At the same time, what is often overlooked is that these charts, graphs, and other visuals can be merely decorations if users are not data literate. Based on the research on data literacy, this article offers a critique of the overabundance of data visualizations and dashboards, talks about when to use them appropriately, and sketches some guidelines for identifying when an alternative is justified.
To better gauge people’s understanding of data visualizations, it’s essential to define the concepts of numeracy and data literacy.
Numeracy is understanding, calculating, manipulating, interpreting results, and communicating mathematical information. It involves reasoning, critical thinking, analyzing numerical data, and making informed decisions based on quantitative information. Such seemingly everyday concepts as date, chance, patterns, relationships, dimensions, shapes, numbers, and change form the core of numeracy comprehension. Importantly, numeracy lays the groundwork for data literacy, which builds to data visualization literacy.
Data literacy is often interpreted as a sub-type of numeracy and can be summarized as the ability to read, analyze, work, and argue with data. Visualizations present many difficulties for people working with data, and proficiency in such data manipulations has been coined as data visualization literacy.
Together, numeracy, data literacy, and data visualization literacy form a spectrum in which it may be difficult to separate where one type of literacy begins and another one ends. However, without basic numeracy literacy, the individual cannot be data-literate, and data visualizations are reduced to decorative images.
With the definitions and concepts of data visualizations explained, let’s now explore the challenging aspects of working with data, which can be summarized as follows:
- Data is often messy and disorganized. Data comes from various sources and requires additional cleaning, verification, and organization steps. These steps require at least some critical thinking and an ability to sort, filter, and standardize the data.
- Data can be biased or incomplete. Humans are data’s primary creators and inspectors, so its results may be inaccurate or missing values.
- Data can be challenging to understand. Data visualizations, statistical measurements, tables, and spreadsheets all represent a variety of formats. Different formats influence the likelihood of arriving at a particular conclusion, the correct interpretation of which requires a high degree of critical thinking.
- Data can be challenging to communicate. More often than not, presenters rely on the visuals of data rather than describing its meaning and implications.
Reliance on data presentations, such as visualization, is usually based on the assumption that they are more intuitive. However, research shows that not all data visualizations are comprehensible. In one data visualization familiarity study, the type of visualization affected the comprehension of information: after much experimentation, it was concluded that both youth and adults are capable of interpreting only the most basic reference systems, such as charts and graphs (e.g., pie charts, bar graphs, and scatter plots). Maps and network layouts were demonstrated to be the least comprehensible. In another study, significantly more participants could correctly identify a value on a chart than make inferences between two points. An additional study found that even fewer people could locate the trends demonstrated on a line graph, suggesting such graphs may pose a more significant challenge depending on the audience.
Overall, different visualization types require different amounts of cognitive capacity. Bar graphs are associated with lower cognitive effort for comprehension, whereas network graphs, generally infrequent, can be too convoluted, requiring users to observe too many relations to understand. The table below provides examples of data visualization types and summarizes their use, ordered by ease of comprehension.
While data visualization may initially seem effective for conveying a point, the inherent complexity of data and the cognitive burden of data visualizations prevent users from complete comprehension. Beyond UI impact, we see low data literacy tendencies around the world, which on their own significantly impact businesses and economies.
Numeracy, fundamental to data literacy, shows many challenges in proficiency and acquisition, including for many people in the US. It is estimated that less than 10% of the general American population aged 16–65 are proficient in understanding numbers. On average, Americans’ numeracy proficiency is rated at a level 2 out of 5, which includes performing basic calculations and interpreting simple tables and graphs. The US ranks at the bottom of the list compared to other developed world countries. Because numeracy and data literacy are interconnected, researchers observe that low numeracy rates also spawn low data literacy rates influencing many professional domains.