Today, when data visualizers have access to powerful design and analytical tools, there is a much greater ability to bridge between analysis, research, synthesis, and design. At the same time, the tension between business intuition and experience and quantitative or qualitative insight often becomes more apparent. In my own work, one of the most vexing challenges is how to bridge this gap. As a visualizer, I bring my research experience, business acumen, and visualization background to the table and can't claim the level of experience that a product owner would have with the business that they run. Likewise, a product owner has a wealth of experience with the product or business line that they manage, but may find it hard to pull up and look at a fully objective analysis.
It's a fundamental belief of mine that the art of analysis and visualization is a complement to business intuition, and that in an era where we have unprecedented access to information, business leaders have more objective data than ever to either support or refute their assumptions.
Some people--like Malcolm Gladwell in this very interesting and wide-ranging discussion with Qualtrics last week--describe managing the tension between analytics and intuition as alchemy. But I strongly disagree with that term. Alchemy is a term from ancient and medieval history that refers to the dark art of transforming a base metal like lead into gold. It implies a level of mystery that does a disservice to what analytics and visualization actually do--"the pathway to many abilies some consider to be unnatural".
It's not about doing something that is mysterious, artificial, or hard to understand; on the contrary, good analysis and visualization are as transparent as possible about the methods they use and how they represent data graphically. That said, it's wrong to dismiss this tension out of hand because it's something that I encounter every day, both in my "day job" at NIH and in my baseball research.
I often work with product owners--meaning people who manage a particular digital product, and who are used to measuring their success based on whether their project is finished on time and under budget. They often have years of experience with that product and in their business. Their intuition isn't based on instinct, but rooted in that deep knowledge. So it's hard for them to think of replacing their hard-earned experience with numbers that are collected by people who don't have that level of experience.
I was recently walking through some research with a SABR chapter when a member said that one doesn't need fancy numbers to know that Josh Gibson was a great hitter--one only needs the stories and even legends about him. And while that may be true, showing the data in a compelling way helps to make those arguments even more effectively with a uniform basis for discussion. The stories are still there -- but now they form an elegant interplay with the numbers. And arguably, having numbers to support the stories--especially now that Negro League data are part of the official major league canon and we are able to recognize the achievements of Black players who were systematically excluded from professional baseball during the first half of the twentieth century.
In the end, it's both. Knowledge informs data from insight, and data informs decision-making based on knowledge. Ideally they both exist symbiotically, and not as alchemy, but as two well-understood ways of thinking. It's not a mystery--it's having as much information as one can have in order to make decisions in the best way possible.
I began my career studying international relations -- a field that was rooted in human relationships, and knowing what to do in every situation. As I've gone forward in my career, the more I've learned that while we do have rules and standards that we use to govern what we do, we also have to look at facts and data to guide us. We can't do one without the other.
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