Data visualization tools are powerful ways to communicate insights, trends, and patterns from your data. But how do you ensure that your visualizations are accurate and reliable? In this article, you will learn some best practices and tips to keep your data quality high and avoid common pitfalls when using visualization tools.
Not all visualization tools are created equal. Some are more suitable for certain types of data, formats, and audiences than others. Before you select a tool, you should consider your data sources, goals, and intended users. For example, if you want to create interactive dashboards with real-time data, you might want to use a tool like Tableau or Power BI. If you want to create static charts and graphs with custom design, you might want to use a tool like R or Python. Choose the tool that best fits your needs and capabilities.
In general, when it comes to making static charts, I'd either use PowerPoint or Seaborn (Python). PowerPoint allows me to make charts quickly for POC. Seaborn allows me to have more customisation without sacrificing the aesthetics. For dynamic charts, I'd use either Plotly or Tableau. For non technical people, Tableau is a great start.
Looker Studio stands out as an excellent starting point for data visualization and data exploration. Its user-friendly interface allows seamless integration of Spreadsheets simplifying the creation of captivating visuals effortlessly. If the audience wants to autonomously consult data, it is easily automatizable. By easily migrating data to a BigQuery table and leveraging the Google Cloud suite to automate data collection through APIs or other becomes a seamless process. While this topic leans more towards data analysis than data science, it serves as a cornerstone for any robust data science project. Finally, it's worth noting that one-shot charts can also be generated using tools like seaborn or matplotlib in python notebooks.
Before you create any visualization, you should check your data for accuracy, completeness, and consistency. You should verify that your data sources are reliable, up-to-date, and relevant. You should also clean and preprocess your data to remove any errors, outliers, or duplicates. You can use tools like Excel, SQL, or pandas to perform data validation and manipulation tasks. Validate your data to ensure that your visualizations are based on sound and trustworthy information.
Validating data means having a robust data pipeline and quality check in place to constantly perform data validation to make sure the data source matches the schema, column names, expected vslues or even the distribution itself. One term that I recently learned for data validation is to use data contracts, which is very important when it comes to data validation to make sure the data shown on charts is accurate.
Once you have your data ready, you should use appropriate techniques to visualize it. You should choose the right type of chart or graph for your data and message. For example, if you want to compare proportions, you might use a pie chart or a bar chart. If you want to show trends over time, you might use a line chart or a scatter plot. You should also use proper scales, labels, colors, and legends to make your visualization clear and easy to understand. You can use tools like ggplot2, matplotlib, or seaborn to create and customize your visualizations. Use appropriate techniques to make your visualizations effective and meaningful.
After you create your visualization, you should test it for clarity, accuracy, and impact. You should review your visualization from different perspectives and ask yourself some questions. For example, does your visualization convey your message clearly and concisely? Does your visualization match your data and analysis? Does your visualization appeal to your audience and inspire action? You can also ask for feedback from your colleagues, clients, or users to improve your visualization. Test your visualization to make sure that it meets your objectives and expectations.
Finally, you should update your visualization regularly to keep it accurate and relevant. You should monitor your data sources and check for any changes, updates, or anomalies. You should also review your visualization and see if it needs any adjustments, enhancements, or additions. You can use tools like Shiny, Dash, or Streamlit to create dynamic and interactive visualizations that can update automatically with new data. Update your visualization to keep it fresh and engaging.
Data visualization tools are great for data science, but they require careful attention and maintenance to ensure data accuracy. By following these best practices and tips, you can create and use visualization tools with confidence and credibility.