analysis and visualization capabilities available. Interactions can be demand-based, including interactive visualization, creating reports, or using a BI tool provided by the big data application service to drill-down data. The interaction function can also be based on a streaming-based or push-based mechanism, in which case the client only needs to subscribe to the output of the big data application system. The above four main components identified by the participants align with the research framework proposed by [PERSON] and [PERSON] (2018) and [PERSON] et al. (2018). As suggested by the participants in the study, three governing characteristics of a big data analytic framework--data security, data privacy, and data management--form essential building blocks that represent the services to provide functionality across the information and technology value chains. These are in concurrence with the four main components of the BDA process, namely system coordination, data sourcing, big data application service, and end users. Although these characteristics are not categorized as components in big data analytics, they are deemed as vital layers that a big data analytic framework should implement. Figure 1 presents a proposed framework with these layers implemented. The holistic framework comprises two dimensions that represent the big data value chain--the information value chain (horizontal axis) and the IT value chain (vertical axis). In the information value chain dimension, the value of big data is achieved by accessing relevant data streams through the BDA application in four stages--data collection, preprocessing, analysis, and visualization--leading to business insights for end users. In the IT value chain dimension, big data value is achieved by providing networks, infrastructure, platforms, application tools, and other IT services that store and run big data for big data applications. The system coordination with the BDA application service is at the intersection of both dimensions. In the BDA framework, privacy and security components are considered as part of the management role, which also means that the privacy and security roles are related to all the activities and functions within the framework. According to participants, the role of management in BDA is often disregarded, since most organizations see the analytic as a technical challenge. Although this is not wrong, as the core component of BDA is technical, it is vital for organizations to have management governance in place to ensure the functioning of an efficient system. This feedback aligns with the literature ([PERSON] et al., 2018; [PERSON], 2018; [PERSON], 2018) that emphasizes management of a BDA as a critical challenge. As advised by [PERSON] (2018) in his study, the cardinality parameter pertaining to the number of records at any point in an ever-growing dataset demandsthe requirement of an effective dispersed database system for capturing data, storage, and analysis of network traffic to predict intrusion. Then, the parameters of continuity (in terms of growth of dataset) and complexity (due to high variety and speed of data processing) add extra difficulties to the task of managing the big data.