According to Google’s dictionary, a mission statement is “a formal summary of the aims and values of a company, organization, or individual.” Some of these stated missions are quite broad, leaving a lot of runway to innovate while keeping track of the business’ original purpose. Consider Microsoft’s, “Our mission is to empower every person and every organization on the planet to achieve more.” Others are laser-focused on a specific product, such as Honest Tea’s, “to create and promote great-tasting, healthy, organic beverages.” For me, a great mission statement is short, incorporates primary values, and provides some direction. The most important aspect of mission statements is that they help in decision making because they remind stakeholders of the purpose at hand. The idea is that these short statements can scale to keep everybody pulling in the same direction. You’ve likely at least heard of mission statements, if not a few famous examples, but have you considered them in the context of data visualization? This post shares my data visualization mission statement and how it helps me solidify some of my opinions including why I believe data visualization is superior to spreadsheets and why I don’t use pie charts.
Last week, I had the honor and privilege of participating in the Asia-Pacific (APAC) Tableau Zen Master Tour. The tour was a whirlwind of nine presentations to more than 1,400 Tableau customers over five days and two countries – beginning in Tokyo, Japan and culminating with the Data Day Out event in Singapore! Along for the journey were Pooja Gandhi of Comcast and Andy Cotgreave of Tableau. We were also joined by David Murphy of Google, the only Tableau Zen Master in the region, for the Singapore leg of the trip. I would also be remiss if I didn’t mention Sarah Kurian of Tableau, who traveled with us and was instrumental in the planning of the event. She kept us on schedule and made sure we never forgot the hashtags #zentour and #datadayout 😊 The trip was packed with highlights for me both personally and professionally. One of my favorite aspects of the tour was getting to know my travel companions better. It was fascinating to be along for the ride to see the masters at work. I also learned, or was reminded of, three powerful data visualization lessons that I want to share.
This is the fourth and final post about my newest strategic framework for data visualization. For previous posts, see An Introduction to the Triple Crown Framework, Triple Crown Framework: Psychology, and Triple Crown Framework: Data. Why do you visualize data? My answer is to find and communicate actionable insights. The first two pillars of the Triple Crown Framework have helped us find and communicate insights, and sometimes to help make them actionable, they need to be seen by the right audience. Sometimes this means the most relevant audience possible (i.e. the decision-maker who can take action); other times this means the largest audience possible (i.e. raising awareness to the mainstream). In either case, my ‘Trojan Horse’ for getting my visualization to cause an impact is design. Among other benefits, an effective design has the power to engage stakeholders, improve adoption, increase credibility, and cause virality. The trend that I’ve observed during my eight-year data visualization career is that an increasing number of practitioners are realizing that you must balance data with design to have the best chance at causing action. This post shares some of my favorite design tips which are applicable for both a corporate environment or mainstream audience.
This is the third in a series of four posts about my newest strategic framework for data visualization. For previous posts, see An Introduction to the Triple Crown Framework and Triple Crown Framework: Psychology. Subscribe here for future updates. You can’t have data visualization without data, so we all know this is an indispensable aspect of our practice. I’ve heard many people complain that data preparation constitutes 80% of their data visualization projects; leaving only 20% to the fun part (in my opinion) of making the data engaging and easy to understand. I typically say that data is ‘half the battle’, but I aim to flip the 80 /20 ratio so that the data aspect only takes 20% and the rest of the time is left for me to focus on visualization and user experience. While this is not a full-fledged data engineering post, I am going to share what I view as the single biggest barrier to Tableau adoption (and it’s related to data), my favorite efficiency tips, and some thoughts on creating advanced chart types.
This is the second in a series of four posts about my newest strategic framework for data visualization. Subscribe here for future updates. As mentioned in the introduction to the Triple Crown Framework, psychology was the third and final guiding pillar that I believe leads to the creation of the best data visualizations. However, despite being thought of last, the psychological aspects of data visualization occur before anything else. Even the consideration of a strategic framework in the first place is a psychological exercise that takes place before getting into the data or design. For this reason, I’m going to move psychology up to the primary pillar, and provide three examples of how psychology can improve your data visualization. This post will provide some context around the importance of knowing your audience, introduce the concept of psychological schemas, and share my personal approach to explicitly introducing the value of data visualization.