In the current data-driven world, harnessing and collecting the correct data isn’t enough. Extracting insights and applying them for your benefit or the advantage of an enterprise is essential, and more often than not, it is challenging to communicate your findings in a simple way that leaves little room for subjective interpretation. A methodology that greatly simplifies these tasks is Data Visualization. Data Visualization is the graphical representation of information and data, used mainly for understanding trends, patterns, and outliers. To apply it effectively, take into account the following best practices:
Identify your target audience: Not all users will perceive and interpret the same information in the same way. It’s necessary to tailor the content according to the profile of the people that will see it. When building a visualization, consider the audience’s expertise and background. It is important to understand how familiar they are with the content and concepts shown. Think about what questions they will have and what decisions they will make with the insights provided.
Select the proper charts: According to the purpose of each visualization, there will be specific graphs that will be best suited to represent the data.
Line graphs measure a variable’s evolution over time or its interaction between two or more variables.
Bar charts are used for measuring volume across different categories.
Scatter plots reveal the correlation between two or more variables.
Heatmaps highlight performance based on color coding.
Keep it simple: Sometimes, we consider complex visualizations with multiple graphs, several filters, and drill-down options to be better. Nevertheless, this can cause clutter, making it confusing to interpret the data and resulting in having different conclusions across users.
Use colors constructively: Colors are very effective to highlight and accentuate information, but they must be applied properly. Use intuitive colors that make sense; if you’re working with financial results use green for profit and red for losses. Using too many colors will cause distortion, while using a single color or too many shades of one color can cause the data to blend. This article contains excellent tips and examples of what color palettes to apply and when.
Provide the necessary context: it’s common to find visualizations without labels on their axes or without titles. Some people consider that avoiding them maintains a clean, minimalist design. Nonetheless, captions explain how to read the figures and provide additional precision for what cannot be graphically represented. The users won’t spend time figuring out the graph’s purpose, instead they can focus on identifying the patterns or tendencies.
If you want to start developing these skills or are looking to cultivate them to advance in your career path, remember, practice makes perfect. Start getting hands-on experience in building dashboards. If you don’t have access to data visualization tools or datasets here is a list of some resources that can be of use:
Tableau Public: is a platform of free access that will allow you to create and share visualizations online. You will also find a library of top-level dashboards, as well as sample data and introductory courses.
Power BI Community: contains a gallery of outstanding dashboards that can be used for inspiration.
Kaggle Dataset: here you can find an extensive library of datasets, from multiple categories.
Start building and let us know of any questions or issues you may encounter.
By: Alejandro Galvis