MIT scientists apply methodologies from Mars mission designs to help marketers manage big data streams.

Jeff Bertolucci, Contributor

June 6, 2013

4 Min Read

5 Big Wishes For Big Data Deployments

5 Big Wishes For Big Data Deployments

5 Big Wishes For Big Data Deployments (click image for larger view and for slideshow)

Apparently there are similarities between digital advertising, where an automated system decides in milliseconds which ad to place before a consumer, and NASA mission planning.

DataXu, founded by MIT aeronautics and astronautics scientists in 2007, is applying its experience in designing NASA missions to the unique world of digital advertising, says company co-founder and chief technology officer Bill Simmons. The Boston-based startup is using its expertise in combinatorial optimization, techniques used to find optimal solutions to a specific problem, to help digital marketers boost their advertising efficiency and return on investment (ROI).

Under the Bush administration, the DataXu founders -- at the time a group of MIT researchers -- received a grant from NASA to determine which manned missions to Mars had the highest likelihood of success. "I was a graduate student at MIT, a full-time employee as a research assistant. I was working on my Ph.D.," Simmons told InformationWeek in a phone interview. "They gave out 11 grants to different institutions. We were in competition against Lockheed Martin, Boeing and other large companies."

[ Planning your big data strategy? Make it decision-driven, not data-driven. Read Big Decisions Drive Big Data Success. ]

Using their combinatorial optimization approach, the MIT team generated 1,162 potential Mars missions from some 30 billion possibilities, and presented its findings to NASA in a 1,000-page report. "We use a technique where you prioritize your decisions [to] eliminate the most options first," said Simmons. "Hypothetically, say you have a choice: One crewmember, two crewmembers or three crewmembers."

The team first eliminated the least desirable options. One crewmember, for instance, wouldn't work out because if he or she died or became seriously ill, the mission would fail. "You make that decision up front: One is not possible," Simmons said. "So you can scrap all the missions and configurations that support one crewmember."

What began as an insanely large number of options soon shrank to a very manageable figure. "In our case, we ended up with exactly 1,162 missions, and that's a small enough number to where you can go through them by hand," Simmons said.

The Mars mission and related research was later shelved, a victim of a terrible economy, a new presidential administration in 2009 and a revised focus on space exploration. The MIT team soon found a new use for its research, however, and today DataXu has about 700 customers.

The company specializes in display, mobile, video and social ads, but not search or email. Its technology has three major components: a real-time decision engine, using with DataXu's combinatorial selection algorithm; a data management and machine-learning system; and a user interface for managing marketing campaigns and interactive analytics.

DataXu's decision engine has a lot in common with its Mars-oriented predecessor, although its mission is to place ads rather than astronauts. The decision process involves many factors, including time of day, day of the week, where the user lives and so on.

"Each of the companies we work with run one to 50 different ad campaigns simultaneously," said Simmons. "So there's a many-to-many combinatorial matching problem that we need to execute in under 100 milliseconds every time someone loads a page. And 100 milliseconds is faster than the blink of an eye -- one-tenth of a second."

DataXu has the sole license from MIT to use its combinatorial optimization technique, and it's the only digital marketing firm using this approach, according to Simmons, who added that the system's flexibility also makes it an attractive choice. "We have an open algorithmic framework that allows clients that don't want to use our technology to deploy an algorithm written by their own data scientists on our platform," he said. "This is unique in the industry, as far as I know."

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About the Author(s)

Jeff Bertolucci


Jeff Bertolucci is a technology journalist in Los Angeles who writes mostly for Kiplinger's Personal Finance, The Saturday Evening Post, and InformationWeek.

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