Data visualization is a key element of master data management for businesses in just about every industry. While having a rein over information is key to making sure that analytics and insights are accurate, it’s important to also be able to present these findings in a clear fashion. That’s where charts and graphs come into play. However, it’s very easy to muddle up these presentations with unclear lines and icons all over the place. A cleaned-up chart can be a lifesaver to truly demonstrate the points you’re trying to make.
What are stacked charts?
Clear visualization of numeric values in any capacity is needed for any chart or graph, including stacked charts. A stacked chart is a type of bar chart that shows the composition and comparison of certain variables, either relative or absolute, over time. Also known as a stacked bar or column chart, these are designed to look like a series of bars or columns that sit atop each other. These charts are an incredibly effective tool, designated to compare total values across numerous categories.
There are two main variations of these charts: horizontally stacked bar charts and percentage stacked bar charts. Horizontal charts generally run left to right, helping to give viewers a look at many totals. This means that data can be viewed on one screen, rather than scrolling up and down to compare the information. A percentage stacked bar chart is scaled to 100 percent so that every bar has the same height, making it more difficult to analyze the primary category totals but is far easier for the secondary distributions. These same charts are displayed vertically through columns as well.
What’s the best way to create these charts?
Bar charts and stacked charts can be approached in a similar fashion. The difference is the addition of another categorical variable. When working with stacked area charts, consistency of the scale is key. The axis must start at zero and be consistent no matter the chart type. Clear labeling is also important to make sure readers are clearly understanding the attributes of the graph they’re viewing. These charts cannot have negative values, as they would offset the positive values plotted on the axes, causing confusion and incorrect displays.
Category levels must also be considered when designing any type of chart. The order of the categories must be consistent across all the bars on the chart, usually going from largest to smallest, in both individual bars or columns with the most important value at the bottom of the chart. The color choice is very important in stacked charts as well. Designers opt for a sequential color palette to show off a continuous variable, like salary plains, going from lighter to darker based on levels. There’s also the qualitative palette that signifies action, or the diverging palette that operates on a Likert scale, basically agreeing or disagreeing with a query.
When should I use stacked charts?
Since they are not as common as bar charts, the objective is to compare numerical values of a categorical variable and the decomposition of each bar. Ideally, the chart should have numeric values, one or two categorical variables, and regular date intervals. These charts are designated for comparison purposes only. Businesses may use these charts to show sales volumes, breaking down sales across different store locations, and then subdivided into categories like male and female purchase values.
These different chart types can also be ideal for comparing population data and information. This can allow for marketing agencies to compare the success of advertising across different age groups to monitor datasets for what’s drawing customers. These charts have also been used in surveying, operating with the Likert scale as previously mentioned.
Despite limited scenarios, these charts can provide clarity through a sleek design to convey what readers need to know.