As the world’s fifth largest digital advertising network, AOL chews through a lot of data. That’s not surprising. What may surprise you, however, is the unconventional way that the company goes about assembling its data science team to ensure the highest return for its advertising clients.
America On-Line holds a special place in Internet lore. As the World Wide Web took off in the late 1990s, AOL provided an on-ramp that introduced the digital world to millions of people willing to pay $19.95 per month for Internet access. AOL grew and grew until it gobbled up Time-Warner for $165 billion at the peak of the dot-com boom in early 2000. Just after completing what remains to this day the largest acquisition in history, it all came crashing down.
Today’s AOL Inc. is a much different animal. The company, which was acquired by Verizon Communications last year for $4.4 billion, is primarily an advertising platform. It serves about 500 million people per year, and is expected to gross more than $1 billion in advertising revenue for 2016 (according to eMarketer).
And like most ad-tech firms, AOL is awash in data and servers. AOL processes tens of billions of transactions per day on behalf of thousands of clients, who rely on it to serve the right advertisement to the right person at the right time. AOL’s data universe measures in the tens of petabytes, while its infrastructure spans many thousands of nodes.
Getting the personnel and products in place to make this business flow is neither easy nor trivial.
As the CTO of AOL Platforms, Seth Demsey’s job is to oversee the research, product development, and engineering. The former Google and Microsoft executive tells Datanami that getting the best out of man and machine requires a balanced approach.
“The general philosophy is tooling complements our needs, as far as computing and research go,” Demsey says. “For us, the technology is a means than an end, and we effectively do whatever we need do to solve the problems at the sale and latency that we want to.”
As you might imagine, AOL uses all the latest big data tech, such as Hadoop, Spark, and Kafka, to power its ad tech business. It also runs a lot of its own proprietary code, such as its Streaming Application Framework (SAF), which it developed before Storm and Spark were stable enough to base a business on.
“We love standing on the shoulders of giants and using open source or leveraging the vitality of communities for certain projects that are going on,” Demsey continues. “But if there’s stuff that’s going on that doesn’t exist, we build it from scratch.”
Finding the right folks to code and operate the machines is a different matter entirely. Rob Luenberger, who’s the chief scientist and senior vice president in AOL Platforms R&D department, says the company sources talent from unusual places.
“Some of our most recent hires have come out of biology and in particular genetics, where you deal with a lot of data and there’s data quality and modeling issues,” Luenberger says. “Some of the same challenges that come up, such as worrying about having too many models and having something be good by luck that you need to worry about.”
The AOL Platforms team has recently hired people with backgrounds in neuroscience, as well as finance.