If we are to represent complex data analysis and represent information showcasing relationships, and trends, then visual representation is tantamount. Several studies indicate that humans can retain up to 85 percent of information when the input is in visual form. These coefficients are summaries to large data sets, which is useful in far more advanced analytics. Correlation matrices store coefficients, and they are used predominantly in big data visualization to aid quicker response times for analysis. Network Diagram - Used in the representation of data from unstructured data sets, network diagrams consist of nodes and links between these nodes with a value signifying their relationship.Ĭorrelation Matrices - Correlation matrices represent relationships between two variables. Larger font words repeatedly occur, whereas smaller font words are of less use.
Word Clouds - Used in the analysis of big data composed generally of contextual text, this visualization tool extracts words, phrases, and even sentences, displaying them in varying sizes that are relative to their occurrence in the analyzed data sets. It is a popular method to understand correlations between variables. When the measures of the variables are increased or decreased, a scatter plot is formed. Scatter Plots - This form of plotting visualization uses a two-dimensional dot, square, or any form of marking, representing the relationship (or joint-variance) between two variables on a graph. Map representations are prevalent in sports, and in the armed forces as well. These techniques help us position objects relative to a geographical formation, buildings, and other areas. Maps - We have all heard of heat maps and dot maps. The variables in the pie-chart are compared relative to percentages they form out of the whole system, or even degrees composed out of the full circle. Pie Charts - The visualized data format helps form perspectives while comparing individual data variables to the whole system. The bar charts can be arranged horizontally or vertically on a graph - depending on their groupings and their height, or length which depicts the value. This dependence can be called the plot function.īar charts - This data representation technique comes in handy when comparing quantities and or values of different variables. Line Plot - One of the most straightforward techniques in visualization, plotting lines between two points, highlights the relationship between two variables. Some commonly used visualization techniques are as follows. These techniques guide us in analyzing quickly from the data and forming a consensus. Data Visualization techniques are commonly used all over the world - from business powerhouses to educational institutions.
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