Data Visualization
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Find two data visualization products to review. These may be either product which you are already familiar or products you could research. Regarding the use of data visualizations, what are the products’ advantages? What are some of the products’ disadvantages? Which product would you prefer and why?
Data visualization entails the representations of information or data in a chart or other visual representation format. Use of visualization is vital as it demonstrates the correlations of the data or information with the graphics. The two products that we will explore in this discussions are Tableau and QlikView software. Qlikview is a data visualization products which facilitate simulations of analytical data applications. Furthermore, the products have the capacity of enabling the users to create a customized or default data templates (Olshannikova et al., 2015). The merits of these product is its flexibility in meeting all the organizational needs. The data visualization, as well as the insights of the products, can be shared. Qlikview has high accuracy, especially on manipulating large data sets. In contrast, the product requires high technical skills and can be inefficient at particular periods (Fiaz et al., 2016).
Tableau software is a product which makes a connection to the data sources. Insights are derived from the transformation of the data into interactive visualization referred to as dashboards. The product is essential as it enables an organization to out win their competitor via an intuitive technique of visualizing data. Creation of interactive visualization with the user of Tableau functions is easier, and it can process large data sets. However, the product lacks automatic functionality, thereby necessitating manual effort in updating the data. On comparison of the two products, Tableau is easier to understand and implement when compared to QlikView. Besides, Tableau has numerous visualization functionalities that enhance the experience of the user. Therefore I would choose Tableau software product.
References
Fiaz, A. S., Asha, N., Sumathi, D., & Navaz, A. S. (2016). Data visualization: enhancing big data more adaptable and valuable. International Journal of Applied Engineering Research, 11(4), 2801-2804.
Olshannikova, E., Ometov, A., Koucheryavy, Y., & Olsson, T. (2015). Visualizing Big Data with augmented and virtual reality: challenges and research agenda. Journal of Big Data, 2(1), 22.