# Big Data Analytic Framework for Organizational Leverage
## 2 Related Works
### Impact of Big Data Analytics
In their proposed framework, big data collected from external (e.g., health maintenance organizations, insurance companies, pharmacies, laboratories, government sources) and internal sources (clinical decision support systems, electronic health records, etc.) are pooled as part of the first layer in big data sources. Then, raw data are transformed (cleansed and processed) and stored in data warehouses. The next component involves the selection and application of appropriate tools for building analytics models. Finally, the data are presented in various formats, readable for decision-making. In studies related to analytics capability at Twitter, phenomenal growth has been seen in terms of variety of use cases, number of users, complexity, and size of \"big data\" mining in the last decade. The evolution of Twitter's capability development and creation of infrastructure for big data mining has required much preparatory work that has preceded the application of data mining algorithms and robust solutions. Schemas have played a vital role in assisting their data analysts in understanding petabyte-scale data stores, although they have been insufficient in generating overall \"big picture\" insights [31]. A major challenge for the company has been in building a data analytic platform stemming from the heterogeneousness of the different elements that must be integrated into production workflows. Short message-based sentiment mining using a keyword-based classifier has helped Twitter to outline a cataloging process with the potential to extend and further add sentiment-based dimensions, providing a deeper understanding about user preferences [32]. Sentiment analysis allows detection of trends and patterns emerging from data effectively. In another Twitter-based study looking at sentiment analytics, authors reviewed the correlation between the approval rating of (President of the United States) [PERSON] and his tweets [33]. The mined and cleaned Twitter feed of [PERSON] was quantified on content basis, or \"sentiment score\". Evaluation of tweets in different stages of Mr. [PERSON]'s election campaign showed that his sentiment score increased by 60% over time, revealing a causative correlation between approval rating and [PERSON]' Twitter activity. [PERSON] et al. [16] extended an earlier study (conducted in 2014) to propose a five-layer architectural framework for BDA. These are (1) data layer comprising raw data, including structured data (from traditional digital clinic records), semi-structured data (as a result of monitoring patient logs), and unstructured data (or doctor notes); (2) data aggregation layer that handles data from varied sources of data. The data are read from communication channels, transformed after cleansing and sorting, and loaded into a target database for subsequent analysis; (3) analytics layer responsible for applying appropriate analytics. Depending on the purpose of the analysis, three main analytic tools--in-database analytics, stream computing, and Hadoop map/reduce--may be implemented; (4) information exploration layer that holds the final outputs of the BDA. This mainly includes readable reports for decision-makers to analyze; however, [PERSON] et al. point out that the most important output in healthcare is information monitoring in real time from proactive notifications and alerts. Such information can be analyzed from real-time monitoring devices such as personal medical devices and mobile phones; and (5) data governance layer that emphasizes master data management (MDM) to ensure the data's availability, accuracy, completeness, and immediacy. MDM helps to manage business information throughout its lifecycle, prevents security breaches, and assists organizations in protecting patient privacy.