Powered by machine learning, AT&T is undertaking a data transformation journey that is helping business units identify opportunities to use information in new ways.

Since its founding 144 years ago, AT&T has reinvented itself many times to harness historic disruptive innovations such as the transistor, the communication satellite, and more recently, the solar cell. Today, the global technology, media, and telecommunications giant is reinventing itself again—this time as a pioneer in the use of machine learning (ML) to reimagine the way it finds, organizes, and uses data.

“One of the things we wanted to do was automate some of the routine cleansing and aggregation tasks that data scientists have to perform so they could focus on more sophisticated work,” says Kate Hopkins, vice president of data platforms in AT&T’s Chief Data Office. Likewise, the company wanted to develop a way to democratize meaningful data to the extent consistent with privacy, security, and other data use policies, making it more broadly available to qualified personnel across the enterprise.

These efforts, Hopkins says, have already borne fruit. New tools have shrunk the time to market required to go from prototype to full-scale production for ML models. These models have had dramatic results, such as blocking 6.5 billion robocalls to customers, deterring fraud in AT&T stores, and making technician visits to customer homes more efficient.

AT&T started its data transformation journey in 2013 when it began aggregating large volumes of customer and operational data in data lakes. In 2017, the company created a chief data office with the goal of leveraging these rapidly growing data stores for hyper-automation, AI, and ML.

The ongoing work of achieving these goals has presented several significant challenges. First, in a company as large as AT&T, it was sometimes difficult to find and access potentially valuable data residing in legacy systems and databases. And even when data scientists eventually found such data, they occasionally struggled to understand it since it was often labeled inconsistently and offered no discernable context or meaning. Finally, there was a formidable latency challenge across all data systems that, left unaddressed, would stymie the real-time data needs of ML models.

To address these challenges, the chief data office developed the Amp platform. Amp enables a culture of technology and data sharing, reusability, and extensibility at AT&T. Pari Pandya, director of technology and project manager for Amp, says that what began a few years ago as an internal online marketplace (aggregating microservices, APIs, chatbots, designs, etc.) for accelerating automation has evolved into a single, powerful source of data truth for systems and users. As data flows through multiple systems and processes, its definitions change. Amp not only finds legacy system data but also uses metadata to ascribe meaning to it, and provides a clear lineage to help users better understand the data. “It serves as a business intelligence platform that provides meaningful data as well as analytic and visualization tools that empower business teams, strategists, and product developers to leverage data in more advanced ways and share insights through data communities,” Pandya says.

To meet the challenge of latency, AT&T is on a multiyear journey to move some of its data and tools to the public cloud. Working closely with cyber teams to ensure data and IP security, the company is leveraging the cloud’s ability to scale up compute power as needed. The cloud’s power is helping create the real-time access that ML—as well as enterprise stakeholders and customers—require. Unlimited access to compute on demand through the cloud and the availability of business-ready data is accelerating the journey.

AT&T leaders recognize the immense challenges and advantages of empowering teams with data, particularly across business entities. “The focus for 2021 and beyond is not only to federate data across business verticals but also to maintain centralized management and governance via the Amp platform,” says Andy Markus, AT&T’s chief data officer.

Amp will be the centralized metadata repository that will directly enable the findability and relevancy of data assets, Markus says. “Our governance mission is focused on guardrails and guidelines that enable speed and agility, as opposed to gates that may slow down users. The vision of fast and free data access with improved findability and successful self-service access will only be possible with governed data and collaboration across these entities,” he says.

Hopkins notes that AT&T’s data transformation journey has yielded another welcome benefit. “The business units have become much more knowledgeable about data science and are identifying opportunities to use data in new ways. Across the board they’re requesting much more mature and sophisticated data,” she says, adding that “being able to democratize data and make the process transparent across the enterprise can deliver exponential payback.”