Become great at Data presentation and visualization
By Tim Wilson, Senior Analytics Director @ Search Discovery
Course length: 3h 45min
Some of the companies that train their teams at CXL:
Drive stakeholders to take action with the results of your reports and analyses
There is a dangerous belief that, because data is objective, it can speak for itself: as long as the charts and tables and slides are accurate, then the analyst’s work is done. This is analogous to stopping and going home after running 25 miles of a race and believing that you have completed a full marathon. What gets delivered in a report or as the result of an analysis, and HOW it gets delivered, is as critical in determining whether stakeholders take action with the information as the underlying analysis itself. More information is generally NOT better. Changing up the data visualizations throughout a presentation primarily just to “mix things up” is a terrible idea.
Presenting “the data” and expecting the stakeholders to draw their own conclusions is a guarantee that they will draw no conclusions at all! The great news is that there are a range of straightforward tips and techniques—grounded in some basic understanding of how the brain processes information—that can be learned and immediately put into practice to ensure that analytics deliverables are clear, understood, and retained by the business stakeholders to whom they are delivered.
Introduction video (4 minutes)
After taking this course you’ll…
- Identify the most effective data visualization for any situation so that the information is readily understandable by your target audience
- Create data visualizations that avoid pitfalls that can introduce confusion and require unnecessary effort for stakeholders to understand and internalize key takeaways
- Craft a narrative that holds the attention of key stakeholders, while also improving their ability to understand and retain the information being presented.
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This course is essential for you if …
- You are responsible for analyzing data and presenting the results to peers, executives, or other stakeholders who need to make decisions based on the information you present to them.
- You want your analyses to be regarded as some of the most valuable work conducted within the organization.
- You need the skills necessary to improve the actionability of the reports and analyses you develop, as well as the ability to teach others how to develop those same skills.
This course is NOT for you if…
- Deeply believe that the “data speaks for itself,” so how that data is packaged and presented does not matter.
- Are already deeply familiar with and actively putting into practice the data visualization and data storytelling best practices established by Edward Tufte, Stephen Few, Dona M. Wong, Nancy Duarte, Cole Nussbaumer Knaflic, and Brent Dykes.
- You are extremely adept at doing the data crunching while relying on others to determine how best to take the results of that work and communicate it effectively so that the business can actually put it to use.
Tim has been working with digital data full-time since 2001 in a variety of roles: from managing a web analytics platform migration and developing analytics processes as the head of the business intelligence department at a $500 million high tech B2B company; to creating and growing the analytics practices at multiple agencies that worked with a range of large consumer brands; to his current role consulting with the digital analytics teams at Fortune 500 companies on their strategies, processes, and tactics for effectively putting their sales, marketing, customer, and digital data to actionable use.
Your course curriculum
Data presentation and visualization
In this lesson, you will learn why data visualization, data storytelling, and effective communication are critical when it comes to effectively delivering analytics information, as well as some of the basics of how the brain works and why that matters.
- How ineffective communication of analysis results can entirely negate the impact of the analysis itself
- Why data visualization is not about simply making the data "pretty"
- The scope and definitions of "data visualization" and "data storytelling" that we will use in this course.
Three types of memory, Gestalt psychology, and examples of reducing cognitive load.
- There are three types of memory: iconic, short-term, and long-term.
- The limitations of short-term memory as illustrated by Miller's Law, and why it is critical to work within those constraints.
- What it means to reduce the cognitive load of a data visualization.
Among data visualization researchers and experts, pie (and doughnut) charts are universally reviled. In this lesson, we will explore why that is as an introduction to many of the concepts and ideas that are covered throughout the rest of this course.
- Why pie charts require the brain to do a lot of (unnecessary) work that, ultimately, make them a poor choice of visualization
- Why doughnut charts are no better than pie charts
- How there are few absolutes in data visualization (which means pie charts are, occasionally, effective)
Maximizing the data-pixel ratio sounds like an entirely esoteric and theoretical concept, but it is arguably the single data visualization technique that, once practiced and mastered, will have the greatest impact on improving the effectiveness of your data visualizations.
- What the data-pixel ratio is and why maximizing it automatically improves the effectiveness of any data visualization
- How to overhaul a chart to maximize the data-pixel ratio
- How to overhaul a table to maximize the data-pixel ratio
As computer monitor technology has progressed, we are now at the point where most monitors can display 16.7 million different colors. But, just because you have an effectively infinite variety of colors available to use, doesn't mean that you should! In this lesson, we will explore why a limited palette is a more impactful one, as well as how to go about determining that palette.
- How and why it is important to select a neutral "base" chart color
- Why the differentiation between different colors should never be required in order to interpret a chart
- How to go about establishing a palette for your data visualizations that matches your corporate palette
Axes may seem obvious and mundane, and most data visualization platforms automatically generate them. But, they can be generated poorly, and they can undermine the impact of a data visualization if they are not implemented effectively.
- Why 0-based axes are (almost) always the best option
- How to avoid too much and/or redundant information in axes
- Why dual axes make for difficult-to-interpret (and, sometimes, misleading!) charts, as well as an alternative that is generally more effective
When it comes to visualizing categorical data, the horizontal bar chart is often the best choice. In this lesson, we will explore why that is, as well as how multiple horizontal bar charts can be used to provide a clear comparison of multiple metrics across multiple categories.
