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High-quality infographics are 30x more likely to be read than plain text. The Wharton School of Business found that while only half of an audience was convinced by a purely verbal presentation, that number jumped to over two-thirds when visuals were added. Other sources attribute the statistic to a study by the University of Minnesota, but this study found that the brain processes visual information 60,000 times faster than text. So there is a lot to think about regarding data visuals. As I mentioned in my previous article, I wanted to dive deeper into each principle I listed. We must support that with relevant but straightforward examples. It should incentivize all of us to follow the principles.
Right data visualization refers to the strategic selection and presentation of data using visual elements to communicate insights, patterns, and trends effectively. It involves carefully considering the most relevant and meaningful data and choosing appropriate chart types, configurations, and design principles. Right data visualization aims to enhance understanding and decision-making by presenting information in a clear, intuitive, and impactful manner tailored to the specific audience and objectives of the data story. It encompasses the use of contextually appropriate data, thoughtful design, and eliminating noise or unnecessary distractions, ensuring that the visualization conveys the intended message accurately and facilitates meaningful interpretation by the audience.
I'll explain the principles through three (3) examples that will give you sufficient guidance before your next analysis. Oh, I almost forgot. If you are a manager or an executive, ensure your analyst has time to consider these things. Often it needs to be noticed that to provide these alternative visuals. You need to play with them and figure out what needs to be added before the analyst formulates the recommendations. Just assume they will know only some of the data and visuals before they start working on the analysis. Nobody does, and it takes a moment before you arrive with something worth reviewing. Otherwise, this is just a report, not an analysis.
Example 1:
Repurposing charts without exploring different data views may hinder clarity for the audience.
Adjusting the underlying data can enhance the chart's impact by expressing insights through different metrics or ratios.
An analyst presents a bar chart showing total sales by region. However, upon further analysis, they realized that expressing sales per capita would provide more meaningful insight into regional buying power and market potential.
Imagine an analyst working for a fintech company that provides financial services to customers across different regions. The analyst is tasked with visualizing sales data to gain insights into market potential. Initially, they create a bar chart showing the total sales by region, which provides a general overview of sales performance. However, upon deeper analysis, the analyst realizes that expressing sales per capita would offer a more insightful perspective into regional buying power and market potential.
By calculating sales per capita (total sales divided by the population of each region), the analyst can create a new visualization highlighting the relative purchasing power of different regions. This approach helps the company identify regions with higher sales per capita, indicating more substantial market potential and opportunities for targeted expansion.
This example illustrates how the data selection process is dynamic. You start with one idea that aligns with one chart. Still, eventually, you end up with an additional data point in your data set to reference underlying data in each region. Although this example is hypothetical, considering sales per capita instead of total sales can provide more meaningful insights into regional buying power and market potential. It sounds obvious (or perhaps unnecessary), but it becomes precious information if a company needs to make informed decisions about expansion strategies and resource allocation.
Example 2:
Dual y-axis bar charts with different scales can obscure significant trends, like the disparity between revenue and customer growth.
Utilizing percent change or calculated metrics can facilitate better comparisons and highlight key points.
A line chart shows the growth of website visitors and revenue over time. However, due to the different y-axis scales, the revenue growth appears minimal compared to the visitor growth. Adjusting the chart to showcase the percentage change in revenue and visitors would more accurately represent the business's performance.
To make this example more tangible, let's consider offering online products and services products and services online. The analyst tracks monthly website visitors and revenue generated from policy sales. Initially, the line chart shows a steady increase in website visitors and gradual revenue growth. Still, the scale difference makes it difficult to evaluate revenue performance.
The analyst modifies the chart to present the revenue and visitor numbers percentage change to address this. By doing so, they create a new visualization that accurately reflects revenue and visitor traffic growth rates on a comparable scale. This adjustment reveals that while the visitor numbers show significant growth, the percentage change in revenue outpaces the growth in visitors, indicating a positive revenue trend and a successful monetization strategy for the insurance company.
Again, these examples are purely hypothetical. We may have zero correlation between these two data points or no causality. By adjusting the line chart to showcase the percentage change in revenue and visitors, the analyst enables a more meaningful interpretation of the company's growth trends and aligns the visual representation with the underlying business performance. You might not get all the answers, but you can spark the proper conversation. And, sometimes, that's what it is all about.
Example 3:
Comparing trends between different years or scenarios with the same data can provide new perspectives.
Experimenting with visual variants and emphasizing deviations or variances can enhance clarity and audience understanding.
Comparing customer satisfaction scores between different regions in a stacked bar chart can be improved by including a benchmark line to provide context and show how each region performs relative to the overall average satisfaction score.
Let's bring this example to life with a real-world scenario. Consider a healthcare company that offers services across different regions, such as hospitals and medical clinics. The analyst collects customer satisfaction scores from post-service surveys and aims to visualize and compare these scores between regions. Initially, they created a stacked bar chart displaying satisfaction scores for each region. Still, it needs more context and makes it easier to assess regional performance than the company's overall average satisfaction.
The analyst introduces a benchmark line in the chart to address this, representing the company-wide average satisfaction score. This line serves as a reference point for evaluating regional performance. Regions above the benchmark line indicate above-average satisfaction, while regions below the line suggest room for improvement. By including this benchmark line, the analyst provides valuable context and enables stakeholders to assess how each region performs relative to the company's overall customer satisfaction. Adding another perspective of changes year over year for both company's performance and benchmark changes gives an idea of how much the company improved vs. the market.
The example illustrates the importance of thoughtful data visualization in the healthcare industry. It helps management make faster resource allocation decisions or identify future risks. Again, simple but telling.
Conclusion: The evidence is clear that high-quality infographics are 30 times more likely to be read than plain text. It means that visuals significantly increase audience persuasion and information processing speed.
Encourage your analysts and executives to consider different data views, utilize calculated metrics or percent change, and experiment with visual variants that emphasize deviations or variances. These simple adjustments will help you to discover new ways of looking at your company's performance. By doing so, you can ensure that the visuals align with the underlying data and accurately convey the intended message (yes, you can also improve the data quality that could be better - always).
Remember, data visualization is not just about creating a report but about facilitating meaningful analysis and interpretation. Following these straightforward examples can empower your team to create impactful visualizations that drive informed decision-making, resource allocation, and risk management.
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