Stuart Ogawa: Sysomos’ Social Data Tinkerer
In some ways, Stuart Ogawa’s upbringing was similar to many children’s. He grew up in California, took piano lessons, and enjoyed studying math in school. But in other ways, it was different.
At age five, he started designing circuits—perhaps not totally farfetched for a child living in Silicon Valley. But he grew tired of this hobby by age 11, so he got an amateur radio license and started communicating in Morse code around the world. During his junior high and high school years, he began experimenting with programming to bounce radio signals off of satellites.
“That’s how we’d communicate,” he says.
When it was time to start thinking about college, Ogawa knew that he didn’t want to do electrical engineering or computer science. He liked math, but he couldn’t see himself pursuing a career in that subject full-time. So, he majored in business economics at the University of California Santa Barbara.
Still, he didn’t stray too far from math and programming. In fact, he says that all of his electives were in these fields.
“I considered those kind hobbies,” he says. “They were still very curious areas.”
After graduating from U.C. Santa Barbara, Ogawa went to work for Arthur Young & Co—an accounting firm that later merged with Ernst & Whinney to form Ernst & Young. But after his employer realized that he knew how to program, Ogawa was tasked with writing script and became a senior technology consultant.
“I never really looked back,” he says.
Indeed, he hasn’t. After working at Arthur Young & Co. for three years, he went on to hold a variety of technological roles. He founded an SaaS financial transaction platform company called AutoEntry Online Inc, where he served as president. He went on to obtain his M.B.A. in business from Santa Clara University and served as VP and CTO of Bay View Capital Corporation, a financial services organization. After four years there, he decided to try launching a business again and cofounded EXP Systems Inc, an SaaS social network platform provider. He served as CTO for two other companies over the next three years—CFares Inc and AngelPoints Inc—and then became General Manager and VP of Applications for Teradata. He then left Teradata after two years to serve as VP of Business Intelligence at Yahoo, where he worked on the Microsoft-Yahoo search alliance. During his time at Yahoo, he also completed the advanced leadership program at Stanford’s graduate school of business.
Now as EVP of Technology and CTO of Sysomos, Ogawa leads the social analytics software provider’s research lab, product management, infrastructure, and application and computing engineering teams. His role requires him to work with a ton of data. In fact, Ogawa says that Sysomos processes more than one billion conversations a day in real-time. He also says that it has more than one trillion conversations in its data store and can link about 1.5 trillion relationship connections.
There are two parts to the Sysomos platform. First, Ogawa explains, there are the individual SaaS applications, which offer customers different solutions. Take Gaze, for example. The solution allows customers to analyze images posted on social media and curate ones featuring their brand. There’s also Influence, which helps companies identify and engage relevant and key influencers. Customers can even opt to integrate four key solutions together with SET. Second, he says, there’s the cloud computing system. This system relies on big data open sources—like Hadoop, Spark, and Titan—to process, store, and find connections between data points. Hadoop allows Sysomos to process and store its data, and its SolrCloud enables Sysomos to index big data so that it can find and retrieve it later. Spark serves as Sysomos’ processing engine and enables the platform to transform data from one format to another while running analyses. Finally, Titan helps Sysomos map different relationships between people.
Through the technology, Sysomos is able to provide clients with insights on consumers’ comments, check-in locations, interest categories, images, and more. But it doesn’t just combine these insights into pretty charts and graphs. “That’s fast-food analytics,” Ogawa says. Instead, Sysomos uses the technology to make data-driven recommendations and help customers answer business-oriented questions.
“It’s like driving a Ferrari through the rear-view mirror,” Ogawa says in regards to these “fast-food” analytics providers. “You don’t care what’s in back of you. In a Ferrari, you drive fast moving forward.”
However, Ogawa knows that social values change over time. So, he says that Sysomos’ data scientists do A/B testing to modify the platform’s algorithms when necessary. He also says that the company can adjust the algorithms for specific verticals.
And while all of this technical jargon may seem like layman’s terms to Ogawa, he knows that this isn’t the case for everyone. To ensure that the benefits of the product are communicated effectively, Ogawa and his teams work closely with marketing. They invite the department to their product roadmap meetings and host weekly town hall meetings where marketers can watch demos of new features and functionalities. In addition, marketing works with product management to assist with testing and collecting information from the field.
“It’s show-and-tell time,” he says in regards to the town hall meetings.
But when he’s not hosting town halls or working with marketing, Ogawa can be found consulting, doing primary research, and growing the organization’s teams. And during his leisure time, he enjoys a lot of the same activities and tinkering he did as a child. He’s back to taking piano lessons along with his children and takes pleasure in constructing LEGO builds with them and filming the process in stop-motion animation.
“We have a LEGO studio in our house,” he says, “and we built one room just for LEGO builds and shooting.”
He also continues to play with hardware—explaining that he and his buddies recently took apart a few iPhones to build an intelligent and economical hearing aid.
Unfortunately for many organizations, finding data experts like Ogawa is no easy task. While the demand for data scientists is high, he says, it far outstrips demand. But if companies are lucky enough to come across a few qualified candidates, Ogawa advises them to hire someone with the following criteria: experience doing primary research, an understanding of how to apply data to solve business problems, and a solid foundation in programming and applying algorithms to various data systems. He says there’s a simpler element to the role, too—being able to articulate data findings in everyday language so that marketers and the rest of the organization can act.
Indeed, finding strong data scientists can be like sifting through big data: You have to dig deep to find what you truly need.