This article is the beginning of a series of tips and formats (in alphabet order) to help you convey your data visualization messages in the best way possible.  

Starting with A for “Area Charts” and going all the way through W for “Word Clouds,” the pros and cons of each data visualization format will be explained and illustrated. Let’s begin with area charts:

Area Charts

Area charts are similar to line charts in that the height of the points convey data and are used for the display of continuous data. Area charts have completed or ‘filled in’ areas below the lines for more clarity in distinguishing the data. It is recommended that you stack the areas on top of each other so that you don’t obscure the back.

Area charts are important to emphasize the variations of change over a period of time. They can express trends that become clear to the viewer through the x-axis (time period) and y-axis (measurement) areas. Use area charts when you have a ‘part to whole relationship’. Always be careful to ensure that one series isn’t obscured by data from other series. This will allow the viewer to get a high-level view of the data.


Bar Charts

Bar charts can be perfect when you have more than one level of comparison. The human eye is sensitive to areas of the bars. Try to draw accurate conclusions so that the bars cannot be truncated. The numerical axis (often the y-axis) must begin at ‘zero’.  If you have multiple bars that share the same ‘height’, you will want to differentiate them with contrasting colors. Here is a good example: 


Business Intelligence Tools

Business intelligence is a broad term that relates to the ability to retrieve, analyze, and report data from either a single or multiple sources. The method of reporting can cover visual analytics, spreadsheets, and queries software to make use of ‘data mining’, decision engineering, or warehousing.

BI (Business intelligence) is typically sourced from an industry-specific database such as ERP, OLAP, CPM, CRM, etc. This is a concentrically focused group of data that can be collected for analysis and then presented in a meaningful way for the decision makers. It’S stored so that it can be used to measure past, present, and future (aka predictive analytics) and then presented to decision makers for business resolutions.  However, these standalone tools often house large amounts of data that require sorting. The results are spreadsheets that often hide critically needed information.  But when you transition the numbers into visual images, you can instantaneously reveal powerful insights. Here is a good illustration:

Choropleth Maps

A choropleth map is a type of thematic map that displays statistical variables through the use of shaded or colored areas. The color is the part of the map that is of the most importance as it is designed to scale the assignment of numerical or categorical data and there is a value for each region and the color or shade used.

When displaying predefined political borders such as counties, states or countries, choropleth maps are the method to use. The colors provide an easy visualization of measurement distribution across regions. But there are perceptual issues to consider. It’s important to make use of color scales that are appropriate so that the data isn’t over or under-represented. When you have multiple areas to demonstrate, it’s recommended to use five to seven color categories. Here is a good example of a chloropleth map:

Column Charts

Many people are familiar with column charts. They are simply bar charts that are vertically oriented. They feature rectangular bars of the same lengths and widths that represent values that are proportional, with each category visualized with a particular bar. Column charts are typically used to display data variations over specific periods of time or to show comparisons between the item topics. But be careful in the use of ‘stacked column charts’ where they make use of stacked in lieu of clusters. The visual may be attractive but it’s often confusing. Here is a good example of a column chart:


And here is an example of a stacked column chart:


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