As I’ve discussed in a previous blog post, a dashboard that doesn’t visually flag metrics that require attention will likely flop with users. In fact, the lack of such indicators could be the number one reason why so many dashboards fail to deliver acceptable levels of user satisfaction and traction.
While not as common on dashboards as other flagging methods, participants in my Practical Dashboards workshops often ask about “% deviation from trailing average”, so I’ve written this post to illustrate why that method actually isn’t much better than the more common alternatives.
Read moreMany of the charts that I see being passed around on social media because they’re confusing or misleading are also beautiful. They seem to have been created by people with great graphic design skills, but who’ve had little or no training in data visualization. Such designers may have been taught that "a visually engaging or aesthetically pleasing design is a good design" since this is certainly true for most types of graphics, but they don’t seem to fully realize that charts are a special type of graphic with a slew of additional design considerations beyond just aesthetic appeal.
Read moreDashboard designers often complain that “my users don’t even know what they want”. This isn’t a reasonable complaint, though, since it requires a great deal of skill to figure out what type of information displays will be most helpful to users, and we can’t expect users to have those skills. We, as dashboard designers, have to bring those skills to the table.
Read moreMuch has been written in recent years about how powerful data storytelling can be, and that’s certainly true. As the saying goes, though, with great power comes great responsibility. Someone who’s great at storytelling is, almost by definition, also great at suppressing the audience’s ability to think critically about what they’re hearing. If we get carried away and start crafting the data around the story instead of the other way around, great storytelling makes that harder for audiences to notice.
Read moreWhen people disagree on whether one chart design is better or worse than another, they often have quite different assumptions about what “better” actually means when it comes to charts. Depending on the person, “better” could variously mean more precise, more creative, more familiar, faster to visually process, more inspiring, more neutral, more versatile, more memorable, or any one of several other quite distinct definitions. Agreeing on a common definition of “better” will allow the data viz field to move past some longstanding controversies and make it much easier for novices to learn how to create truly useful charts.
Read moreA short video interview with performance measurement and improvement expert Louise Watson, in which she explains why a dashboard on its own won’t improve organizational performance. We also cover some of the common bad practices that torpedo performance improvement initiatives, as well as key elements that are required beyond just having a dashboard. Some absolute gems for anyone who’s ever struggled with how to choose the right KPIs.
Read moreBecause errors do happen, our dashboards will sometimes contain “obviously wrong” metric values, such as a “Customer Satisfaction Rating” of 12.5 on 10, or a “Manufacturing Defect Rate” of -14%. It’s essential that our dashboards be “smart” enough to detect such obviously wrong values so that we can visually flag them as incorrect on the dashboard. If we don’t flag obviously wrong values, users will likely notice them anyway, putting the accuracy of every other value on the dashboard into question.
Read moreSlope charts and other common ways of showing “before and after” data are almost always misleading. Using a “merged arrow chart” eliminates the risk of misleading readers.
Read moreOrganizations often ask me how to hire people who can create better charts for their decision-makers and this post summarizes my (current) answer to that question. While this post is written as a guide for employers, it can, of course, also act as a guide for those who want to become data visualization professionals themselves and work for the organizations that so urgently need those skills.
Read moreAs I discuss in the pre-workshop video for my Practical Charts course, many charts don’t do a great job of serving the purpose for which the chart was created in the first place. I think that there are several reasons why this is such a common problem and this post focuses on a big one, which is that there’s a lot of confusion around exactly what skills are needed in order to create truly useful charts.
Read moreI recently recorded the opening section of my Practical Charts online course as a 70-minute video for workshop participants to watch before live, online workshops begin. I decided to make this recording publicly available, and it can now be watched on YouTube.
Read moreI regularly hear or read comments such as, “Scatter plots are the most useful chart type”, “I love bullet graphs”, or “Clustered bars are better than stacked bars.” In this post, I discuss why these kinds of preconceived preferences or inclinations to use one chart type over another don’t really make sense.
Read moreFollowing a successful trial run, the Practical Charts course is now available for private online or in-person workshops. This new course from globally recognized data visualization expert Nick Desbarats equips participants with the knowledge and skills to handle all of the data visualization challenges that they’re likely to face in their work.
Read moreI sometimes get requests for a one- or two-hour abridged version of the two-day data visualization course that I teach. This blog post explains why I always decline such requests. It's fairly challenging to teach people enough to be able “safely” create charts that don’t accidentally mislead or confuse audiences in two full days, and so the main upshot of a one- or two-hour session is likely to be an unwarranted sense of confidence that participants have the necessary skills to reliably create clear, non-deceptive charts.
Read moreLast month, I delivered a 90-minute talk in SAS’s beautiful, state-of-the-art auditorium at their North Carolina headquarters. That talk was webcast live and, rather astonishingly, over 4,000 people registered to watch it and many more have watched a recording of it since it was made freely available on SAS’s site.
Read moreI recently contributed an article to SAS’s JMP Foreword magazine, entitled “All charts are biased, but some are useful.” In it, I challenge the common misconception that charts can ever be truly neutral or unbiased, and propose that we should, instead, strive to create the most useful chart for a given situation.
Read moreAs a number of people reading this already know, I’ve been working on a new workshop for quite some time. After several successful trial runs, I’m happy to announce that I’ve begun delivering the one-day Practical Dashboards course in private, on-site training workshops.
Read moreIn order to gain traction and acceptance among users, dashboards must visually flag metrics that are underperforming, overperforming, or behaving in other ways that warrant attention. If a dashboard doesn’t flag metrics, it becomes very time-consuming for users to review the dashboard and spot metrics that require attention among a potentially large number of metrics, and metrics that urgently require attention risk going unnoticed. In previous blog posts, I discussed several common ways to determine which metrics to flag on a dashboard, including good/satisfactory/poor ranges, % change vs. previous period, % deviation from target, and the “four-threshold” method. Most of these methods, however, require users to manually set alert levels for each metric so that the dashboard can determine when to flag it, but users rarely have the time set flags for all of the metrics on a dashboard. Techniques from the field of Statistical Process Control can be used to automatically generate reasonable default alert levels for metrics that users don’t have time to set levels for manually.
Read moreDot plots are a very useful chart type, but many people have trouble understanding them when they see one for the first time, which probably explains why they’re not widely used. In this post, Xan Gregg of JMP and I propose changes to the traditional dot plot design that might make them easier for first-time viewers to understand and, hopefully, make the use of this valuable chart type more widespread.
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