- Why horizontal bar charts are so easy to interpret
- How to use multiple horizontal bar charts and how to incorporate color selectively to minimize cognitive load
- How to incorporate horizontal bar charts within data tables to make them faster and easier to digest
Line charts are one of the most common types of chart, and they are especially good at showing how a metric has changed over time. In this lesson, we will explore the key considerations when it comes to developing a line chart.
- When line charts are appropriate (and when they are not)
- What a slopegraph is and why it can be effective when there are only two points of time in the series
- How to handle line charts when the scale of the different series is drastically different
While the default approach for data visualization is "a chart," sometimes, using multiple charts together is a more effective way to convey information. That's where sparklines and small multiples come in, which are the topic for this lesson.
- What sparklines are and how and when they are most effective
- How small multiples can be used to effectively communicate a metric across multiple dimensions
- The limitations of sparklines and small multiples
Approach metrics written as text as visualizations in and of themselves.
- How standalone text can be a powerful visualization, and what goes in to ensuring that is the case
- Using text as the primary element in a data visualization in concert with a sparkline
- Considerations for text in data labels and axis labels
Heatmaps done well can be an incredibly effective way to represent a single metric across multiple dimensions in a way that enables the audience to quickly see which combinations of dimensions stand out. In this lesson, we will explore when heatmaps are most appropriate and the key considerations that go into designing them.
- For what situations heatmaps are most effective
- When to include values within the heatmap cells (and when not to)
- Important considerations for selecting the colors/gradients in a heatmap
Correlation is not causation, but correlation—the positive, negative, or lack of a relationship between two metrics—can be key to making a specific point. In this lesson, we will explore how to use scatterplots to visualize the relationship between a pair of metrics.
- How scatterplots can be used to illustrate the strength (or weakness) of the relationship between two metrics
- When and how to call out outliers on a scatterplot
- Techniques for dealing with extreme values in a scatterplot
Waterfall charts, boxplots, error bars and more: when adding cognitive load is warranted.
- How non-standard chart types increase the cognitive load for the audience for the data visualization
- Why increasing the cognitive load can be okay, as long as it is intentional
- Examples of non-standard chart types and when it is worth considering using them
Why 3D charts, stacked charts, bubble charts, and others are generally ill-advised
- How to assess a visualization to identify where it may be adding unnecessary cognitive load.
- How to experiment with alternate visualizations and assess them, too.
There are two types of dashboards. Data visualization concepts
can be applied differently depending on the type.
- The difference between performance measurement dashboards and analytical interface dashboards.
- Key considerations when developing performance measurement dashboards.
A review of the key themes throughout these lessons, and resources for learning more
- A review of the main concepts and considerations for data visualization.
- Suggested resources for learning more.
- What we haven’t yet covered: data storytelling.
"Storytelling" is not a buzzword: information presented as narrative is more easily understood and more likely to be retained.
- What data storytelling is, and how "narrative" is at its core.
- The increase in engagement, retention, and recall when information is presented with a clear narrative.
- The most common narrative arcs and how they can be applied.
Who are you ultimately targeting, how will you reach them, and what do you expect them to do with the information you present?
- Key considerations once you identify your core audience.
- The importance of starting with a desired action.
- An understanding of the different types of delivery formats and how they influence the construction of a data story.
Discovering the narrative before building out the content saves time and increases the ultimate impact.
- Why the way slide decks typically get produced lead to a meandering and ineffective finished product.
- How to develop the narrative for an analytics presentation.
- How and why storyboarding is an efficient way to develop a presentation outline.
The title of the slide is the most dominant position. Use it to maximum effect and then complement it with the content.
- What a McKinsey Title is and why it is more effective than a typical slide title.
- Why each slide in a presentation should make only a single point.
- How to ensure all of the elements of a slide complement and reinforce each other.
Decluttered slides and brain-friendly data visualizations make for high impact presentations.
- How maximizing the data-pixel ratio can be applied to slides.
- How the selective use of color can be used to maximum effect.
- The importance of including sufficient context on the slide itself.
Bullets are bad, despite their proliferation in presentations. And they are unnecessary.
- Why bullets detract from the effectiveness of slides in a presentation
- How to keep text to a minimum, while also selectively using color within text to emphasize key ideas within that text
Live presentations vs. the "leave-behind" version of the presentation vs. presentations never delivered live at all!
- What Slidedocs are (and how they differ from presentations).
- How to separate the “leave behind” from the “presented live” deck.
A picture is worth at least 100 words, if not 1,000. Images are an easy and impactful way to support a data story.
- Why imagery is so effective when it comes to increasing engagement, comprehension, and retention.
- Tips for quickly finding appropriate and relevant images.
- Techniques for placing images to make them appear seamless and professional.
Thinking about what you will say for each slide is not rehearsal. Rehearsal is rehearsal, and it is a worthwhile investment of time.
- The importance of rehearsing a data story before delivering it.
- How rehearsal of a presentation is part of the editing process.
- The importance of rehearsing out loud.
A review of the key themes throughout these lessons, and resources for learning more.
- No data story is perfect. Don’t make that the goal.
- Every presentation of analysis is an opportunity to learn and improve.
- Resources for learning more.
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