Data is the driving force behind businesses across any industry in this era of digital transformation. While analysts are able to understand these datasets inside and out, this information needs to be simplified for some viewers to understand the true insights being delivered by this data. Beyond the usual bar chart or pie graph, there are scatter charts. Also known as scatter plots, this is one of the greatest assets in the data visualization arsenal to benefit a company’s decision-making and business processes.
Understanding Scatter Plots
A scatter plot is a chart that shows the relationship between two variables. This is an incredibly powerful chart type that allows viewers to immediately understand a trend that would be impossible to see in other formats. The origin of scatterplots dates back to 17th-century mathematician Rene Descartes, using these charts in scientific journals and other publications to afford an easy understanding of new innovations to the masses. Scatter charts are more versatile and useful, taking confusing datasets of all volumes and trying to make sense of it.
A scatterplot has an X-axis and a Y-axis. The X is the horizontal line, associated with an independent variable in research, while the Y is the vertical line and the dependent variable in the query. An even scale is created for these axes, with a dot used to make a point to represent intersections within these coordinates. There are other patterns that can be found within scatter charts, such as a linear correlation through data points, or a non-linear correlation that can show a curved relationship. These charts also point out strong correlations based on how close dots are together to demonstrate the pattern in these categorical values.
Using Scatter Charts
Scatter charts are a great way for identifying unique instances and anomalies within these datasets. The resulting plot will uncover what is the outlier within a large volume of numeric values. Scatterplots also help to see how one variable affects another, identifying correlations, patterns, and other relationships. This clear understanding of the data and the layout demonstrates the value of the data. This clear visualization of information is able to take on a range of data values to make the scatter clearer than ever.
By clarifying variables in this query, scatter charts are able to provide clarity into relationships that are crucial to business processes. For example, a retailer may want to spot seasonal trends between sales volumes based on certain variables. This could be based on weather, proximity to holidays, or even just the availability of a product through the retailer’s supply chain. In order to clearly show these relationships and trends, many scatter charts utilize trend lines. A trend line is drawn on the chart to emphasize the direction and strength of the pattern.
The Best Practices for This Chart Type
The good thing about scatterplots is that they can handle a remarkable amount of data. That said, it’s important for users to know what’s going into the data table that will make up the creation of this scatter chart. It’s recommended that the Y-axis is set at zero within a scatter plot to avoid distortion of data. There are cases where a scale accordion may be recommended to better display information, but those are rare. It’s important to keep the scale evenly distributed across both axes to avoid any hurdles in the display process.
With scatter plots, more data doesn’t lead to confusion. However, it’s important to make sure that the data points or dots you’re seeking to provide attention to are prevalent. This can be done by adjusting size or color variations so that this information stands out. Trend lines also help to provide clarity about a correlation between the scatter plot variables. Overall, a scatter chart benefits the user.