BUS612: Data-Driven Communications

5a. Plan informational presentations

• What are the different sources of data for an information presentation?
• How would you compare the two ways of obtaining data? How can each one be properly used?

When you present data, start with a story: a message or a point you are trying to make. The message should be crafted for the intended audience, with a clear narrative flow and visuals that connect and convey that message.

A lot is involved in creating an informational presentation. Once the message and the point have been determined, the data required to address the query must be obtained. Using data as a source can help with all your research project's information needs. It can help to:

When planning informational presentations, it is important to evaluate various data sources. This involves comparing different methods of data acquisition and assessing their appropriate utilization.

• Acquiring background information: Data is a valuable resource for gaining a deeper understanding of the subject matter.
• Answering research questions: The evidence provided by data aids in determining the most suitable answer to your research question.
• Convincing the audience: Data often supplies evidence that supports your research question's answer, ensuring its credibility or reasonability.
• Describing the research context: Data helps provide a comprehensive picture of the circumstances surrounding your research question.
• Reporting existing perspectives: Data allows you to incorporate the opinions and findings of other researchers into your presentation.

There are two primary methods of obtaining data. The first involves utilizing pre-collected and analyzed data. The second method entails collecting data directly through environmental observations, surveys, interviews, laboratory or field measurements, or even electronically recorded data from computers or machines.

The most effective criterion for selecting a display format for your data is whether it enhances your understanding and that of your audience beyond what can be conveyed through text alone. A well-designed display should enable you to notice key points that might have been overlooked.

To review, see Data as Sources.

 

5b. Identify visualizations that convey data findings

• What are some key considerations when selecting visualizations for data findings?
• How do different visualizations (such as bar charts, line graphs, and scatter plots) convey specific data findings?
• How can visualizations enhance the understanding and communication of data findings?
• What are some best practices for creating effective and informative visualizations?

Identifying suitable visualizations is crucial for effectively conveying data findings. Considerations for visualization selection, types of visualizations, enhancing understanding and communication, and best practices for visualization creation are important topics to identify visualizations that convey data findings.

When selecting visualizations, it is essential to consider factors such as data type, the purpose of the analysis, the audience, and the message to be communicated. Various visualizations, including bar charts, line graphs, scatter plots, and heatmaps, have distinct strengths and limitations that align with specific data findings and relationships.

Visualizations enhance understanding and communication by providing visual cues, patterns, and trends that are more accessible than raw data alone. They facilitate concise and impactful communication of complex findings to diverse audiences.

Following best practices for visualization creation, such as the appropriate use of colors, labels, titles, axes, and legends, ensures that visualizations are informative, accurate, and visually appealing. Simplicity, clarity, and alignment with the intended message are emphasized, while clutter, misleading representations, and unnecessary complexity should be avoided.

Addressing the guiding questions, considerations for visualization selection include the data type, analysis purpose, audience, and effective communication of findings. The impact of data collection on analysis quality influences the suitability of visualizations, emphasizing the importance of accurate and representative data for meaningful and reliable findings.

Common challenges in data collection, such as non-response bias, sampling bias, and measurement errors, can introduce biases and inaccuracies that affect the validity and reliability of findings and, subsequently, the choice of visualizations.

Analytical approaches such as statistical analysis, exploratory research, and business intelligence contribute to data-driven decision-making by providing insights and patterns that can be visually represented. Statistical analyses identify relationships and patterns, exploratory research uncovers trends, and business intelligence supports strategic decision-making through data-driven visualizations.

To review, see Data Visualization to Promote Public Awareness and Creating Good Visualizaitons.

 

5c. Apply data narratives to visualizations

• What approaches to creating credible and effective visualizations to convey data findings are most effective?
• How can different disciplines collaborate on digital projects for educational and commercial uses?

Good visualizations don't happen by accident. The user/creator must take a systematic approach to creating credible and effective visualizations. The user must systematically think about visualizations and how to reason between different visualization design choices.

Data visualizations can be used in multiple disciplines to report data outcomes. It has been used for many cross-disciplinary applications, such as computer science, marine science, and art, to collaborate on digital projects to educate the public on ecological issues. 

To review, see Creating Good Visualizaitons.

5d. Evaluate when visualizations are needed to explain a concept

• Why is reducing the need for an audience to interpret the key to create an effective presentation?

Reducing the need for the audience to interpret the findings in visualization is the key to an effective presentation. This can be accomplished by the type of chart used or by highlighting key points through color choices.

There are five distinct levels of user engagement in information visualization.

1. Expose (viewing): At this level, the user comprehends how to interpret and engage with the presented data, understanding the basic mechanisms of the visualization.
2. Involve (interacting): Moving beyond passive viewing, the user actively interacts with the visualization, manipulating the data to gain a deeper understanding and explore different perspectives.
3. Analyze (finding trends): At this level, the user delves into the data, conducting analyses to identify patterns, trends, and outliers within the visual representation.
4. Synthesize (testing hypotheses): Building upon the analysis, the user reaches a level where they can generate and evaluate hypotheses based on the data presented. This stage involves synthesizing information to formulate and test various hypotheses.
5. Decide (deriving decisions): The highest level of user engagement is achieved when the user can make informed decisions and draw conclusions by critically evaluating different hypotheses and considering the implications of the presented data.

Visualization embellishment is useful for the comprehension and memorability of charts. When plain and embellished charts were compared, a user's accuracy in describing them was similar, but their recall after a two- to three-week gap was significantly better.

As an illustration, consider two distinct approaches to visually enhancing the same data set. The first example depicts a graph incorporating embellishments while preserving a bar chart's recognizable characteristics. The second example replaces the bars with a silhouette of a person positioned alongside a beverage, where the drink's height corresponds to the original bar's height. Additionally, this approach utilizes color to highlight and emphasize the data points.

To review, see Storytelling and Visualization.

 

5e. Evaluate the proper visualization for a given data set

• How do visualizations make large amounts of data easier to understand?
• How do visualizations reveal data at several levels of detail?

Well-crafted data visualizations present data as easily understood images. When done well, they enable the viewer to quickly perceive insights they may have missed if presented in summary tables and spreadsheets. Good data visualization does not only convert large amounts of data into images; when done well, it engages the viewer and tells a story.

Well-crafted visualizations present complex ideas or results and communicate them clearly, precisely, and efficiently. Visualizations should:

• Show the data
• Have the viewer on the substance instead of the methodology
• Avoid distorting the data
• Present many numbers in a small space
• Make large data sets easier to understand
• Present the data at several levels of detail, from a high-level overview to a deep data dive

To review, see Data Visualization Design and Principles.

 

5f. Present data to a non-technical audience in an ethical manner

• What are the uses of narrative charts in storytelling?
• What are the key components of an effective narrative chart?

Narrative charts are another form of data visualization. It combines the best of data visualizations, storytelling, and some of our favorite historical or fictional tales. They describe an experience, event, or a sequence of events in the form of a story. Often, they are used to depict the structure of a movie or book.

The key is that these narratives are usually created for non-technical audiences, and they must follow the sequence of events from the original story source.

To review, see Narrative Charts Tell the Tale.

 

Unit 5 Vocabulary

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

• analyze
• decide
• direct data collection
• expose
• involve
• narrative
• narrative chart
• pre-collected data
• story
• synthesize
• systematic approach