My revision of Alexander Popes words, “To err is human, to forgive, divine,” is not meant to diminish the importance of forgiveness, but instead to promote the great value of errors as learning opportunities. We don’t like to admit our mistakes, but it’s important that we do. We all make errors in droves. Failing to admit and learn from private errors may harm no one but ourselves, but this failure has a greater cost when our errors affect others. Acknowledging public errors, such as errors in published work, is especially important.

I was prompted to write this by a recent email exchange. I heard from a reader named Phil who questioned a graph that appeared in an early printing of my book Information Dashboard Design (First Edition). This particular graph was part of a sales dashboard that I designed to illustrate best practices. It was a horizontal bar graph with two scales and two corresponding series of bars, one for sales revenues and one for the number of units sold. It was designed in a way that inadvertently encouraged the comparison of revenues and unit counts in a way that could be misleading (see below).

I would not design a graph in this manner today, but when I originally wrote Information Dashboard Design in 2005, I had not yet thought this through. This particular graph was further complicated by the fact that the scale for units was expressed in 100s (e.g., a value of 50 on the scale represented 5,000), which was a bit awkward to interpret. I fixed the dual-scale and units problem in the book long ago (see below).

I began my response to Phil’s email with the tongue-in-cheek sentence, “Thanks for reminding me of past mistakes.” I had forgotten about the earlier version of the sales dashboard and Phil’s reminder made me cringe. Nevertheless, I admitted my error to him and now I’m admitting it to you. I learned from this error long ago, which relinquishes most of this admission’s sting. Even had the error persisted to this day, however, I would have still acknowledged it, despite discomfort, because that’s my responsibility to readers, and to myself as well.

When, in the course of my work in data visualization, I point out errors in the work of others, I’m not trying to discourage them. Rather, I’m firstly hoping to counter the ill affects of those errors on the public and secondly to give those responsible for the errors an opportunity to learn and improve. This is certainly the case when I critique infovis research papers. I want infovis research to improve, which won’t happen if poor papers continue to be published without correction. This was also the case when I recently expressed my concern that most of the books written about data visualization practices in the last decade qualify as “Data Visualization Lite.” I want a new generation of data visualization authors and teachers to carry this work that I care about forward long after my involvement has cease. I want them to stand on my shoulders, not dangle precariously from my belt.

Imagine how useful it would be for researchers to publish follow-ups to their published papers a few years after they’re published. Researchers could correct errors and describe what they’ve learned since publication. They could warn readers to dismiss claims that have since been shown invalid. They could describe how they would redesign the study if they were doing it again. This could contribute tremendously to our collective knowledge. How often, however, do authors of research papers ever mention previous work, except briefly in passing? What if researchers were required to maintain an online document that is linked to their published papers, to record all subsequent findings affecting the content of the original paper. As it is now, bad research papers never die. Most are soon forgotten, assuming they were ever noticed in the first place, but they’re often kept alive for many years through citations, even when they’ve been deemed unreliable.

A similar practice could be followed by authors of books. Authors sometimes do this to some degree when they write a new edition of a book. Two of my books are now in their second editions. Most of the changes in my new editions involve additional content and updated examples, but I’ve corrected a few errors as well. Perhaps I should have included margin notes in my second editions to point out content that was changed since the first to correct errors. This might be distracting for most readers, however, especially those who hadn’t read the previous edition, but I could provide a separate document on my website of those corrections for anyone who cares. Perhaps I will in the future.

Errors are our friends if we develop a healthy relationship with them. This relationship begins with acceptance, continues through correction, and lives on in the form of better understanding. Those who encourage this healthy relationship by opening their work to critique and by critiquing the work of others are likewise our friends. If I’ve pointed out errors in your work, I’m not your enemy. If you persist in spreading errors to the world despite correction, however, you become an enemy to your readers.

Data visualization matters. It isn’t just a job or field of study, it’s a path to understanding, and understanding is our bridge to a better world.

Take care,