This post kicks off a partnership with Jon Schwabish from PolicyViz.com in which we are interested in exploring how teams and organizations improve the way they communicate with data. This is a frequent question Jon and I each hear at workshops and conferences, so we thought we would start a bigger conversation by posting some initial thoughts here and at Jon’s site. To kick this off, Jon has written about steps an organization can take and my post is about steps individuals can take (you’ll notice some interesting overlaps). We’re then asking you to provide us with your thoughts and experiences. You can get in touch with us using the comment boxes on either site, Twitter (me | Jon), or sending us an email. We’re not sure where these conversations will take us, but we’re looking forward to the journey.
"How do you influence others at your organization to adopt good data visualization practices?"
This is a question that is posed to me frequently. Through my workshops, people develop a new lens for looking at graphs, and along with this often a new or renewed disdain for things like gridlines, 3D, a lot of color, and pie charts—as well as a desire to want others to stop doing things “wrong.”
Which leads me to the approach that doesn’t work. It sounds something like this: “I went to this class (or read this book) and learned how to visualize data correctly. You should do it this way now—the correct way—because the way you’ve been doing it is wrong.” I’m being intentionally blatant here. The point is that no one likes things being forced on them.
Also, people tend to resist change. It’s a natural human tendency: change makes us uncomfortable. Let’s add to that potential feelings of not trusting one’s design instincts or ability implement good design via tools. It can feel intimidating. People may question whether those around them will be accepting of doing things in a new way or whether they’ll have support from managers or stakeholders. It can feel like a big risk. Take all of these things together, and it isn’t surprising that people are sometimes resistant to doing things differently. And certainly not shocking that people don’t tend to react positively to the “I am right, you are wrong” argument.
I’d like to introduce some alternative ideas for helping build acceptance and adoption of effective data visualization practices across a team or organization. The following strategies are not mutually exclusive; rather, this is a space where a multi-pronged approach will often yield the best results. Note also that this is intentionally not a comprehensive list. As mentioned in the intro, one of the goals through the posts that Jon and I are each writing on this topic is to start a conversation and also hear from you what approaches you’ve found successful. That said, here are a few strategies for building acceptance and adoption of effective data visualization practices:
Start with easy wins. Sometimes showing success in one place can help others be more receptive to trying something different. Consider whether there is a low-risk area where you can introduce data visualization best practices with little resistance, or if there are particular friendly audiences or clients who may be more willing to try out something new. Sometimes getting traction in a few places can create momentum. Over time, you may find others emulating the best practices that you’ve started to incorporate. It may help build the appetite for more. At which point, you may also turn to the following strategy.
Provide examples of and guidance on what “good” looks like. Expose people to best practices. Make it easy for others to create effective data visualization by providing resources. This can take the form of books, training, or internal templates and examples of effective data visualizations. I’ve worked with a number of organizations who have created data visualization libraries. This can serve as a source of inspiration when someone is facing a data visualization challenge or seeking a new way to look at something. It can save time by providing a starting point rather than recreating the wheel. A data visualization library is also a fantastic resource for new hires, as it simultaneously demonstrates examples of the data your team or organization communicates with and also illustrates best practices and sets expectations for quality of output.
Often, these libraries take the form of a slide deck and sometimes they are part of a broader PowerPoint or Keynote template. I’ve worked with teams before who have run a regular contest, where a little friendly competition motivates folks to create and share their best work and then a winner or several winners are selected for inclusion in the library. The best libraries are living, breathing documents that continue to grow and evolve over time.
Develop internal expert(s). Sometimes people resist change in this area because they aren’t confident in their skills. Having someone internal to turn to when someone is feeling stuck—whether it’s not knowing how to execute something in a tool, or not being clear on an effective approach for a given data visualization challenge—can help this situation.
Identify one or a few people and invest in them to build their expertise (provide resources like training and books and have data visualization and helping others with data visualization be a core component/expectation of their role). The best candidates will be people who enjoy this space and have already demonstrated some aptitude. This can be a great form of recognition and career development for the individuals tapped to become experts. It can also help reduce frustration of the team by providing an internal resource to whom others can turn when they get stuck.
Get support from someone influential. Grassroots efforts are awesome, but often momentum can be accelerated by getting support from someone influential. This could be someone in a senior role in the team or organization, or possibly just someone influential to whom others will listen. Convince them of the value of effectively visualizing information and storytelling with data and have them help set an expectation of others.
These are just a few thoughts and some of the strategies I find myself recommending most frequently. Be sure to also check out Jon’s thoughts on the topic. We’d love for you to continue the conversation: what tactics have you employed to convince others at your organization to adopt good data visualization practices? What additional strategies can you imagine would be successful? Leave a comment with your thoughts!