# Big Data Analytic Framework for Organizational Leverage
## 2 Related Works
### Capabilities of Big Data Analytics  
The prior literature in big data analytics has identified five main categories of BDA in terms of process capabilities, which include analytical capability, unstructured data analytical capability, decision-making support capability, predictive and preventive capability, and traceability.  
Analytical capability refers to the use of tools and techniques in a BDA system that allow big data to be processed and rationally analyzed [25]. For example, analytical capability enables healthcare firms to uncover associations from the enormous healthcare records by identifying emerging patterns among patients [16]. Healthcare organizations can capture big data in real time for processing patients' medical data and potentially identifying previously unnoticed symptoms in patients. In the telecom industry worldwide, analytical capability enables communication service providers (CSPs) to reduce churn and properly harness large data volumes to increase their revenue intake. Furthermore, it allows them to analyze customer patterns or behaviors to identify potential market trends and put forth attractive personalized offers. By adding real-time semantic insights to business decisions, they can offer targeted marketing to improve their market brand.  
Between legacy (traditional) data mining methods and big data analytics, the biggest difference is the unique ability of the latter in analyzing unstructured and semi-structured data [16]. Whether it is dealing with doctors' handwritten notes or a social media interaction, the data analytical capability of BDA entails dealing with unstructured data. According to studies, 27 percent of the digital data today are unstructured and 21 percent are semi-structured, which sums up to 48 percent of the total modern data [2; 13]. [PERSON] et al. [16] have stressed that the analysis of unstructured data leads to successful targeted marketing, revenue growth, and allows the building of customer segmentation. These data are complex in nature and must be pre-processed before useful insights can be extracted.  
BDA supports strong decision-making capability. By producing informative reporting statistics, a big data analytics system can enable managers to understand customers and/or patients, which in turn helps to further improve their decisions and subsequent actions. Furthermore, the reports allow managers to identify business opportunities that traditional business intelligence would be incapable of recognizing. This is evident in the case study of the Continental Airlines, in which the company could turnaround its performance to become a top-ranked airline [9].  
Analyzing big data enables organizations to build a more dependable business model with the insights collected. This allows organizations to accurately predict events that are likely to happen and accordingly plan a preventive strategy in advance. In healthcare, predictive capability allows organizations to achieve better cost-saving as well as ensure the implementation of broad-scale disease profiling. In the telecom industry, predictive and preventive capability allows for congestion control as well as churn identification and prevention.  
Traceability is the ability for organizations to trace back to the origin from which the data were collected. This is especially important in the healthcare industry, where individual patients can be monitored closely even when they are away from healthcare facilities such as hospitals. This means providing personalized care and home care, which, in a way, increases the healthcare facilities' capacity, allowing for better medical supply utilization rates as well as reducing delays in patient queues. Fraud detection is also possible with real-time data analysis in both the healthcare and telecom industries.