they share with, and how influential they are to power the experience we provide to our users,” says Meattle. “Aggregated data points include: influencer, influencee, level of influence, intent, topics of interest (based on proprietary [natural language processing], semantic, etc. analysis of shared content) at scale.”
“Shareaholic reaches more than 270 million people each month through Web browser extensions, open platform APIs [application programming interfaces], and one of the largest and fastest growing networks of content publishers—we’re the very definition of ‘big data,’” he says.
—On his lessons learned at Lookery and Compete:
“So, so many! I have been so fortunate to have been part of both those great companies,” he says. “Both Compete and Lookery deal or dealt with big data at scale. Shareaholic is already well past both companies in terms of data scale, but having been in the trenches dealing with big data (before it was in vogue) and learning from some of the very best informed the technology and business vision at Shareaholic and also enabled us to move a lot faster as a result.”
He adds, “How many companies can boast of a 1:25,000,000 employee to user-base ratio?”
—On how to help consumers cut through the chaos of competing channels, screens, and noise to discover relevant content:
“We believe content discovery is fundamentally broken for some of the very reasons you have outlined,” he says. “In addition to sharing, we’re aiming to make content discovery and consumption on the Web a simple, delightful, and elegant experience for readers. There is a tremendous amount of room to innovate. We see a not-too-distant future that is less chaotic and less noisy where you consume content on your own terms on your favorite screen.”
—On what comes after search and social on the Web:
“Lines are blurred between search, social, and what’s next, but generally speaking I see personalization at scale,” he says. That will be “enabled by data-centric companies that leverage advances in…being able to process and make useful sense of loads and loads of data efficiently and quickly.” In short, the dream of harnessing big data for Web personalization seems very much alive.
