As I worked through a just completed project chronicling the diverse musical career of Neil Young, some valuable (if unintended) insights were reinforced once more. I work on a regular basis with a variety of large datasets that require analysis, interpretation, and ultimately visualization and presentation. Often, these goals are not easily reconciled, which leads to unsatisfactory results across one or more of these factors.
As much as we as analysts need to depict the data accurately and meaningfully, if we don’t do so with an attractive visual approach we risk not having our message get communicated at all. Merely presenting our data in a table may technically get the job done, but is also likely to bore the reader to tears while simultaneously failing to deliver the key messages. At the other extreme, we can pull out individual bits of the data and spend our time creating flashy infographics that may capture attention but fail to represent the data in its proper context. All flash, no substance. Neither approach is terribly effective.
At the same time, we may present all of the information using a reasonable visual approach that preserves the integrity of the data while still falling short of creating a fulfilling user experience. This is what I recently experienced with the Neil Young project, as I’ll detail below.
After spending a few days getting the data from the AllMusic site into Excel, and eventually as node and edge files into Gephi, it was finally time to create the network data visualization. I was determined to attempt one of the many force-based methods used in network graph analysis to create the graph. These methods are very popular and useful for creating graphs out of a variety of data networks, allowing viewers to see the larger patterns at work within the data.
After a few iterations, I wound up with a serviceable graph that covered most of the basics I spoke of earlier – all the data was exposed, element types were sized and color-coded for easier interpretation, and the project was navigable via the web. Here’s a look:
Not bad, but there was something nagging at me as I viewed it, tweaked it, played with the styling, and so on. Everything was technically fine, but something was missing. So back I went to Gephi to find the answer. The next day, it occurred to me – I was using the wrong approach for the type of data I was trying to depict. Where the force-directed approach is ideal for dense, social media type networks, this was a unique network that didn’t possess the same structure. Therefore, it was not as aesthetically appealing or as intuitive as it could be.
After iterating through a few approaches, I came across a winner that best exploits the structure of the underlying data while conveying a far more intuitive feel to the end user. Why not have Neil at the center of the graph, surrounded by all of his albums, ordered by release date? On top of this, I could then have the style and mood data form an outer ring, as they needed only to link to the albums in some fashion. Now we have something that conveys the same information as the first attempt, but in a much more pleasing layout relative to this dataset. See for yourself:
The new version addresses the issues of aesthetics and intuition where the first graph fell short. All moods and styles are now easily found; the same is true for all albums. Highlighting a single mood (or album) also provides an information-rich view for how the music changed over periods in Young’s career. This was nearly impossible to see in the initial layout.
So the message is this – visualizations not only don’t need to sacrifice aesthetics and intuition in order to be effective; rather, they should take advantage of these attributes to increase their appeal and impact. Don’t be afraid to experiment until you find the right formula, as it seldom presents itself the first time around, and trust your instincts.