Kristen Sosulski - Collected Quotes

"A heat map is a graphical representation of a table of data. The individual values are arranged in a table/matrix and represented by colors. Use grayscale or gradient for coloring. Sorting of the variables changes the color pattern." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"A picture may be worth a thousand words, but not all pictures are readable, interpretable, meaningful, or relevant." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Avoid using irrelevant words and pictures. Only use charts that add to your message. […] In addition, words should be read or heard - not both. Decide which one supports the key takeaway for your audience." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Building on the prior knowledge of your audience can foster understanding. Ask yourself, what does my audience already know about the topic? What don’t they yet know?" (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Data graphics are used to show findings, new insights, or results. The data graphic serves as the visual evidence presented to the audience. The data graphic makes the evidence clear when it shows an interpretable result such as a trend or pattern. Data graphics are only as good as the insight or message communicated." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Ensure high contrast values for colors. Allow even those with a color vision deficiency or color blindness to distinguish the different shades by using contrasting colors. Convert graphs to grayscale or print them out in black and white to test contrast." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Pitfall #1: not sharing your work with others prior to your presentation [...]
Pitfall #2: lack of audience engagement [...]
Pitfall #3: little or no eye contact with the audience [...]
Pitfall #4: making your work unreadable (small font) [...]
Pitfall #5: over the time limit [...]
Pitfall #6: showing too much information on a single slide [...]
Pitfall #7: failing to use appropriate data graphics to show insights [...]
Pitfall #8: showing a chart without an explanation [...]
Pitfall #9: presenting a chart without a clear takeaway [...]
Pitfall #10: showing so many variables on a single visual display that they impair the readability of the chart or graph" (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Stories can begin with a question or line of inquiry." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Good data visualizations are persuasive graphics that help tell your data story. When you begin any visualization project, how do you know if your audience will understand your message? Your audience has input in the data visualization process. Consider what they already know and don’t know. Determine how you will support them in identifying and understanding your key points. " (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"Use color only when it corresponds to differences in the data. Reserve color for highlighting a single data point or for differentiating a data series. Avoid thematic or decorative presentations. For example, avoid using red and green together. Be cognizant of the cultural meanings of the colors you select and the impact they may have on your audience." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)

"When there are few data points, place the data labels directly on the data. Data density refers to the amount of data shown in a visualization through encodings (points, bars, lines, etc.). A common mistake is presenting too much data in a single data graph. The data itself can obscure the insight. It can make the chart unreadable because the data values are not discernible. Examples include: overlapping data points, too many lines in a line chart, or too many slices in a pie chart. Selecting the appropriate amount of data requires a delicate balance. It is your job to determine how much detail is necessary." (Kristen Sosulski, "Data Visualization Made Simple: Insights into Becoming Visual", 2018)