Remember The Jetsons? The flying cars and the automated kitchen and the food pills? Sometimes modern life can feel like that futuristic utopia. We've got robots in the home and a speech-recognition personal assistant named Siri built in the iPhone in our bag. IBM Watson, a supercomputer, beat its human competition in the TV game show Jeopardy! last year. How? By translating corny, nuanced questions into a format it could understand and compute.
But for most of us, our digital experiences at work feel like we're stuck in The Flintstones.
We wonder: "How can Amazon.com monitor my customer data so closely that it knows what book I want next, but after five years of daily use, my enterprise search engine doesn't get that I work in HR in the Chicago office?" We need to dig into our enterprise information so it is more rich and useful. Hal Varian, Google's Chief Economist, explained in the McKinsey Quarterly that "We have free and ubiquitous data, so the complementary scarce factor is the ability to understand that data and extract value from it." (He even goes so far as to say that statisticians will be the sexy job in the next 10 years!)
It's understandable to be cynical about semantic processing, especially if you've been told it relies on manually entered metadata.