visualizations and presentations

Question 1

visualization key considerations

Answer 1

EvDt P McDp

• McCandless (Mc) method for visualizations
• design thinking (Dt) (which is in no way limited to data visualizations)
• pre-attentive attributes: marks and channels
• some common or main chart types (and eg visualization-choices decision trees)
• artistic elements, and design principles (Dp) (there are a lot of these)
• patterns to consider in data visuals (P)
• main guidelines for effective visualizations (Ev)
• things to avoid in visualizations–colors that don’t contrast well or that run contrary to sense (eg red for increases); too many labels
• static vs. dynamic visualizations
• addressing accesibility
• storytelling approach to visualizations and presentations–engage your audience, create compelling visuals, tell the story in a meaningful way
• good data journalism–set context, analyze variables, draw conclusions

Question 2

McCandless method for visualizations (Mc)

Answer 2

ISGF

• information–the data with which you’re working; reflects the conclusion(s) drawn from the data
• story–a clear and compelling narrative throughline, or concept; adds meaning to the data and makes it interesting
• goal–a specific objective or function for the visual; makes the data usable and useful; allows the viewer to draw actionable insights
• visual form–an effective use of metaphor or visual expression; creates both beauty and structure

Question 3

design thinking (Dt)

Answer 3

P DIET

a process used to solve complex problems in a user-centric way; apply a user-based mindset; note, this is not restricted to visualizations

main phases:
* empathize–emotions and needs of the target audience in context of your database; consider accessibility–eg color vision deficiencies
* define–helps define audience needs, problems, and your (data) insights; which data should be shown?
* ideate–generate data visualization ideas; eg create drafts with different color combinations and shapes
* prototype–put visualization and charts together; eg may create many visualizations, and do process of elimination
* test–share with other workers, etc, and listen to feedback

Question 4

artistic elements, and design principles (Dp)

Answer 4

C COIN

• choose the right visual–eg a table or a chart
• optimize the data-ink ratio–the data-ink ratio entails focusing on the part of the visual that is essential to understanding the point of the chart–try to minimize non-data ink like boxes around legends or shadows to optimize the data-ink ratio
• use orientation effectively–for text, ensure the written components of the visual, like the labels on a bar chart, are easy to read; you can change the orientation of your visual to make it easier to read and understand
• color–use color consciously and meaningfully, staying consistent throughout your visuals, being considerate of what colors mean to different people, and using inclusive color scales that make sense for everyone viewing them
• number of elements–this can mean a lot of things, but basically avoid overcrowding the visual with too many elements of a given type; If your visualization uses lines, try to plot five or fewer. If that isn’t possible, use color or hue to emphasize important lines. Also, when using visuals like pie charts, try to keep the number of segments to less than seven since too many elements can be distracting.

Question 5

patterns to consider in data visuals (P)

Answer 5

CCCRR / hol/ea

• Change: This is a trend or instance of observations that become different over time. A great way to measure change in data is through a line or column chart.
• Clustering: A collection of data points with similar or different values. This is best represented through a distribution graph.
• Relativity: These are observations considered in relation or in proportion to something else. You have probably seen examples of relativity data in a pie chart.
• Ranking: This is a position in a scale of achievement or status. Data that requires ranking is best represented by a column chart.
• Correlation: This shows a mutual relationship or connection between two or more things. A scatterplot is an excellent way to represent this type of data pattern.

Question 6

main guidelines for effective visualizations (Ev)

Answer 6

5 ConColuM

• Five-second rule: A data visualization should be clear, effective, and convincing enough to be absorbed in five seconds or less.
• Color contrast: Graphs and charts should use a diverging color palette to show contrast between elements.
• Conventions and expectations: Visuals and their organization should align with audience expectations and cultural conventions. For example, if the majority of your audience associates green with a positive concept and red with a negative one, your visualization should reflect this.
• Minimal labels: Titles, axes, and annotations should use as few labels as it takes to make sense. Having too many labels makes your graph or chart too busy. It takes up too much space and prevents the labels from being shown clearly.; it might also help to have a single unified font for the labels

Question 7

presentations key considerations

Answer 7

Mc NeKe

• narrative needs / elements (Ne) for a good presentation
• tips for spoken presentations and tips for slides–speaking velocity, even pitch, use short sentences; pause 5 seconds after showing a visual, and pause intentionally at certain points; stand still, move with purpose, maintain good posture, make eye contact
• key elements in a presentation (Ke)
• strategic framework for presentations–frame presentation around the business task; outline and connect with business metrics
• McCandless (Mc) method for presentations
• preparing for Q&A in presentations–understand and review stakeholder expectations and goals; prepare for likely questions; have an appendix with supplementary information for questions
• responding to objections–about the data, about the analysis, about the findings and recommendations

Question 8

narrative needs for a good presentation (Ne)

Answer 8

PCS RA

• characters–people affected by the story (eg stakeholders, clients)
• settings–what’s going on, how often it’s happening, what tasks are involved, other background info
• plot–what creates tension in the situation, a complication (eg challenge from competitor, inefficient process that needs to be fixed); this compels characters to act
• big reveal–how the data informs resolving the problem the characters are facing, eg becoming competitive or improving a process
• aha moment (note in McCandless this may be aka the “so what” moment, framed around actions to take)–share the recommendations and explain why you think they’ll help the company

Question 9

key elements in a presentation (Ke)

Answer 9

APSRA

• agenda
• purpose
• tell your “data story”
• make recommendations / make the pitch
• suggest actions / call to action (eg further research, or do A, B, C)

Question 10

McCandless method for presentations (Mc)

Answer 10

Gs IQIDM

• go from the general to the specific
• basic information–introduce the graphic by name, helping get the audiences attention
• answer obvious questions of the audience before their asked; start with the high level information and work to the lowest level of detail that’s useful to the audience
• state the insight(s) the data visualization provides; do this before getting into supporting details
• call out data to support that insight(s)–ie data points or specific examples / subsets
• tell the audience why it matters; the “so what” moment; present the possible business impact of the solution and clear action stakeholders can take