The Value of Data Visualization By Sarah Brich, Design Technologist

Recently, we posted about the value of GIS Mapping and its ability to bring clarity to otherwise complex or confusing data. Mapping is just one element of a broader term known as data visualization (a.k.a. data viz), the use of images to represent information. Those rings on an Apple Watch? Data visualization. The battery icon on your phone? It, too, is data visualization, showing you, at a glance, how much battery power is left. Although data visualization traces its roots to the 2nd Century, these modern-day examples testify to its manifestation in society today.

As data collection and analysis increases, the demand to make sense of it all via visualization increases as well. Data, you might recall from previous posts, is nothing more than numbers. Given the vast majority of human beings are hardwired to process information visually rather than numerically, the phrase “a picture is worth a thousand words” is made refreshingly clear. Let’s go back to the rings on the Apple Watch. Three concentric circles layered above one another, each with a unique coded color representing “move,” “exercise,” and “stand.” Beautiful simplicity, it’s all one needs to know the status of her daily fitness goal. Behind those rings, however, lies a litany of data compiled from onboard sensors measuring the wearer’s movement, heart rate, and GPS location with some help from a connected iPhone.

When it comes to digital marketing, the data set becomes exponentially larger. It’s not at all unusual for a single data file to include millions of individuals, each with identifiable personality traits; purchasing and consumption habits; professional, social and cultural characteristics; income levels; ethnicities; geographic locations; and more. To illustrate the depth of the data abyss, consider this: DX Marketing has a database of 115 million addressable households in the US that we are able to combine with over 700 million offline and online behavioral profiles and more than 45,000 data segments. This is the very definition of Big Data and, as these numbers suggest, it can be tricky to understand all the information that it presents. Data visualization allows marketers to view Big Data in a way that’s easy to comprehend, so they can quickly solve problems, gain new understanding, and ultimately enhance the return on marketing investment.

Let’s look at an example of data visualization in action with this sample data from RawGraphs.

Example of Data

This snapshot of the data shows different types of music storage mediums, including the media, year, and market share. Although this is a small set of data with only 496 data segments included, it would nevertheless be difficult for most to take away more than one or two simple facts from a quick glance of the data alone. This specific data set is a time series, meaning the data is listed in a set of data points over a certain period of time. Specifically, it is quantitative, discrete data. This means the data contains numbers that cannot assume just any random value. Rather, the value of the numbers is important. Knowing basic facts like these can guide us to knowing which data visualization tools will best represent the information. For instance, the most effective ways to represent time series visualizations are Gantt Charts, Area Graphs, Bump Charts, Horizon Graphs, and Stream Graphs.

For our example, here I used an Area Graph:

Area Graph
Area Graph

From this example, we can quickly see that CDs have had the largest market share over a long period of time. Let’s say I wanted to see a bit more detail with my data, I could show all of the data points with a scatter plot:

Scatter Plot

From this, we are able to see that in 2010, CDs had the largest market share, however, in 1990 cassettes and CDs had the same market share. Electronics retailers would find this information extremely valuable to help determine the right mix of product offerings.

It is also important to present data without a bias. It is easy to display data in a way that can show a certain outcome if the proper rules for data visualization are not followed. For example, one rule is to always show bar charts starting at 0 on the y-axis. From our music data example, let’s compare the market share of CDs vs cassettes over all years with an improper y-axis starting at around 600:

Biased Bar Chart

Looking at this biased chart, it can appear that cassettes had very little market share compared to CDs. However, if we properly start the y-axis at zero, it becomes clear that while CDs have enjoyed the largest market share, cassettes once had a sizable market share, too. With the ratios of informational data now properly aligned according to basic rules of data visualization, the data is presented accurately.

More Accurate Bar Chart

Another best practice protocol for data viz specialists to follow is to use a wide variety of tools. There is a tendency to gravitate to a single favorite tool because of its ease of use, attractive graphics or other characteristic. However, just like people, no two data viz tools are alike. For instance, Tableau does exceptionally well with huge amounts of data. D3js is a standout javascript library with many interactive visualization options, but Dimplejs is our go-to choice for quickly coded graphs in javascript. Also an interesting point is that Tableau and D3js can work together to make very robust visualizations. Another reason to never settle on one single tool is that not all CMOs are created alike, either. In other words, while one CMO may find viz graphics from Illustrator the most comprehensive, another CMO may find it absolutely confounding. The best way to know which tool is best for your client and your data “story” is to put them to the test.

Here is an example of D3js: Word Cloud of words from

Word Cloud

For a more Interactive example, here is Tableau Public:

If Big Data is leaving you feeling more confused than confident, perhaps you need more reliable data visualization to help you see through the numbers and improve your marketing ROI.

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