Don’t believe everything you see — it’s one of those age-old bits of wisdom that can serve us well in many areas of life, including the visualization of your business mapping data. Data visualization can be an invaluable analytical and strategic planning tool for just about any organization. But, as with any tool, it is vital to use data visualization in an appropriate manner.
In this post, we will take a close look at the various ways that data visualization could be misleading, misinforming or even deceiving. We will also provide tips on how you can avoid the pitfalls that so often lead to misinformation when interpreting business data.
Data visualization generally refers to any activity designed to help individuals gain a better understanding of the significance of specific data by placing it in a visual context. Another way to look at it is that data visualization provides a means of bringing dry, one-dimensional pieces of information to life. Mapping is one of the most common — and effective — data visualization tools for business owners and managers, as it allows them to obtain a fresh, clear perspective that is likely to elude them when viewing rows and columns of data on a spreadsheet printout or computer screen.
Organizations in a wide range of industries use data visualization as a regular component of their strategic planning activities. There are many industries that derive huge benefits from the vast array of business mapping software features available such as performing comprehensive market analyses for multiple locations, designing profitable, cost-effective sales territories, route optimization and creating powerful reports and presentations, all through the use of maps. These map enabled industries include retail, banking and finance, insurance, healthcare and consumer goods manufacturing — to name a few.
Data visualization is an invaluable resource for analyzing your current customers, prospects and competitors. Used effectively, it will help you maximize your sales opportunities and identify potential areas of vulnerability. How can you use data visualization to improve organizational performance? Here is a brief summary of the many benefits derived when visualizing your data with business maps:
Creating a map with the advanced visualization features you need to maximize the data visualization process is easy with the help of business mapping software. It’s a relatively simple five-step process:
There’s no doubt that data visualization has a powerful impact on the audience, whether it consists of a group of colleagues, customers or prospects. However, this can be somewhat of a double-edged sword. The same characteristics and benefits that make visualization such an effective tool can also lead to what is known as “data malpractice.” In other words, it is easy to be misleading with your data — in both intentional and unintentional ways. Thus, the distortion of data can have intended or unintended consequences for your audience and your organization.
By its very nature, data visualization is the embellishment of information. One could make a comparison between data visualization and advertising. The latter often entails the “enhancement” of the features and benefits of a product or service with the use of persuasive marketing tactics — think of a glossy magazine picture that makes a food product look irresistibly appetizing or a weight loss commercial that promises to make women look like swimsuit models if they follow a specific diet plan.
Data visualization that uses mapping as a vehicle for presenting data can have a similar effect. Mapping software gives the user the flexibility to “manipulate” the data to present information in the most favorable light. As we will see later, the method in which a graph, chart or map is created can have a major influence on the way an audience interprets the data — for better or for worse.
Misinformation with data visualization can occur in three ways:
There is a wide range of data visualization techniques that can misinform and even mislead the audience. Examples include:
1. Creating Non-Zero Baselines
The typical line graph or bar chart consists of two lines that form a right angle and establish the data coordinate plane. The x-axis represents the horizontal line across the bottom, while the y-axis is the name for the vertical line that extends in an upward direction from the x-axis. One common “trick” is to create a y-axis on a bar chart that starts with a value that is something greater than zero. This has the effect of skewing the visual comparison in a way that improperly emphasizes the difference between the bars, which can lead to misinterpretation on the part of the viewer. This can be accomplished on a map too. For instance, in a demographic population map color coding only zip codes that have at least 500 people living there.
This is a classic example of how creating a non-zero baseline can alter one’s perception of the data. The bar graph on the left uses a y-axis starting point of 3.14 percent, making it appear that interest rates have soared over a four-year period. The graph on the right uses a starting point of zero for the y-axis, which paints a more accurate picture of how interest rates have actually remained relatively flat over time.
2. Misleading Colors
One of the more popular features of mapping software is the ability to create heat maps and similar vehicles, where different colors are used to distinguish between individual values. How these colors are arranged on a map can have a direct impact on how an audience interprets the values.
For instance, using an abrupt contrast in colors, such as going from a dark shade of blue to a light yellow, can make a viewer believe there is a more dramatic change in the values than actually exists. Conversely, a map that displays little in the way of color contrast can give the impression that there is very little difference between the map values, when in fact the very opposite may be true.
3. Graphs That Don’t Tell the Whole Story
It is possible to use data visualization in a way that only tells a portion of what is really occurring. A prime example is when using data to create a cumulative graph to show growth over time. For instance, a graph that is nothing more than a compilation of a company’s revenues over a period of years will not indicate whether the revenues are increasing or decreasing from one year to the next. Unless the audience takes the time to closely scrutinize the data, audience members may believe that the company’s revenues are increasing, when they may actually be in a steady freefall.
This example shows how misleading using a graph showing cumulative data can be. The cumulative graph on the left indicates the sum of the company’s revenues from 2004 to 2014, which makes it appear that the organization has experienced constant growth over a period of a decade. But when you consider that a company’s cumulative revenues can do nothing but increase — assuming it stays in business and generates any sales at all — this graph really provides little in the way of meaningful information.
The graph on the right depicts the actual amount of revenue generated each year, instead of on a cumulative basis. It illustrates that the revenues have been in gradual decline over the past several years, a more truthful and precise measurement of the company’s recent and long-term performance.
4. Deviating From Standard Practices
There are certain standard practices that apply to using mapping for data visualization. Deviating from these practices can render the chart or graph virtually meaningless. For example, the typical pie chart includes segments or “slices” of data with assigned values that should add up to 100 percent.
During the 2012 GOP presidential primaries, however, the Fox News Chicago affiliate displayed a pie chart where the values of the segments regarding the voters’ preferences of the three main Republican candidates totaled well over 100 percent (poll respondents were permitted to make multiple choices). When displayed as a pie chart, viewers were led to believe that each candidate had garnered about one-third of voter support, when this was actually not the case at all.
5. Improper Scaling Methods
One issue with displaying images on a bar chart or graph is that the dimensions of the images may be scaled in a disproportionate manner, which can be misleading to the viewer. If one image is meant to represent a value that is twice that of another image, but the image itself is actually four times larger, it fails to give the viewer a true indication of the actual relationship between the two. Thus, the viewer may interpret the data as if that one company is generating four times the sales, profits or market penetration than the other, when the actual value is only two times as much.
From an ethics and accuracy standpoint, most businesses wish to avoid misleading with their data, whether intentionally or unintentionally. Displaying a map, chart or graph containing information that deceives a customer is a sure way to lose business and give your company a less-than-favorable reputation throughout your industry. Just as important, relying on skewed data will likely lead you to make poor decisions that negatively impact your company’s performance — not to mention your bottom line.
Tips to help you use data visualization in an accurate and ethical manner include:
If you are a regular consumer of data visualization, it is important to take a proactive approach when reviewing maps, charts and graphs that have been prepared by others. Closely scrutinize the actual data, instead of only the visual component of the display. If the information doesn’t make sense, ask the presenter for clarification or ask a colleague for their interpretation. In other words, don’t believe everything you see!
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