Misleading visualization of data. The importance of representing analytics correctly.

If you prefer attractive images over accuracy, then visualizing the data can be deceptive. In order to reliably provide analytics and not mislead users, it is worth avoiding common data visualization errors.

Data visualization can serve as a critical tool for learning and communicating complex information, and can confuse, distort, or misrepresent data. Misleading visualization of data can be intentional if the creator has a plan to advance. Or this can happen due to errors, misunderstanding of the data or the visualization process. Whatever the reason, data visualization that misleads the user is not allowed in analytics.

What is data visualization?

Data visualization can be seen as a powerful communication tool that helps in data analytics by converting raw data and numerical values into aesthetically appealing visuals. This helps to present complex information in a logical, simple, understandable and interactive way. Thus, data visualization is also a form of data organization in convincing and digestible forms.

Data visualization types

Consider methods or forms in which data can be presented:

  • histogram: usually used to compare categories and groups;
  • line graph: used to display trends;
  • scatter plot: suitable for highlighting the values of two different variables as points in the diagram;
  • the following area chart: very similar to a linear diagram; the only difference is that the area below the line is shaded with a certain color;
  • indicator: required to indicate the direction of movement of objects;
  • map: used to indicate the geographical distribution of data;
  • matrix: used to highlight the relationship between multiple variables and data points;
  • summary table: convenient for summarizing a large amount of data (highlights important information);
  • box plot: useful for highlighting a data distributor.

Errors to avoid when visualizing data

Misleading color contrast

Color is one of the most convincing design elements. Even minor variations in shades cause strong emotional reactions. When visualizing data, a high degree of color contrast can lead viewers to believe that the differences in values are greater than they actually are.

Incorrect use of 3D graphics

3D visualizations of data are interesting and fascinating, but creating them may not be worth the effort. It is almost impossible to trace the height of each column to the correct Y value on a multi-level histogram. Moreover, it will not be possible to see the values of columns hidden behind more visible columns. If these indicators are not needed, you just need to exclude them from the data visualization. If they are critical, use a simple histogram instead of a 3D histogram. It's also important to remember that adjusting all 3D views and colors takes more time and money than creating a 2D visualization.

Too many details

The point of creating a data visualization is to tell a story. Thus, it is necessary to include as much relevant information as possible, while excluding irrelevant or unnecessary parts. This ensures that the audience pays attention to the most important data.

For this reason, when creating a data visualization, you must first try to determine the required variables. The number of variables selected will determine the visualization format.

To exclude a baseline and truncate a scale

The data is always different. Sometimes their difference is significant, for example, when measuring income levels or voting habits depending on the geographical region. In an effort to make visualization more dramatic or aesthetically pleasing, designers manipulate scale values on graphs.

A typical example is skipping the baseline or setting the start of the Y axis somewhere above zero to make the differences in data more noticeable.

Another example is truncating the value of X in a data series to look comparable to those with lower values.

Biased textual descriptions

The text that accompanies visualization (auxiliary text, headings, labels, captions) is intended to give viewers an objective context rather than manipulate their perception of data.

Selecting the wrong visualization method

Each data visualization method has its own use cases. For example, pie charts are used to compare different parts of an integer. They are well suited for budget breakdown and survey results, but are not designed to compare individual datasets.

A pie chart can be used to visualize the revenues of three competing businesses, but a bar chart will make the differences or similarities between businesses more apparent. If the visualization was intended to display income over time, then a linear chart would be a better option than a histogram.

Violation of generally accepted standards

Another common trick is not to follow the well-established rules and norms of information presentation. For example, everyone is used to the fact that circular graphs are used to represent parts of an integer, or that on time lines with progress, movement goes from left to right. Therefore, when such rules are violated, it is difficult for people to navigate, and they may not understand what exactly they are shown by misinterpreting the data presented.

Impact of bad data visualization

While an inaccurate chart may seem like a small mistake in your organization's overall scheme, it can have serious consequences.

As a last resort, an improperly formatted chart or graph can lead to legal or regulatory problems. For example, a misleading visualization of data included in a financial statement could force investors to buy or sell shares in a company.

By nature, inaccurate data visualizations result in your audience having an inaccurate understanding of the data that is presented in them. This misunderstanding can lead to erroneous conclusions and incorrect business decisions.

For these reasons, a solid understanding of data science is an important skill for professionals. Knowing when data is accurate and complete and the ability to detect discrepancies between numbers and any visualizations created from them is imperative in today's business environment.

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