I had the pleasure of lunching with Stephen Few last week. His books (particularly Show Me the Numbers) and overall approach to visualizing data has influenced my work in a big way over the years. We had an enjoyable conversation (and some amazing caramelized brussels sprouts!) at a lively Berkeley cafe.

We agree on a lot of things. We spent some time bonding over a shared distaste for pie graphs. In our work, we both promote clutter-free graphs that focus on the data, with sparing and intentional use of color to direct attention. We also happen to both be introverts who are passionate about data visualization in a way that leads us to break out of our respective comfort zones and share our learnings with others.

But there are certainly also areas of disagreement. Which is where the real fun comes in. As we were preparing to leave, Steve asked if I'd be open to discussing some of these areas. Of course! Friendly debate can help to refine one's point of view, articulate their logic, and perhaps even change their way of thinking.

One area of disagreement is on the use of 100% stacked bar charts. Steve already wrote a great post on this that summarizes our conversation well, which you can view here (be sure to read the comments, where our conversation continues and others chime in with their viewpoints).

Through some of our back and forth on 100% stacked bars, another area of varying beliefs came up. It has to do with the existence of a single "right" or "best" answer when it comes to visualizing data, the topic of this post. Let me summarize our respective points of view and then I'll ask you to share yours.

I believe that data visualization sits at the intersection between science and art. There is absolutely some science to it: best practices and guidelines to follow. But there is also an artistic component. This means that two different people may approach the same data visualization challenge differently. There is room for diversity of thought and approaches. But it is important that we use our artistic license to make the information easier for our audience to understand, not more difficult.

My view is that we are often faced with tradeoffs when it comes to determining how we design our visuals. For example, I may choose a 100% stacked bar even though it makes it hard to directly compare two segments within the bar (assuming that is a lower-priority comparison and it allows me to still make my main point clearly) because with it, I get an implicit visual cue that the segments are part of a whole. I've decided that the tradeoff is worth it. Or I may include an annotation on my graph, even though it adds what someone else might consider clutter, because I believe it makes an important point in the data easier to interpret. I might choose sparing use of bright orange to draw attention to a negative aspect of the data because it comes from the given brand's color palette, while someone else might choose black or red for the same purpose. In these situations, none of these choices is necessarily the "right" choice. Different people will make different decisions. I think that is ok. For me, paramount is that design choices like this are intentional—I considered the tradeoffs and made a decision in light of those—not happenstance. Yes, they should be informed by science. But my view is there is also room for personal choice.

Steve's view is that the introduction of art into this conversation is a slippery slope because individuals will define art differently. He states, "there is no art in what I do; it's all science" and goes on to say that when he makes a judgement to design a data visualization in a particular way, he's basing that judgement on a fairly deep understanding of what works and what doesn't, which is rooted in science. That said, he concedes that there is a creative component but that this creativity and judgement should always be applied in an effort to create the most effective outcomes in understanding.

Given this, his view is that in a particular situation, there is a single right or best answer for visualizing data. In a comment on his blog, he poses the following question to me:

In your opinion, are some data visualization solutions better than others in a given situation or are they all equal in merit? If the former, on what basis can we judge one to be better than another? You mentioned that data visualization designers should “make the information easier for their audience to get at, not more difficult.”? This suggests that we can judge the merits of a data visualization by its ability to make the information as easy to understand as possible. Choices of graph type, colors, etc., are made to present the information as clearly and accurately as possible. If one data visualization does this better than another, it is the better solution–correct? If this is true, does it make sense to say that there is no “best” solution in a given situation among a set of proposed solutions?

I don't disagree that some data visualizations are better than others. We've certainly all seen examples of data visualizations that are inferior for a great number of reasons (bad chart choice, lack of clear labeling, too many colors, unnecessary clutter), while others are more effective. For me, though, it is possible to have multiple varying visuals that may be equally effective (or roughly equally, if the differences are marginal enough not to matter and all do a good job of getting the intended information across).

What is your view? Is there a single "right" or "best" answer when it comes to data visualization, or is there room for varying approaches? Leave a comment with your thoughts. I look forward to the continued debate!