Data visualization is a critical component of data analysis, allowing complex data sets to be understood at a glance. However, even experienced data analysts can make mistakes that lead to misleading or confusing visualizations. Here are five common mistakes and how to solve them.
Mistake: Selecting a chart type that doesn’t match the data or the story you want to tell. For example, using a pie chart to represent changes over time instead of a line chart.
Solution: A chart should be an integral part of your data story, tailored to the relevant stakeholders. Consider their background, data skills, and agenda when choosing the chart type. For instance, executives might prefer high-level insights from simple bar or pie charts, while data-savvy team members might appreciate more detailed scatter plots or histograms. Align the chart type with the story you want to tell, ensuring it effectively communicates the key message and is easily understood by your audience. The right chart not only represents data accurately but also enhances the narrative you’re building around the data.
Mistake: Including too much data or too many variables in one visualization, making it cluttered and difficult to read.
Solution: Simplify your visualizations to enhance graph speed, comfort, and safety. Focus on the key message you want to communicate and remove unnecessary elements. A well-designed graph should be quickly interpretable (speed), comfortable to view without overwhelming the audience with too much information (comfort), and clear enough to prevent misinterpretations or mitigate their consequences (safety). Use filters and highlights to draw attention to important data points, and consider creating multiple visualizations to convey additional information in a more digestible format. This approach ensures your visualizations are effective and user-friendly.
Mistake: Using colors that are too similar, too many, or that don’t convey the right message, can lead to confusion or misinterpretation of the data.
Solution: Use color strategically. Choose a color palette that is easy to distinguish and aligns with the message you want to convey. Utilize color to highlight important data points and maintain consistency in your color choices. Tools like ColorBrewer can help in selecting effective color schemes.
Mistake: Manipulating the axes of a chart (e.g., not starting at zero) to exaggerate differences or trends, which can mislead the audience.
Solution: Ensure your axes are appropriately scaled and labeled. Start axes at zero unless there’s a compelling reason not to, and make sure any deviations are clearly explained. Check that the intervals are consistent and avoid using overly large or small scales that distort the data.
Mistake: Failing to include necessary context, such as labels, legends, and annotations, which makes it difficult for viewers to understand the visualization.
Solution: Always provide clear labels and legends. Annotate your visualizations to highlight key insights and provide context. Include titles that describe what the visualization is about and add notes where necessary to explain any anomalies or important points.
Data visualization is an important component of data analytics skills, serving as the bridge between complex data analysis and actionable insights. It enables analysts to present data in a clear, concise, and visually appealing manner, making it easier for stakeholders to understand trends, patterns, and outliers. By transforming raw data into intuitive visual formats, data visualization not only enhances the interpretability of data but also drives informed decision-making and strategic planning.
School of Data Science can help organizations enhance their data analytics capabilities through comprehensive courses, including the EXIN’s Data Analytics Foundation Program.
It is a foundation-level exam and is ideal for someone just starting in data analytics and decision-making. These courses provide the necessary skills and knowledge to transform raw data into intuitive visual formats, driving informed decision-making and strategic planning.
This article has been authored by Farisch Hanoeman.
Farisch Hanoeman is the founder and director of the School of Data Science, based in The Hague, Netherlands. With a background in applied physics from TU Delft, he has been instrumental in training over 5000 individuals in data science. Farisch has led initiatives to bridge the gap between educational institutions and organizations, emphasizing practical, data-driven skills. He has also contributed to significant improvements in IT and customer journey analytics through previous ventures like Blancoblauw, collaborating with major organizations such as Schiphol and Qbuzz. Farisch is passionate about advancing data science education and its application in various sectors.