Improved Insight: Data visualization can provide insight that traditional descriptive statistics cannot. A perfect example of this is Anscombe’s Quartet, created by Francis Anscombe in 1973. The illustration includes four different datasets with almost identical variance, mean, correlation between X and Y coordinates, and linear regression lines. However, the patterns are clearly different when plotted on a graph. Below, you can see a linear regression model would apply to graphs one and three, but a polynomial regression model would be ideal for graph two. This illustration highlights why it’s important to visualize data and not just rely on descriptive statistics.

Faster Decision Making : Companies who can gather and quickly act on their data will be more competitive in the marketplace because they can make informed decisions sooner than the competition. Speed is key, and data visualization aides in the understanding of vast quantities of data by applying visual representations to the data. This visualization layer typically sits on top of a data warehouse or data lake and allows users to discover and explore data in a self-service manner. Not only does this spur creativity, but it reduces the need for IT to allocate resources to continually build new models.