In my last post, I discussed how the adage “Quality over quantity” has become such a commonplace phrase, that it’s often used as a justification for why some of us don’t have more than 50 friends, haven’t dated X amount of people by age X, or don’t have more than two pairs of jeans hanging in our closets.
However, this isn’t to say that quality always trumps quantity. In fact, I’m confident that qualitative nor quantitative data is necessarily “better” than the other. In his article “Qualitative Analytics: Why numbers do not tell the complete story,” author Anmol Rajpurohit argues that people tend to resort to their “gut feeling” and favor one type of method (qualitative vs. quantitative) over the other. Numbers-focused data shows what the “what” behind a trend - how many people participated in X behavior, how much time they spent on a certain activity, how much money they put towards a product or service, and so on. But as Rajpurohit argues, quantitative data doesn’t give the entire picture. He writes, “Qualitative analytics includes the analysis of context, human behavior, emotions and other factors that are hard to digitize without losing any meaning … it is also a great tool to bridge the gap between insights provided by quantitative research, and provide in-depth understanding of the underlying reasons and motivations for a phenomenon.” Take, for example, the MLB World Series**. Last night, I watched the Boston Red Sox dominate the L.A. Dodgers in their fifth game by 5-1. By the start of yesterday’s game, Boston was already enjoying a three wins over the Dodgers before clinching their fourth victory, and their eighth World Series champions title. **For the record, I am NOT a regular sports watcher; I was simply using the information I had in front of me to determine what the overall outcome would be. As a California resident, I felt obligated to root for Dodger Blue but based on how the past four games went I used the quantitative data I had (a.k.a. the past scores, each player’s batting averages, number of home runs, etc.) to predict how well I thought the Dodgers would perform in the fifth game. In the end, my prediction was right. The proverbial nail in the coffin came when Sox hitter Steve Pearce hit his second home run in the eighth inning, putting the final score at 5-1. I’m no sports analyst. But I imagine that the sports reporters and statisticians whose job it is to dissect every facet of the games they watch, as well as each player, don’t rely on the numbers alone. They’re also evaluating - qualitatively - what other factors might’ve played into the outcome of the game beyond the statistical data. They’re looking at the possible impact of home field advantage (which of course did nothing to help the Dodgers yesterday), the experience level of the players, what errors contributed to bad plays, and opportunities that certain players might have used to steal a base or swing for the fences. (Note: This New York Times play-by-play of Game 5 is a great resource that shows how exactly the Red Sox won the World Series by analyzing each inning.) Sean Donahue, founder and CEO of Context Strategies, writes in his commentary “The Big Data Craze Is Just as Qualitative as It Is Quantitative” that most businesses, government entities, and other organizations don’t put enough emphasis on the “why” behind a trend: “It’s easy to rest on our laurels when the obvious answer might appear to be right in front of us,” Donahue says. “But disrupting the status quo is just as qualitative as it quantitative - in this case, it’s the only way that we make big data work.” When used in conjunction, quantitative and qualitative can prove to be a home run for organizations that use and apply the information gleaned from both types of data analysis.
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