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data visualization

The insights derived from analysis can be as varied as the original question that was posed in step one of analysis. At this stage, as the nontechnical business users are the receivers of the information, the form of presenting the data becomes important. How could the data make sense efficiently so it could be used in good decision making? Visualization (graphics) of the information is the answer to this question.[9]

The best visualizations are ones that expose something new about the underlying patterns and relationships contain the data. Exposure of the patterns and understating them play a key role in decision making process. Mainly there are three criteria to consider in visualizing data.

  • Understand the audience: before building the visualization , set up a goal, which is to convey great quantities of information in a format that is easily assimilated by the consumer of information. It is important to answer, who is the audience, and can you assume the audience has the knowledge of terminologies used. An audience of experts will have different expectations than a general audience; therefore, theexpectations have to be considered.[10]
  • Set up a clear framework: the analyst needs to ensure that the visualization is syntactically and semantically correct. For example, when using an icon, the element should bear resemblance to the thing it represents, with size, color, and position all communicating meaning to the viewer.[10]
  • Tell a story: analytical information is complex and difficult to assimilate, thus, the goal of visualization is to understand and make sense of the information. Storytelling helps the viewer gain insight from the data. Visualization should package information into a structure that is presented as a narrative and easily remembered. This is important in many scenarios when the analyst is not the same person as a decision-ma