Data Sharing Key to AT&T's AI Push

SDN, AI and machine learning are changing how AT&T manages its network, but breaking down silos and empowering all its employees to be data scientists has been the real key to its transformation.

Sarah Thomas, Director, Women in Comms

June 15, 2017

4 Min Read
Data Sharing Key to AT&T's AI Push

SDN, artificial intelligence (AI) and machine learning (ML) are all making a big difference in AT&T's network operations, but what has been the biggest game changer for the communication service provider is really a lot simpler than that -- learning how to share data.

While the algorithms have greatly improved over the years, the real challenge of AI and ML -- technologies that underpin AT&T Inc. (NYSE: T)'s software-defined network -- was gaining access to the right data and having the ability to act on it, according to Chris Volinsky, AT&T's assistant vice president of Big Data Research. He says that necessitated a change in culture at AT&T to one that was powered by data and in which silos between divisions came down to allow for data sharing. (See AI Key to Telcos' Digital Transformation – Survey and SDN + AI: A Powerful Combo for Better Networks.)

"With the big-data revolution over the last five years, enterprises in AT&T realize the value of unlocking data and making it available for analysis," he says. "In my early career, I had to beat down doors for access to data. It'd take months of escalations. Now there's a real data-powered culture in the company whereby people realize the value in letting others have access to data and the benefit to the company's efficiency and customer experience."

He calls this the perfect storm of having more data to analyze, improved algorithms to analyze it and an increased ability to act on it. Volinsky is referring to AT&T's end customers opting in to share data, but more so to what's going on internally. AT&T has to have a data-driven justification for every business decision it makes. That means it needs its entire staff to be able to analyze data. Volinsky says this has required both an internal shift in mindset and more training of its employees to make everyone a data scientist.

"In order to get value out of large data sets, you had to hire a statistician from [AT&T] Labs to consult," he says. "Now, so many more people in the company have expertise through skills pivot or AT&T university that the people with subject expertise can analyze their own data. The work is being distributed around company to accelerate the learning we can get."

For more on big data analysis and artificial intelligence, visit the dedicated analytics content page here on Light Reading.

The willingness to share data across business divisions is also a relatively new phenomenon. Volinsky says it's been a slow cultural change over the past five to ten years, but it's accelerated in the last three to four driven by the move to open source.

While many in the industry have mixed feelings on open source, AT&T has embraced it and actively contributes and draws from the open-source community. The mindset also extends to its internal use of data, Volinsky says. "There's a general culture and understanding that it's worthwhile to open your data to have others explore, join with their data set, add value to it and weigh in on how to analyze it," he adds. (See AT&T's Chris Rice on Open Source & Standards, OPNFV Summit Comes at Critical Time, AT&T's Donovan: Resistance to Change Is Futile and Open Source an 'Overrated Necessity,' Says PCCW.)

Coupled with SDN, AT&T is able to use the data it collects from networks in real time to automate network processes, self-correct network problems and detect anomalies in the network before they affect the end user. One example he offers is a proactive care model that AT&T built for its U-verse customers. By analyzing customer data, AT&T learned that many of the customers were calling in about problems that could be solved by rebooting their modems. Its employees began using the algorithms to predict where problems might arise and reboot those modems overnight as to not impact service. Volinsky says they had 30% fewer calls into customer care and 30% fewer dispatches needed the following month.

"Every call to care that we prevent saves a certain amount of money," he says. "Every time we prevent a truck roll, that saves money. These are real benefits that are gotten through smart data science."

— Sarah Thomas, Circle me on Google+ Follow me on TwitterVisit my LinkedIn profile, Director, Women in Comms

About the Author(s)

Sarah Thomas

Director, Women in Comms

Sarah Thomas's love affair with communications began in 2003 when she bought her first cellphone, a pink RAZR, which she duly "bedazzled" with the help of superglue and her dad.

She joined the editorial staff at Light Reading in 2010 and has been covering mobile technologies ever since. Sarah got her start covering telecom in 2007 at Telephony, later Connected Planet, may it rest in peace. Her non-telecom work experience includes a brief foray into public relations at Fleishman-Hillard (her cussin' upset the clients) and a hodge-podge of internships, including spells at Ingram's (Kansas City's business magazine), American Spa magazine (where she was Chief Hot-Tub Correspondent), and the tweens' quiz bible, QuizFest, in NYC.

As Editorial Operations Director, a role she took on in January 2015, Sarah is responsible for the day-to-day management of the non-news content elements on Light Reading.

Sarah received her Bachelor's in Journalism from the University of Missouri-Columbia. She lives in Chicago with her 3DTV, her iPad and a drawer full of smartphone cords.

Away from the world of telecom journalism, Sarah likes to dabble in monster truck racing, becoming part of Team Bigfoot in 2009.

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