Visual Business Intelligence – To Err Is Human

Visual Business Intelligence – To Err Is Human

Visual Business Intelligence – To Err Is Human

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).

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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.

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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.

 



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