How to Use Data Visualization to Communicate Complex Findings
Informatics Magazine
How to Use Data Visualization to Communicate Complex Findings
Unraveling complex data can be a daunting task, but with the right tools and techniques, clarity emerges. This article demystifies the process, showcasing how data visualization can transform intricate findings into accessible insights. Drawing on the wisdom of seasoned professionals, it delivers practical guidance for deploying visuals that speak volumes.
- I Used Clear, Simple Visuals
- I Created Intuitive Power BI Dashboards
- I Simplified Data With Visual Comparisons
I Used Clear, Simple Visuals
I once worked on a project where we needed to present complex healthcare data to hospital administrators. To make the information accessible, I used a combination of bar charts, heat maps, and interactive dashboards. These visual tools highlighted key trends and outliers, making it easier for the audience to grasp the critical insights without getting bogged down in technical details. What made this approach effective was the use of clear, simple visuals paired with concise explanations, ensuring that the audience could quickly understand and act on the findings. Additionally, I incorporated real-world scenarios to illustrate the data's impact, which helped to engage the audience and make the information more relatable.
I Created Intuitive Power BI Dashboards
From my experience, stakeholders who lack a strong background in data often need clear and straightforward data visualizations. When data is presented effectively, with intuitive graphs, stakeholders can easily grasp the analysis. Conversely, if the visuals are cluttered or overly complex, they may feel overwhelmed and confused.
As a consultant, I am frequently hired to create Power BI dashboards. My clients typically aren't tech-savvy, so they rely on me for both technical and analytical expertise. To ensure my work meets their needs, I follow several key principles:
Keep graphs simple - Familiar visuals like bar charts are universally understood, making them highly effective. On the other hand, complex visuals like Sankey diagrams may confuse non-technical stakeholders unfamiliar with interpreting them.
Eliminate clutter - Graphs should be as clean and streamlined as possible. Remove unnecessary elements, such as axis titles or labels, if they don't add value. Minimizing extraneous text helps the audience focus on the key information.
Use color intentionally - Leverage natural color associations, like green for positive outcomes and red for negative ones. Strategic use of color can reinforce your message and make the visuals more intuitive.
Maintain consistency - Consistent use of colors across all visuals helps build familiarity. For example, if revenue is always represented in green and expenses in orange, your audience can quickly interpret the data without needing to decipher it repeatedly.
I Simplified Data With Visual Comparisons
At Tech Advisors, I once worked with a healthcare organization struggling to understand the risks posed by outdated systems. The data we collected included hundreds of vulnerabilities across their network, and presenting it as raw numbers would have been overwhelming. To simplify it, we used a bar chart to compare the severity of vulnerabilities in each department. This visual immediately highlighted which areas needed the most attention, helping leadership prioritize updates without needing a deep technical background.
We also used a heat map to show risk levels across different locations. The colors on the map ranged from green to red, making it easy for non-technical staff to see where issues were most critical. The simplicity of the visual allowed us to explain complex cybersecurity concerns in minutes, leading to faster decisions and actionable outcomes.
When presenting data to non-technical audiences, it's important to keep visuals clear and focused. Avoid clutter and use labels that are easy to understand. Tools like pie charts or line graphs can also help highlight trends or show progress over time. The goal should always be to make the data relatable and actionable, ensuring everyone at the table feels confident about the decisions ahead.
