Factual’s Gil Elbaz Wants to Gather the Data Universe
AT 7 years old, Gilad Elbaz wrote, “I want to be a rich mathematician and very smart.” That, he figured, would help him “discover things like time machines, robots and machines that can answer any question.”
In the 34 years since, Mr. Elbaz has accomplished big chunks of these goals. He has built Web-traversing software robots and answered some very big questions for Google, along the way becoming a millionaire several hundred times over.
His time-machine plans, however, have been ditched for something he finds more important: trying to identify every fact in the world, and to hold them all in a company he calls Factual.
“The world is one big data problem,” Mr. Elbaz says from his headquarters, a quiet office 14 floors above the Los Angeles Country Club. He is a slim, soft-spoken man who weaves in his chair when an idea excites him. “What if you could spot any error, as soon as you wrote it? Factual is definitely a new thing that will change business, and a valuable new tool for computing.”
In the booming world of Big Data, where once-unimaginably huge amounts of information are scoured for world-changing discoveries, Mr. Elbaz may be the most influential inventor and investor. Besides Factual, he has interests in 30 start-ups, including an incubator in San Francisco dedicated to Big Data. Factual’s headquarters, in a high-rise on the Avenue of the Stars, hosts seminars for a data community he hopes to foster in the Los Angeles area.
Mr. Elbaz also serves on the boards of the California Institute of Technology, his alma mater, and the X Prize Foundation, which offers cash prizes to teams that meet challenges in space flight, medicine and genomics. The company he sold to Google, Applied Semantics, is the basis of Google’s AdSense business, which brings Google close to $10 billion in revenue annually.
While valued for his investments and guidance, Mr. Elbaz remains relatively little-known. He is so self-effacing that he recently walked through a conference of 3,000 data scientists, recognized only by the staff members of one of his investments. He lives quietly with his wife, a former federal prosecutor, and his three children in a modest ranch house in West Hollywood. For fun, he plays basketball at a local sports club.
His mental and financial assets, he says, are like gifts he needs to deploy so the world works better.
“If all data was clear, a lot fewer people would subtract value from the world,” he says. “A lot more people would add value.”
Creating clear, reliable data could also make Factual a very big company.
“Gil is pretty far ahead of the rest of us, the one entrepreneur where it takes a few meetings before I really understand everything he is talking about,” says Ben Horowitz, a venture capitalist who backed Factual through his firm, Andreessen Horowitz. “Three years ago, he thought Factual was his biggest chance to change the world. Over time, the world has moved his way.”
Since its start in 2008, Factual has absorbed what Mr. Elbaz terms “many billions of individual facts we’ve collated.”
Geared to both big companies and smaller software developers, it includes available government data, terabytes of corporate data and information on 60 million places in 50 countries, each described by 17 to 40 attributes. Factual knows more than 800,000 restaurants in 30 different ways, including location, ownership and ratings by diners and health boards. It also contains information on half a billion Web pages, a list of America’s high schools and data on the offices, specialties and insurance preferences of 1.8 million United States health care professionals. There are also listings of 14,000 wine grape varietals, of military aircraft accidents from 1950 to 1974, and of body masses of major celebrities. Odd facts matter too, Mr. Elbaz notes.
He keeps 500 terabytes of storage near Factual’s headquarters. That’s about twice the amount needed to hold the entire Library of Congress. He has more data stored inside Amazon’s giant cloud of computers. His statisticians have cleaned and corrected data to account for things like how different health departments score sanitation, whether the term “middle school” means two years or three in a particular town, and whether there were revisions between an original piece of data and its duplicate.