Managing and Visualizing Unstructured Big Data

Managing and Visualizing Unstructured Big Data

Copyright: © 2018 |Pages: 12
DOI: 10.4018/978-1-5225-2255-3.ch035
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One of the most common terms that is used in a significant amount of popular and scholarly discussion is “Big Data.” As pointed out earlier, the term has a dubious history and different people claim ownership (see, e.g., Mitra, 2014). What remains true of the notion of Big Data is that it exists. With the increasing rate at which institutions and individuals are digitizing many different kind of information the amount of data can only go up in volume. Therefore, Big Data has become an object of analysis for a variety of groups, from academics to marketers, all of whom are interested in understanding how Big Data could provide highly granular information about people.
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This essay expands on the notion of “Big Data” to open up alternative analytic opportunities, on certain components of the data, through a theoretical lens that is mobilized to offer an interpretation and visualization of the information contained in large amounts of data. It is useful first to examine the term “Big Data” in some detail. The term refers to a phenomenon which results from the fact that institutions and individuals are digitizing many different kinds of information leading to an exponential growth of the amount of data can that is being stored in the digital space. First, this expansion relates to the increase in data points as more records are added to the corpus of Big Data. Second, the idea of Big Data needs to be considered in terms of the details that are being digitized. The notion of Big Data should be considered both in terms of the breadth of the data in terms of number of data points (amount) and the depth of the data related to the various fields of information available for each record (details). Therefore, Big Data has become an object of analysis for a variety of groups, from academics to marketers, all of whom are interested in understanding how Big Data could provide highly granular voluminous information about people (see, e.g., Mitra, 2014c). Next, it is useful to examine the different categories of information that makes up “Big Data.”

Much of Big Data is numeric that is amenable to mathematical analysis. For instance, it is possible to easily count the number of tweets produced by an individual. Such counts offer the opportunity for companies such as Tweeter to offer information about what topics are popular at any moment in time. The segment of Big Data that offers the ease of analysis and visualization has been called “structured” Big Data. There is, however, another vast component of Big Data that does not allow for easy numeric analysis. This segment is made up of the utterances of the people who are self-generating the Big Data by voicing themselves in the digital space. An example of this segment of Big Data is the actual specific tweet produced by an individual or the specific photograph uploaded on photograph sharing spaces. In the case of the tweet, the language of the tweet contains information about attitudes and opinions, just as a photograph offers information about the individual who has captured the picture. This form of data requires a more nuanced and “qualitative” analytic process that would discover the intent of the authors and the meanings of the messages encapsulated in a microblog or picture. This segment of Big Data has been named “unstructured” Big Data. Not only is it difficult to analyze the unstructured Big Data but it is also difficult to visualize the findings of analysis. The qualitative process does not typically produce convenient charts and graphs. The analysis needs to be offered for easier understanding and unstructured Big Data makes this a challenge as well.

This paper offers a theoretical and analytic process to consider ways of analyzing Big Data and visualizing the analysis. To do this, it is important to offer a theoretical basis to consider the elements of unstructured Big Data.

Key Terms in this Chapter

Data Visualization: The process of translating complex data and data interpretations into visual graphs and figures that capture the complexity in easy to understand and easy to communicate images.

Narrative Map: Narrative maps are produced from analysis of narbs, where the maps are made up of specific nodes and connectors. The nodes represent attitudes, concepts, behaviors and different issues that people are talking about in their narbs. For instance, a common node is “positive opinion” which could be made up of positively affected terms such as “like,” “love,” “good,” etc. Similarly, another node could be “immigration” referring to the issue. The map connects nodes together to indicate how strongly two nodes are related to each other. For instance, a dark line between two nodes would show a strong relationship between the nodes, suggesting people are talking about two things simultaneously, almost “in the same breath!”

Narb: As stated by the World Future Society on their Web site ( AU35: URL Validation failed because the page points to a restricted resource (HTTP error 403). ): “Narrative bits, or “narbs,” refer to small bits of information in the digital universe that, when collected, tell an otherwise untold story. The term is credited to Wake Forest University communication professor Ananda Mitra, who believes that narbs offer a way to turn massive amounts of social communication into a tool for predicting behavior and reactions.”

Big Data: This is personalized data that is coming from people who are actively and voluntarily contributing to the compilation of these data sets. Much of the attention on big data has focused on the two key components: (1) gathering the large amounts of data and (2) quantitatively analyzing the data to obtain both personal-individualized information as well as information about different groups of people.

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