I recently participated in a panel discussion at the Sikka Software retail health data conference, and the panel was asked what are the key differences if any between data visualization and data analytics. My response was that an effective data visualization is a result of data analytics with the goal of using visual tools to be as persuasive as possible. While some data analysis tools enable creation of data visualizations and some data visualization tools have analytical capabilities, data visualization and data analytics have different goals and can frequently require different tools.
The goal of data analytics is to awaken the available insights from the data through fully understanding the available data, exploratory summary statistics, and advanced statistical models. Once that process is sufficiently completed to answer the question at hand, then the findings can be translated so that they are accessible to others. The findings become a story, and the visualizations are the pages organized in the most clear and persuasive way.
I subscribe to the thinking that if you cannot explain complex concepts simply and elegantly, you probably have not mastered them yet. Similarly, if it seems impossible to distill a complex analysis into one or two basic visualizations, you probably haven’t cracked the nut at the foundation of the business question. Through the analysis process, one finds the one or two charts that distill the core drivers of the insight. Of course, having additional, more-detailed analyses prepared to dig deeper into the story with your viewer is an excellent approach. However, that is secondary to developing a set of visualizations that clearly demonstrates the core, insightful findings of the analysis.
There is some debate on how far a visualization should go in filtering and stylizing what information is presented. Some decision makers expect the visualization to take yet another step out of the process for the viewer: interpretation. The thinking is that looking at the visualization should provide an immediate “aha” moment without requiring interpretation of the visualized data in order to come to their own conclusion. Other decision makers want to be provided with the appropriate, distilled information and to come to their conclusion autonomously. This second audience might prefer a less stylized visualization, while the former may want to see text-box cues and big arrows pointing to the takeaway.
One benefit of data visualization compared to data analysis is that it allows the analyst to focus the data story in a way they understand to be the most accurate and persuasive. Data visualization is also generally the most efficient way for business decision makers to see the scope of the value that data analytics can provide to their work.