Data Visualization: Case Review Summary

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Main Topic:

Data visualization means representing data in a visual format like graphs or charts. The visualization is necessary in day-to-day IT world as we can see the patterns and trends of the data. Without visualization, finding hidden patterns and anomalies in data would be very difficult. The most critical mindset for a data visualizer is the overall business message that he or she want to communicate and what the influence would be.

For developing an effective way to think visually one should know the answers to the below 2 questions:

1. Is the information that needs to be presented data driven or conceptual?

2. Does the visualization conclude something or explore something?

The response to the above queries would provide a better insight to the tools and type of visualization that would be required for a good presentation.

for conception information, the concentration should be on ideas and goals should be to simplify and instruct. For Data driven information or facts, the focal point should be on statistics and goals would be to notify and educate. For declarative visual information, the focus should be on logging all designing with a goal to declare or confirm. For Exploratory information, the code focuses on interacting or automating & the goal would be to affirm or identify.

Explanation on contents:

The overall questions of purpose and nature defines four types (2X2) visual communication.

Idea Illustration: Conceptual & Declarative

This quadrant is called the “Consultants Corner” and these illustrations rely on metaphor (trees, bridges). Also, they include various graphics like decision trees & process diagrams. Idea illustration should be clear and simple with main focus on logic & structure. Here are the type of information will be process framework with teaching & presentation being the regular setting.

Idea Generation: Conceptual & Exploratory

This quadrant As well relies on conceptual metaphor but usually happens in brainstorming strategy sessions. It is used to find new ways of seeing how business works and to answer complex management challenges. Classic examples are writeups on a whiteboard or napkins. Its More informal setting. Later, these visuals can be converted into more formal idea illustrations. Team building would be the main skill, moving towards the goal of innovation and problem solving.

Visual Discovery: Exploratory & Data-Driven

This is one of the most complicated quadrants and holds 2 categories:

1)Visual Confirmation;

2) Visual Exploration.

Visual Confirmation Indulges in answering queries such as, is the debatable information true? or in what different ways we can present this idea? For these kinds of visual setup, the critical skills would be to play with spreadsheets and knowledge with paired analysis and programming.

Vision exploration pertains to data driven open-ended content in which one is not aware of what he or she is looking for. For example, the best way could bring out project trends and relations for huge data in a spreadsheet is to plot it on a chart or a graph. Skills such as data management, business intelligence and analytical programming would prove handy.

Everyday Dataviz: Data-Driven & Declarative

This quadrant is a simple display of graphs, charts and scatter plots. The message should be simple enough and provide clarity in order to explain itself in a timely fashion. Here the goal would be to affirm setting context.

Key Takeaways:

• Visualization is a process of designing where the person who presents it would require knowing what's the idea that needs to be conveyed and not just the presentation.

• Data visualize properly should be able to tell a clear story.

• A person with good mindset on visualization would make people to see and visualize what they would have never thought of, changing mindset and making them to learn.

• In order to achieve a good visualization better data interpretation is very critical.

• The analysis given in the paper is constructed to give a wider perspective to promote better visualization.

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Data visualization: Case Review Summary. (2022, Sep 29). Retrieved June 24, 2024 , from

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