Introduction
Everywhere you look, data takes its place in your surroundings. The amount of data around you can be disorienting when it is unfiltered and all over the place. Data visualizations have been around for centuries, from the oldest pictographs etched onto stone in caves dating back to the earliest troglodytes to today using tools such as Tableau, Power Bi, Splunk, Salesforce, and more. The best way to make the most of your data is to determine the right way to represent your data using any one of the plethora of visualization options you have at your fingertips!
Why do we need Visualizations?
Visualizations create dashboards, but why do we even bother with them? The first step to building a dashboard is to determine what kind of data is necessary and what kind of trend you want to observe in the data. There are multiple varied reasons to have data visualization.
Visualizations portray change over time. You can use standard charts such as bar charts, line charts, and box plots to show this change. Line charts are typically used if there is a baseline in the data that does not provide any value to the analysis or if there are too many variables to be considered, resulting in a cluttered bar chart. A time series box plot is used for distributing values plotted for a specific period and is useful when you have multiple data points in a specific time interval.
Visualizations compare a part of the data set and the entire thing. It can typically be done using a pie chart, which works best with a few variables to show the ratio of variables; a stacked bar chart, or a bar chart with multiple sub-sections as well to offer a part-to-whole comparison within each subsection; or a stacked area chart or a line chart with sub-groups to show better comparison.
Visualizations can show you how data is distributed, which is essential when understanding the properties of data features. You can use bar charts when a variable takes in specific values; a histogram when a variable takes numeric values; or a density curve, which is not technically a chart but does the job in place of a histogram to estimate an underlying distribution; a violin plot which plots density curves for each group to compare numeric value distributions; and a box plot to demonstrate the locality, spread, and skewness groups of numerical data.
Visualizations can compare values between groups using dot plots, amongst other charts. The dot plot can be used when a bar chart has too many values assigned between groups. A dot plot groups the number of data points in a data set based on the value of each point. Dot plots help detect the median of the data set, dispersion, and skewness of the data. To compare values, you can also use grouped bar charts, which group variables by plotting multiple bars at different locations; violin and box plots, both of which are used to compare data distributions between groups; funnel charts to show how variables move through a process (such as the conversion rate on an email marketing campaign); and bullet charts, to compare values between pre-determined benchmarks.
Visualizations allow you to observe relationships between variables better through bubble charts, which serve as an addition to scatter plots, through indicators such as colors, shapes, and even varied sizes to represent relationships. If ‘time’ as a variable is introduced, the scatter plot turns into a connected scatter plot, also known as a dual-axis plot, meaning that the chart is plotted with a horizontal axis rather than a vertical one.
Visualizations help you observe geographical data using a choropleth or a heatmap that uses different shades and colors to visualize data tied to geography and cartograms. This map combines statistical information with geographic location to show relative area, distance, and terrain.
How do you choose the best chart type for your needs?
Considering all the types of graphs and the needs that can be used for an organization, the next step will be to assess which type of graph best answers a specific question.
The first question to ask yourself before even starting with visualizations is to determine, “What is the story I want my data to tell?” Selecting one out of the six use cases (mentioned above) will help you provide meaning with what you want to deliver. Set a KPI (Key Performance Indicators), something you want to measure, such as revenue, conversion rate on an email campaign, or total goods sold.
The following questions will help you determine which chart to use for your data needs.
- Who will see the results of the data? Know your audience. Are you presenting to business people, doctors, or stakeholders with little knowledge of how data visualizations work? Knowing your audience lets you choose the best chart type to make data communication efficient.
- What is the size of my data? Some charts are meant to be used with less extensive data sets, so it is essential to determine the size before getting into what kind of charts you will use.
- What is my data type? Do I have categorical data or qualitative? You can use the kind of data to narrow your selection of the chart you will use.
- Does my data have any relation? For example, ask yourself if your data is ordered based on factors such as time, size, or type and if there is a correlation between variables.
Top Chart Types
There is no shortage of options when selecting a chart, but here are the six commonly used charts that are the easiest to build and interpret.
Here is how to know when to use them and when to avoid them.
- Bar charts
Bar charts, in general, should be used to present categorical, discrete, or continuous variables, compare parts of more extensive data sets, and show change over time. These charts also allow you to illustrate both positive and negative values in a dataset.
You should only use bar charts if you have at most ten sets of variables and multiple data points.
- Pie charts
Pie charts should show relative proportions or parts of a whole in a data set if your data is nominal rather than ordinal and only if you have up to 6 categories.
You should only use pie charts if you have at most ten variables or want to compare values precisely.
- Line charts
Line charts should be used if your dataset is too big for a bar chart, if the dataset is continuous, or if you want to visualize trends rather than exact values.
You should avoid line charts if you have smaller datasets.
- Scatter plots
Scatter plots should be used to show correlation and clustering in big datasets if your dataset contains pairs of values or if the order of the data points is not essential.
You should avoid scatter plots if you have a small dataset or if the values of your dataset are not correlated.
- Area chart
Area charts should be used to show part-to-whole relationships or if you want to portray the volume of your data, not just the variables’ relations to time.
You should avoid area charts if you have discrete data or data that can only take specific values.
- Bubble chart
Bubble charts should show the distribution or relationship between independent variables.
You should avoid bubble charts if you have a small dataset.
Conclusion
You can experiment with several types of charts for different variables. For easy visualization analysis, click here now contact Prudent so you can see your data in a whole new way!