Data, Personalization, and Practical Magic
Steven Johnson’s new book, 的地狱 Machine: A True Story of Dynamite, Terror, and the Rise of the Modern Detective, has been justly acclaimed for packing all of the punch of great detective fiction. But Johnson’s modern detectives aren’t interrogation room intimidators. They’re a handful of early 20th-century 信息 science pioneers who brought data-driven detective work to the NYPD. These proto-data architects not only revolutionized crime fighting, but they also planted the seeds of our modern data- saturated surveillance state.
One of the book’s most memorable scenes concerns the trial of burglar 查尔斯Crispi in 1911, in which detective Joseph Faurot introduced fingerprint evidence for the first time in an American courtroom. Faurot had built a card catalog of fingerprint data categorized by various ridge patterns that enabled him to match a collected fingerprint with any print on file within 6 minutes. Though Faurot knew he had Crispi dead to rights, convincing a jury using entirely unfamiliar science presented a unique challenge.
After 4 hours spent painstakingly explaining the science of whorls, 拱门, and loops and drawing only blank stares from the jury, Faurot proposed a magic trick: With the detective out of the room, the court would take fingerprints from all 12 jurors, then take a second set of prints from one randomly selected juror. The detective then returned and ID’d the matched prints in seconds, quickly making converts of them all.
Discussions of data acquisition, 应用程序, and monetization that would have seemed like the most mind-blowing magic to denizens of other eras proved ever-present at 流媒体 NYC, the reimagined and rebooted 流媒体 event that made its raucously well-received return to Manhattan in May. The first 流媒体 event to emphatically position itself (by way of new conference chair Evan Shapiro) as a stock-taking, industry-recalibrating follow-on to the previous week’s TV advertising industry upfronts, 流媒体 NYC deftly steered clear of data discussion-as-信息-science-seminar and hewed closer to the magic trick vibe.
在一个 spirited AI debate that helped kick off the show, General Creativity CEO Robert Tercek painted a striking picture of how far we’ve come from a few thousand fingerprints classified in card catalogs: “As you walk around New York City with a phone in your pocket, you’re leaving a trail of data smog that’s being collected by hundreds of companies,他说. “关于 300 companies know that we are all in this room because our phones are in close proximity. They’re collecting a profile. And they know that we have some things in common because we’re in this room together. That data is incredibly useful for marketers.”
在他的 animated closing keynote, eschewing the increasingly fossilized “data as the new oil” metaphor, Shapiro put a fresher analogy in circulation: “In our media universe, I think we have to really get our heads around the idea that data is the bloodline—that everybody has content, but [we struggle] to understand how that content is being used, how to get that content discovered, and how to keep that content relevant.”
If using data to drive streaming monetization was the week’s overarching theme, misusing data took center stage in 流媒体 NYC’s curtain call: “Who here has seen the same ad many, many times across multiple services in on-demand video in the course of one night?夏皮罗问道. “That is our fault. That is a bad use of data. The people in this room have access to technology to fix this user experience problem. The over-frequency of ads is a bad thing for our ecosystem. It’s bad for the user, it’s bad for the advertiser, it’s bad for the platform, and it’s bad for the brand. No one wins, and there is no excuse for it.”
Personalizing user experiences is arguably streaming media’s signature magic trick, and we’ve got the data and the tools to pull it off. 不要再找借口.
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