To create the ultimate, useful, and intuitive data visual, there is a lot to know besides how to use your favourite data visualization tool. Whether you use Power BI or another tool, Data Visualization Best Practices must be applied to derive real business value from your visualizations.
The entire raison d’être for data visualization is to make it easier and faster to understand and gain insight from large volumes of data and ultimately to make the best decisions for your business. If Data Visualization Best Practices are not followed, you run the risk of developing visualizations that may look nice, but aren’t very useful. On the other hand, by adhering to these best practices, you will be able to produce visualizations that are both visually impactful and effective.
Define a process by which you obtain your design requirements, data, and design visuals, and how you release them. Only a well-defined methodology will ensure continuous quality improvement and consistent quality in your data visuals. To this end, we use UD³ an agile process that ensures all elements critical to the creation of highly effective dashboards are addressed.
Identify and prioritize who will be looking at the data? It is difficult to create a dashboard that meets the needs of every single stakeholder who might one day look at it. Next ascertain what questions they want answered. Make sure you answer the questions properly for the end user’s perspective. You also need to be aware of how much time they have available to interpret your dashboard, their technical capacity for self-service investigation within your dashboard, their familiarity with statistics, etc.
One of the single most important steps you should make for any data visualization is to define what central question it needs to answer. Next identify the KPIs that are vital to answering the central question. It’s important that answers to business questions are obvious in the visualization, and that they are driven by business goals and objectives.
What actions would you expect your end-users might take after they look at your dashboard? For example, a marketing dashboard showing where marketing activities are falling short of goals allows marketers to identify specific tactics or activities that are under performing, explore further to find out why, and take action such as redirecting funds to better performing activities.
There are three types of data: categorical, ordinal, and quantitative. Different visual features work better with different types of data. For example, scatter plots work well with two pieces of quantitative data, whereas line charts work best for date ordinal data. Conversely, line charts are a poor choice for (non-ordinal) categorical data as line charts imply continuity. Make sure you know what data your visual will be using. Here’s a brief definition of each type of data:
Categorical – data that logically belongs together, such as: North America, Europe, and Asia
Ordinal – data that logically belongs together and has a logical sequence: gold, silver, and bronze medals
Quantitative – data that defines “how much” of something there is: $1 million in sales, 20° Celsius, 150 defects
Hard science, involving decades of research, studies, and physiological measurement has defined a hierarchy of effective visual features based on the type of data being used. Learn more about visual features here: Optimize Data Visualizations with the Right Visual Features.
Don’t wait until your requirements are 100% understood before beginning the design process. Get a “good” understanding of the requirements then start designing proofs of concept and prototypes to elicit feedback in an interactive setting. This way visualizations are revised with direct end-user interaction. This avoids “analysis paralysis” which tends to happen with older project management approaches.