The Influence of Data Visualizations

The Influence of Data Visualizations
Data visualizations play an increasingly important role in today’s data driven operations. They have a way of making people more aware of just some of the many relationships that are concealed within the current magnitude of data surrounding us. Oftentimes, selecting the right data visualization can aid in one’s overall confidence in the quality of data. Visualizations are not only an eminent means of illustrating various facets of data quality, but they also help to give data vibrancy which otherwise eludes it.
Moreover, there are a number of contemporary techniques in various domains such as machine learning and other cognitive capabilities that involve data visualizations. Progressive technologies such augmented reality, virtual reality, and other variations of extended reality were specifically designed to reinforce visualizations’ chief value proposition. They are able to effectively use images to illustrate relationships that can prove valuable in both academic and social settings. Having recently learned how important it is to select such a tool, I would deploy a cluster diagram to analyze the High School Longitudinal Study dataset.
I’m sure the findings would more than likely indicate that there are definitely patterns, if not characteristics, that are visible at the social level found in academic settings, even some of the earliest ones (Frankfort-Nachmias & Leon-Guerrero, 2018, p. 49). By using visualizations designed for semantic graph technology, for instance, one could identify some of the points of bias in the dataset that could prove even more valuable in terms of its practical social utility.
Still, it is never easy to pinpoint the exact vectors that affect change in a social setting as large as our contemporary one is. Were this author to consider the findings in this matter, it would certainly appear that social change is actually produced from a series of forces, or groups, perhaps, which affect it in numerous ways. Actually, one of the most notable findings revealed from using a semantic graph based data analysis mechanism which is best suited for high dimensional data sets on the High School Longitudinal Study dataset, is that data can almost always be grouped by more than one means. The findings of the High School Longitudinal Study dataset offer some substantial insight into this matter.
In some instances, the usage of relational tables, or even just the numeric values themselves, would prove sufficient for illustrating data’s true worth. However, when one begins to identify factors at a micro level that are contributing to the formation of groups such as is visible in the dataset examined for this assignment. In particular, the categorical variable examined in this visualization showed that at this point in their lives, there is a fiar amount of compartmentalization. In the professional world, this capacity of data visualizations is used to exploit business value in various avenues, most notably marketing, finance, and even some facets of the public sector.
Still, the primary conclusion is that this tendency to view others in categorical terms, or to even have social categories in mind at all when one encounters others, is something that is ultimately supported by the evidence analyzed in the data visualization for this particular assignment. Ultimately, such tendencies to categorize others imply that social changes would occur in cycles, or not as dynamically as some people might think they occur. The obvious implications are that social change would occur in cycles, and have definite patterns which affect the way it ultimately manifests in society.
References
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 2, “The Organization and Graphic Presentation Data” (pp. 23-62)
Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications.
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