SAN FRANCISCO -- Structure 2016 -- How did analytics go so spectacularly wrong during this year's presidential election? How can enterprises and telecoms trust conclusions delivered by analytics anymore?
As the Structure conference kicked off its first day, speakers extolled the virtues of cloud computing. Among these is the ability to deliver sophisticated analytics using big data, which is just not practical to deliver on a wide scale without the cloud.
At the same time, American voters selected Donald Trump as President, after months in which the best big data analysts in the world concluded Hillary Clinton would win.
I spoke to a few experts at the conference and asked them if this outcome means analytics is baloney. They said that analytics is valuable, but the election fiasco was a valuable lesson in the limits of analytics.
"When you have humans involved, analytics can only tell you one piece of the puzzle," Eric Chiu, founder and president of HyTrust, which provides security software for the VMware Inc. (NYSE: VMW) stack, told Light Reading. "Analytics are great when it has to do with machines, patterns and behaviors of things that aren't thoughts, feelings and emotions."
People voted for Trump but didn't tell pollsters they would do it, Jeetu Patel, SVP of platform and chief strategy officer for cloud provider Box.net, told Light Reading. "There were a fair number of people that didn't overtly state their preference for Donald Trump that did go out and vote for him," Patel said.
The conclusion for companies starting to put their faith in analytics: Don't rely on what people say. Rely on what they do.
Also, unlike people, machines don't lie, deceive themselves, or change their minds. And much of the domain of analytics doesn't involve people -- it involves network management and Internet of Things.
"Instrumentation in how people use products and services is getting baked into businesses," said Patel.
Analytics used right can predict human behavior. The key is to measure what people do, not what they say they will do. Mobile apps, location data, and web tracking are valuable tools for that.
"There's a huge difference between fuzzy and inaccurate data like polling, which is not a reliable indicator of how people will vote, and application data and mobile data," Matt Wood, GM product strategy for AWS, said in a presentation at the conference here. Application and mobile data "is not a shadow or mirage of intent -- it is exactly what customers have done." And unlike political polling, application and mobile data doesn't rely on sampling -- it collects all the data. (See Why Amazon Web Services?)
In other words: "Garbage in, garbage out."
That's not an expression you hear very much anymore, though it was common in the mainframe era of computing. For those who weren't around, tt means if you feed a computer bad information, you get bad answers.
The phrase was common when I was a pre-teen in the early 70s, learning in school about computers. And it's far older than that; the website Atlas Obscura finds an appearance in print in a 1957 Indiana newspaper, with hints that the phrase was already common among engineers by then.
Indeed, Atlas Obscura notes that "garbage in, garbage out" became self-referential, as Wikipedia attributed it wrongly to an IBM technician/instructor named George Fuechsel. That claim later appeared in a book -- perhaps originated by the Wikipedia entry. The book was added to the Wikipedia article as a citation, and then the information appeared in other books.
But Fuechsel himself wasn't sure; he posted a comment to a blog in 2004 wondering whether he was misremembering inventing the phrase.
The principle behind "garbage in, garbage out" dates to the 19th century, when Charles Babbage designed the first calculating machines. Babbage wrote: "On two occasions I have been asked,— 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question."
Here in the 21st century, data scientists should tattoo those two sentences on their arms.
— Mitch Wagner, , Editor, Light Reading Enterprise Cloud