Big Data Delusions: Samuel Arbesman Debunks Common Misconceptions
Here's what he (and popular big data speakers) had to say about it:
- Big Data Doesn't Have A Clear Definition: "The term is thrown around so often, in so many contexts—science, marketing, politics, sports—that its meaning has become vague and ambiguous," Arbesman says. The concept has technically been defined, but because of how commonly it's used today, confusion in the industry has arisen as to what, specifically, constitutes big data.
- Big Data Isn't New: "By many accounts, big data exploded onto the scene quite recently," Arbesman writes. "But big data has been around for a long time." In fact, Arbesman says it's been around for centuries. Debbie Berebichez, a physicist and science educator, agrees. In her new big data talk, she speaks to the way that those in the sciences and on Wall Street have been using big data analysis for years and why it's important to acknowledge the way the process has been used in the past. In a historical keynote, Berebichez shows us how different disciplines used big data and how we can improve the practice today by pinpointing where we went right and wrong before. We may have new ways of harnessing big data, but we can still learn from the ways of the past.
- Big Data Isn't Necessarily Revolutionary: If you want more precise advertising directed toward you, then yes, big data is revolutionary," Arbesman argues. "Generally, though, it’s likely to have a modest and gradual impact on our lives." In a recent talk, Sean Gourley, a TED Fellow and founder of Quid, says that big data can't yet solve our biggest problems."We're sitting here with one of the biggest technologies we've ever invented, and we're using it to solve trivialities," he notes, mirroring what Arbesman says about the limits of big data for social causes. But Gourley believes that shifting our focus from data science to data intelligence could help move us into a new era of big data use. Jake Porway, founder of DataKind, feels the same. He has a vision of using big data to solve problems beyond bourgeois applications like finding a local bar or the nearest parking spot.
- Bigger Data Isn't Better: "We need long data, not just big data," Arbesman tells us. Big data sets are complex and sometimes difficult to effectively work with. That, and they only offer a snapshot of one moment in time, something that could be accounted for by including long-data sets into the mix.
- Big Data Won't Eliminate Scientific Theories: Big data can certainly help us to better understand the world. But, it won't teach us everything. "To contend with the “why” of things, we still need ideas, hypotheses, and theories," he says. "If you don’t have good questions, your results can be silly and meaningless." Jer Thorp, a popular big data speaker, also talks about the importance of approaching data analysis as a medium to ask questions—not just as a way to look for answers. It's important not to see big data analysis as the means to an end. Humanizing and contextualizing the data, Thorp says, allows you to ask intriguing new questions about the human experience. And that, he argues, is perhaps more beneficial than knowing all the answers.
To hire any of the big data speakers mentioned in this article, contact The Lavin Agency Speakers Bureau.