Big data analysis should be viewed from two perspectives:
- Decision-oriented
- Action-oriented
A decision-oriented analysis is more akin to traditional business intelligence. Look at selective subsets and representations of larger data sources and try to apply the results to the process of making business decisions. Certainly, these decisions might result in some kind of action or process change, but the purpose of the analysis is to augment decision making.
Action-oriented analysis is used for rapid response when a pattern emerges or specific kinds of data are detected and action is required. Taking advantage of big data through analysis and causing proactive or reactive behavior changes to offer great potential for early adopters.
Finding and utilizing big data by creating analysis applications can hold the key to extracting value sooner rather than later. To accomplish this task, it is more effective to build these custom applications from scratch or by leveraging platforms and/or components.
First, look at some of the additional characteristics of big data analysis that make it different from traditional kinds of analysis aside from the three Vs of volume, velocity, and variety.
Bibliography: dummies. (2018). Characteristics of Big Data Analysis - dummies. [online] Available at: https://www.dummies.com/programming/big-data/data-science/characteristics-of-big-data-analysis/ [Accessed 11 Oct. 2018].
Image 1: Tehseen.dbsdataprojects.com. (2018). [online] Available at: http://tehseen.dbsdataprojects.com/wp-content/uploads/sites/107/2016/04/3vs1.jpg [Accessed 11 Oct. 2018].
Big Data visualization involves the presentation of data of almost any type in a graphical format that makes it easy to understand and interpret. But it goes far beyond typical corporate graphs, histograms and pie charts to more complex representations like heat maps and fever charts, enabling decision makers to explore data sets to identify correlations or unexpected patterns.
A defining feature of Big Data visualization is scale. Today's enterprises collect and store vast amounts of data that would take years for a human to read, let alone understand. But researchers have determined that the human retina can transmit data to the brain at a rate of about 10 megabits per second. Big Data visualization relies on powerful computer systems to ingest raw corporate data and process it to generate graphical representations that allow humans to take in and understand vast amounts of data in seconds.
Bibliography: Datamation.com. (2018). What is Big Data Visualization?. [online] Available at: https://www.datamation.com/big-data/big-data-visualization.html [Accessed 11 Oct. 2018].
Image 2: Static.timesofisrael.com. (2018). [online] Available at: https://static.timesofisrael.com/www/uploads/2014/08/israel-gaza-war-data-media-clusters-1407246759.22-148293.png [Accessed 11 Oct. 2018].
A defining feature of Big Data visualization is scale. Today's enterprises collect and store vast amounts of data that would take years for a human to read, let alone understand. But researchers have determined that the human retina can transmit data to the brain at a rate of about 10 megabits per second. Big Data visualization relies on powerful computer systems to ingest raw corporate data and process it to generate graphical representations that allow humans to take in and understand vast amounts of data in seconds.
Bibliography: Datamation.com. (2018). What is Big Data Visualization?. [online] Available at: https://www.datamation.com/big-data/big-data-visualization.html [Accessed 11 Oct. 2018].
Image 2: Static.timesofisrael.com. (2018). [online] Available at: https://static.timesofisrael.com/www/uploads/2014/08/israel-gaza-war-data-media-clusters-1407246759.22-148293.png [Accessed 11 Oct. 2018].
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