I attended an online webinar today hosted by Data Science Central titled Making Flow Happen: Dashboards that Persuade, Inform, and Engage. The presenter was Jeff Pettiross (photo, right) from Tableau Software. I found Jeff’s presentation to be very informative and helpful, but it was the Q&A session afterwards that I thought brought an interesting topic to the surface.
The question asked was:
When creating a dataviz and taking feedback, how do you determine what feedback is based on personal opinion and what feedback adds flow to your dataviz?
Jeff discussed this as having principal-centered arguments versus personal-centered arguments. So, for principal-centered arguments, you could refer to Edward Tufte when you are discussing the field of data visualization, junk charts or small multiples, Stephen Few for best practices for dashboard design, or Alberto Cairo for best practices for creating infographics. You could also discuss articles and academic research related to data visualization.
Where the water gets murky is when you are exposed to personal-centered arguments or, basically, someone’s personal opinion. Sometimes when you are sitting in a dataviz review session, the criticism or critiques you receive can feel very personal. Some of it may be in the way the person is expressing their opinion and the intonation in their voice. Other times it truly may be personal; that personal may not like the person being reviewed or feels threatened by their work.
Jeff made a real good suggestion related to personal critiques by simply asking more questions. Deflect the criticism and ask them to tell you more about what they did not like about the visualization. For example, they might feel your dashboard is too crowded or too busy. You might want to ask for suggestions from that person. If the situation allows, you could bring up a copy of that visualization and make the changes in real-time as they are stating their suggestions.
Jeff pointed out that, unfortunately, this will not work in all cases. If you are a paid consultant at a company, and the client insists that they want it a particular way, the old motto “The Customer is Always Right” would take precedence here. You could say, “O.K., we will do it this way this time, but I would like you to consider this as an alternative for future visualizations.”
Jeff pointed out that at Tableau, they are a critique-centric culture. They often have review sessions of their visualizations where people from different areas of the company may sit in. For example, you might have Sales people, consultants, marketing, training, etc. Using thoughtful critiques, spending about 20 minutes on each feature, and including a diverse group of people, they are able to refine the dataviz as a group and learn and hear other people’s ideas on dataviz.
Thanks to Jeff and Data Science Central for a great session today. What do you think? What do you feel is the best way to critique data visualizations?
I would love to hear your thoughts.