The Rise of Infographics in Presenting Data Analysis

In an earlier post we talked about the difference between graphics used for visualization of data points and graphics used for presentation.  We concluded that the point of an analyst’s effort when analyzing survey data was to communicate the results to busy decision makers in a format they could understand.

Enter The Infographic

Using information graphics to convey an idea or meaning has been around since the earliest cave paintings. Today, infographics are an essential part of the survey analysts tool box because they convey complex data in an easy to follow and visually appealing format.

From blog posts and web articles to glossy brochures and of course, data analysis presentation, infographics are a ubiquitous part of the information landscape. But why have they become so prevalent?

Infographics Are Easier Than Ever To Create

Modern computers and sophisticated software can easily render thousands, even millions, of data points into a visual representation, often with nothing more than a mouse click.  What used to take hours to create by hand (and yes, most graphics used to be done by hand, I’m talking 1980s and 90s, not the 1800s!) can now be done as a matter of course.

Decision Makers Have Faster Access To More Data than Ever Before

The trend toward greater use of infographics results in part from the speed at which information is available to decision makers. The Internet and the World Wide Web have transformed not only how we receive our information but how fast we have access to it.  Also, our expectations regarding how much information we are willing to absorb has changed.  When was the last time anyone picked up a 2000 page reference book and actually read it?

It’s Easier To Look At An Infographic Than It Is To Read About The Same Thing

Today data and information comes at us in packets.  This blog post is an excellent example.  It’s short, concise, and to the point.  The title and sub headings tell you most of what you want to know regarding the topic and they provide key information you might need to justify a decision to use more infographics in your next data analysis presentation.  The rest of these words are written to support the headings but the important information might’ve been rendered visually rather than in prose.  If it were, you might have spent half the time absorbing the information.

Now, if I could present this post using only an infographic…

The Business Impact of Data Visualization

What is Data Visualization?

Data visualization is the study of data being communicated visually through graphical means. Bar charts, graphs and maps are illustrations of basic data visualizations that have been utilized for quite a few years. Over time, visualizations have become more intricate to the extent of having animations that illustrate changing data over a period of time. There is virtually no limit to the translation of information into an image.

As the visualization designer, you can ascertain which visual components, such as color and shape, symbolize and communicate particular data points.  For example, images could be 2D or 3D, dynamic or fixed, or more importantly they could allow user interaction.

Visualizations provide a unique way to communicate trends and correlations that can ultimately lead to important marketing breakthroughs. Visualizations that represent a large amount of information allow you to see patterns that may normally be hidden in unconnected data sets.

Using Data Visualization to Immediately Observe Key Points

Data visualization does not occur in isolation. Therefore it is important that you understand the context that you are communicating. When planning your communication, make sure it meets these key points:

  1. Precision. Could it be accurate with regard to proper quantitative assessment?
  2. Versatility. Can the creation conform to assist numerous wants?
  3. Aesthetically Pleasing. The data visualization must not insult the reader’s senses; a great example of this is moire patterns.
  4. Efficient.  Is your visualization simple to understand?  Will the viewer “get it” with ease?
  5. Effective. You need to be sure you display the information; evaluate regardless of whether you need to increase or even reduce data-ink percentage; additionally think about brase non-data-ink as well as brase repetitive data-ink.

The Importance of Making Data More Usable

In recent years, a variety of methods have been proposed for analyzing the consequences of data effectiveness.  During a study conducted by the University of Texas, researchers found that if a company increased the accessibility of data for the user, by only 10%, the result could increase revenue drastically and create new jobs. The researchers analyzed accessibility, mobility, quality, and usability to come up with these findings. Overall, improving your company’s data usability will likely benefit business and should not be overlooked.

Correspondence Analysis – what does it all mean?

One of the most commonly used forms of Brand mapping is correspondence analysis. Brand maps are often used to illustrate customers’ images of the market by placing products and attributes together on a map together on a graph. This allows close interpretation of company perceptions with a variety of product and service attributes that are closest to them on the map. If products are placed close to each other on the graph, it means they have a similar image or profile in the market. This helps to inform strategy, like where to take the client’s product in the future.

Some questions that can be answered with a correspondence analysis are:

- What attributes does the brand own?
- What attributes do competitors own?
- Are there gaps in the market that may be filled by the client brand?
- How should the brand be positioned to be both relevant to the market and differentiated from the competition?

Correspondence analysis is an exploratory data analytic technique that allows rows and columns of a cross-tabulation (two-way and multi-way) to be displayed as points in two-dimensional space. The information from the cross-tab, as seen in the example below, is usually collected using simple multi-coded grid questions or semantic rating scales (typically 5 or 10 point scales).

