BUS612: Data-Driven Communications

3a. Select appropriate visualizations to explore data findings

• What is the difference between presentation graphics and exploratory graphics?

Data visualizations are crucial in data analysis, including cleaning, exploring structure, outlier detection, trend identification, cluster identification, pattern recognition, evaluating modeling output, and result presentation. They are indispensable tools for exploratory data analysis and mining, ensuring data quality checks and enhancing an analyst's understanding of the data's structure and characteristics.

Presentation graphics are usually a select number of graphics created for any number of people and need to be well-designed and well-created with an effective explanatory text, either verbally or textually. They are used to convey known or summarized information.

Exploratory graphics can include several graphics created for an individual such as yourself. They don't need to be perfect but provide alternate views and additional information. 

To review, see Why Is Data Visualization Important? and Presenting Data in Meaningful and Interesting Ways.

 

3b. Encode and annotate data visualizations

• What are the best practices for creating data-driven visualizations free of unnecessary distractions?

For visualizations to be the most effective, they must be data-driven, clear and accurate, and free of items that may distract the viewer from the message.

Interactive visualizations enable the viewer or presenter to drill down into the available data to provide additional insights.

To review, see Data Visualization.

 

3c. Evaluate visualization techniques

• Name the challenges in visualization methods for analyzing big data.
• Identify the purposes of big data visual representation.
• Explain the importance of rigorous analysis in the creation of data visualizations.

Visualizations are valuable tools for analyzing diverse information that is graphically formatted. Effective visualization tools consider the human brain's cognitive and perceptual properties. They aim to enhance clarity, aesthetics, and comprehension of displayed information, enabling individuals to make sense of large data sets and interact with them. In big data, visual representation serves significant purposes such as identifying hidden patterns or anomalies, facilitating flexible searches, comparing units to determine relative differences in quantities, and enabling real-time human interaction.

However, it's important to recognize that data visualization is not an ultimate objective. Instead, it is an integral part of a larger process that involves gathering, formatting, and analyzing data to generate meaningful insights. Neglecting this process is ill-advised. While using data visualization to present enticing infographics about people's beverage preferences may have minimal consequences in terms of persuasion, it becomes more critical when visualizations are employed to support or oppose public policy decisions. Misuse of data visualization for persuasive purposes can have profound implications. Without rigorous analysis, data visualization becomes, at best, mere rhetoric and, at worst, potentially harmful. A responsible approach is essential to ensure accurate interpretation and mitigate potential negative outcomes.

To review, see Describing Data.

 

Unit 3 Vocabulary

This vocabulary list includes the terms you will need to know to successfully complete the final exam.

• data visualizations
• exploratory graphics
• presentation graphics