The Architecture of Clarity: Why Data Viz is a Tool, Not a Decoration

In this conversation, visual journalist Amanda Shendruk explains the principles of data visualization, noting that the most effective graphics are those that reduce the mental effort required to understand them. Her main point is that data visualization is a functional tool rather than a decorative one. Shendruk suggests that by limiting complexity and using what she calls useful redundancy, creators can guide audiences to immediate insights without losing accuracy. For the reader, this perspective turns data from a static reporting requirement into a communication asset. Those who master this approach can capture attention and build authority, turning complex information into something actionable.

The Hidden Cost of Cool Visuals

Many creators prioritize aesthetic novelty over clarity. Shendruk points to platforms like Statista as examples of infographics that pack too much data into a single, cluttered frame. This often results in a failure of communication, leaving the audience unsure of the intended message.

I do like data vis art. I do love when people make cool artistic visualizations using data, but there is a category in between where people try to take data and make it look cool rather than communicate something. I look at it and I go what do I get out of this? I do not get any information.

-- Amanda Shendruk

The goal is to treat a chart like a supply chain. Just as a chef processes raw ingredients into a ready to eat meal, a visual journalist must process raw data into a digestible format. When the audience has to work to decode the visual, the system has failed.

Why Useful Redundancy Creates Lasting Advantage

Conventional advice often suggests that brevity is the ultimate virtue. Shendruk disagrees, arguing that useful redundancy is a deliberate strategy to reduce cognitive load. By stating the main point in the headline, repeating key information in the chart, and using annotations to call out outliers, the creator ensures the audience does not have to struggle to understand the data.

This creates a competitive advantage. While others demand effort from their readers, the practitioner who builds frictionless charts earns the reader's trust. Over time, this consistency shifts audience expectations, making the creator's work a default reference for complex topics.

I often think about holding my readers hand or helping them through the chart at the annotation level. I have put the point in the headline of the chart and if you just scan this, you can see that mostly the statutory vacations for most countries are between 20 and 40. I have also colored the circles by the region. I always like when I can to have another layer.

-- Amanda Shendruk

The Systemic Risk of Black Box Data

Shendruk’s experience with a class action lawsuit against H and M, which began with her own data scraping investigation, shows the potential of data journalism. However, she warns that relying on external data requires a rigorous vetting system. If a source does not list its methodology, it is effectively a black box that introduces hidden risks to the creator's reputation.

The system responds to bad data with corrections. Shendruk recalls a time a reader identified an error in a UN or OECD dataset she had used. Her response, removing the chart and issuing a correction, shows the importance of transparency. In a system where data is the lifeline for understanding reality, maintaining the integrity of that data is more important than the temporary embarrassment of a correction.

Key Action Items

• Audit your audience's friction threshold: Stop designing for yourself. If your audience is general, remove axis lines and details that are not critical to the story. If they are experts, include error bars and uncertainty. (Immediate)
• Implement the Squint Test: During your design phase, squint at your graphic. If the main point, such as the outlier, trend, or headline, is not immediately obvious, you have too much noise. (Immediate)
• Adopt Useful Redundancy: Explicitly label your lines instead of using a legend, and include the core takeaway in the title. This increases shareability and retention. (Ongoing)
• Vet the Apple Pie Sources: Be suspicious of data provided by companies about their own products. If you cannot find the methodology, do not use the data. (Immediate)
• Build a Lifeline Repository: Start a spreadsheet of your own data findings. This creates a long term asset that can be repurposed for future newsletters, workshops, or service offerings. (12-18 months)
• Design for Black and White: Ensure your charts are readable without color. Use color only as an additional layer, not as the primary way to communicate a difference. (Immediate)