It reduces a complicated set of data to a graphical display that is immediately and easily interpretable. The graph contains a point for each row and each column of the cross-tab. Rows with similar patterns of counts produce points that are close together, and columns with similar patterns of counts also produce points close together.

Running a correspondence analysis on the above cross-tab yields the following graphical display:

In this example, McDonald’s is more synonymous with Low Prices. Pizza Hut and El Pollo Loco can be associated with Overall satisfaction, as well as Taste of the Food and Quality of Food for the Money.

Correspondence analysis is a valuable tool that can be applied to many situations.

Survey data analysis – using graphic visualization as a tool

We’d previously reflected on the opposing roles of visualization and presentation graphics; let’s now examine how visualization graphics can help us analyze survey data.

Visualization graphics can be used to quickly identify outliers within large quantities of data. Our brains are wired to recognize graphic differences in shape, magnitude, and direction more readily than we can recognize the equivalent differences within a table of numbers.

“Outliers” occur when the data visually rises above or below the average or the “noise” within the results. Outliers can serve as the source of the stories that an analyst constructs to offer understanding and explanation of the survey results. As an analyst you should be asking yourself “why?” when you observe an outlier.

The more data you include within your visualization, the greater your odds of observing outliers. There is nothing wrong with creating a visual “rats nest” of lines or bars as part of the visual analysis. Your objective is to skim the edges of the data, ignoring the bulk of data that represents the average; you are visually filtering out the noise to identify the data observations that stand out from the fray.

The radar chart below is a good example of a visualization of a large number of data points (156) summarizing hundreds of thousands of survey responses. Using this radar chart format, we can identify interesting outliers at a glance, much more conveniently than we could by studying a table or even a bar chart representation of this same data.

Here we are observing purchase decision importance survey questions results between six different brands rated by the survey respondents.

At a glance we note the red line, represented of Brand E, displays considerable lower ratings than all other brands (i.e. “the noise”) for the respondent’s consideration of manufacturer’s reputation, prestige of the product, prior experience with the manufacturer and technical innovations. Brand E may be a relatively new brand in the marketplace as purchasers did not consider reputation, prior experience or prestige as important criteria within their decision process.

Alternatively, the blue line represented by Brand A sits above the noise for attribute fun to drive, but below the noise for attributes seating capacity and cargo space. Brand A may represent a manufacturer of sporty products that emphasize fun over practicality.

Using this visualization, we have quickly identified outliers and have constructed hypothesis that we can then test and explore with our survey analysis drill down tools.

In future posts, we will illustrate how to summarize the information we’ve gleaned from our visualization into a presentation graphic display, allowing us to communicate the story of our data to our customers.

Visualization techniques – graphic visualization vs. presentation

You’ve just completed fielding your latest survey research project, now you need to analyze the data and communicate the results.  As a complement to cross tabulation and statistical analysis of the survey data, you’ll likely incorporate graphics within your analysis and reporting process.

In fact, you should consider incorporating graphics in two different ways; using graphics as a visualization tool to assist with your analysis, and graphics as a presentation tool to clearly communicate the results of your analysis.

These two separate functions or “graphical roles” require different types of graphics as well as different methods of viewing or analyzing the graphics.

Graphic of my LinkedIn network – useful as a visualization tool, but not as a presentation tool.

Visualization graphics typically incorporate a relatively large number of data points, enabling the analyst to view respondent segments, trends or outliers in a manner that may not be obvious from the viewpoint of tabular reports.

Presentation on the other hand, embodies the the art of communication, and presentation graphics are therefore designed to quickly and persuasively depict a key point or conclusion from the analysis of the survey results

Visualization graphics are typically “noisy”, containing lots of information and capturing several dimensions including multiple axes, quadrants, or variations of text or data point sizes.  Visualization graphics typically require careful study and consideration to identify their underlying meaning.

On the other hand, presentation graphics need to immediately convey the point that the graphic is making.  They are purposefully succinct and focused, and as such presentation graphics typically avoid multiple dimensions.  Good presentation graphics preclude the need for supporting explanatory text to convey their message.

The experience analyst will be thinking of presentation graphics at every step of the data analysis process, including while utilizing visualization graphics as an analysis tool.  The point of the analyst’s effort is to ultimately communicate the results of the analysis to busy decision makers in a manner they can readily comprehend.

Stay tuned to this blog as we explore new and interesting ways to use graphics to both analyze and present the results of survey data.

If you’d like to learn more about how to prepare survey data for analysis, please download our whitepaper  “10 essential prerequisites for survey data analysis”.