foreword
Early in my career, I was conducting a complex analysis as part of a consulting project. I spent weeks collecting and cleaning data and putting together a model that pointed to a clear recommendation. (True confession: if you were confused as to the seemingly random pricing of Wi-Fi on a major US airline in the early 2010s, I was partly to blame.)
As I walked my manager through it, however, his feedback was somewhat unexpected: he thought I did a great job on my analysis, but he wanted me to change my methodology, explaining that another angle would “better fit the story the client wants to hear.”
What?! As a data analyst, I saw my job as a seeker of objective truth, out to understand the world through exacting measurement. Changing my approach because it fits a story someone wanted to hear felt . . . weird. But looking back, I’m not sure it was wrong. The alternative approach was completely valid—it’s not like we were faking data. And just because it was what the client wanted to hear didn’t make it inaccurate; it was just another way to look at the world.
Reading through this new book by Mona Khalil, I found myself reflecting on lessons from a career in data and finding a new appreciation for the science—and art!—of effective data analysis. I believe practitioners of any experience and technicality can benefit from this book and avoid common pitfalls so they can make new, unique mistakes of their own.