Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [86]
But in the autumn of 2010, as Watson prepared for its culminating Jeopardy match, it had yet to land its first hospital job, and its medical abilities remained largely speculative. “We have to be cautious here,” Jasinski said. Though full of potential, Watson was still untested.
It may seem frivolous for the IBM team to have worked as hard as it did to cut down Watson’s response time from nearly two hours to three seconds. All of that engineering, and those thousands of processors were harnessed, just to be able to beat humans to a buzzer in a quiz show. Yet as Watson casts about for work, speed will be a crucial factor. Often it takes a company a day or two to make sense of the data it collects. It can seem remarkable, because the data provides a view of sales or operations that was unthinkable even a decade ago. But still, the delay means that today doesn’t come into focus until tomorrow or next week. The goal for many businesses now is to process and respond to data in real time—in the crucial seconds that a quick investment could net $10 million or the right treatment could save a patient’s life. Chris Bailey, the chief information officer at SAS, a major producer of analytics software, says the focus is on speed. “Our goal is to make the systems run a thousand or a million times faster,” he said. “That enables us to look at a million times more input.” With this speed, companies increasingly will be able to carry out research, and even run simulations, while the customer is paying for a prescription or withdrawing funds.
Computers with speed and natural language are poised to transform business processes, perhaps entire industries. Compared to what’s ahead, even today’s state of the art looks sluggish. Consider this snapshot of the data economy, circa 2011: A man walks into a pharmacy to renew his blood pressure medication. He picks up some toiletries while he’s there. He hands the cashier his customer loyalty card, which lowers his bill by a dollar or two, and then pays with his Visa card. This shopping data goes straight to Catalina Marketing in St. Petersburg, Florida, which follows the purchases of 190 million shoppers in America. Catalina scans the long list of items that this consumer has bought in the last three years and compares his patterns with those of millions of others. While he’s standing at the register, it calculates the items most likely to interest him. Bundled with the receipt the cashier hands him, he finds several coupons—maybe one for oatmeal, another for a toothpaste in a new upside-down dispenser. If and when he uses them, Catalina learns more about him and targets him with ever-greater precision.
That might sound like a highly sophisticated process. But take a look at how Catalina operates, and you’ll see it involves a painfully slow roundtrip, from words to numbers and then back again. “Let’s say Kraft Foods has a new mac and cheese with pimentos,” said Eric Williams, Catalina’s chief technology officer. The goal is to come up with a target group of potential macaroni eaters, perhaps a million or two, and develop the campaign most likely to appeal to them. The marketers cannot summon this intelligence from their computers. They hand the instructions—the idea—to Catalina’s team of seventy statisticians. For perhaps a week, these experts hunt for macaroni prospects in the data. Eventually they produce lists, clusters, and correlations within their target market. But their statistical report is not even close to a marketing campaign. For that, the marketers must translate the statisticians’ results back into words and ideas. “Trying to interpret what these people find into common language is quite a feat,” Williams said. Eventually, a campaign takes shape. Catalina concocts about six hundred to eight