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A year ago, AT&T, the world’s largest telecom company by revenue, announced a partnership with AI cloud company H2O to jointly launch an artificial intelligence (AI) feature store for enterprises. This paid software platform enables data scientists, developers and engineers to discover, share and reuse machine learning (ML) features to accelerate their AI project implementations.
Since then, the feature store has become a key part of AT&T’s vision to scale its own AI efforts across the organization and “really integrate data and AI into the core of how we run the business,” Andy Markus, AT&T’s chief data officer, VentureBeat told me.
Markus, who joined AT&T in February 2020 after nearly two decades in roles at media companies such as Turner and Warner Media, said the company carries more than 543.7 petabytes of data across its global network. To manage AT&T’s data and AI at this scale, it has defined a common approach to how data is stored, managed, accessed and shared across the company.
- 1 AT&T’s “North Star” for Data and AI
- 2 Modernizing AT&T’s Data and AI Stack
- 3 AI-driven decision making is important in telecom
- 4 Democratize the ability to create AI
- 5 AT&T uses AI to solve business problems
- 6 Standardize skills in data science teams
- 7 The last big push towards data modernization
AT&T’s “North Star” for Data and AI
The company relies on its Chief Data Office (CDO), he said, to establish AT&T’s “North Star for data, analytics and AI excellence.” AT&T’s mission is to leverage, share and catalyze insights from the company’s vast data store and transform and modernize AT&T’s data platforms, data supply chain and data science ecosystem.
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In addition to the feature store, the CDO leverages a centralized data intelligence platform that provides a “single version of the truth” for each defined data product, enabling both business managers and data scientists to gain self-service access to data sets.
“There’s been a big focus on standardizing on best-in-class tools that are cloud-based,” he said. “We use advanced technologies such as H2O, Good AI, Databricks and Snowflake to deliver value to the customer and to our data science community.”
Responsible AI is also a big focus, he added. AT&T created a technology called SIFT, the system for integrating fairness and transparency in AI, and rolled it out across the company so that all models are assessed for potential bias. “The process not only detects the bias, but also guides the user through mitigating steps,” he said.
Modernizing AT&T’s Data and AI Stack
Several years ago, AT&T’s data science efforts were “a bit of the Wild West,” Markus said.
“We had a lot of different kinds of technologies, a pretty disparate ecosystem of data scientists,” he explained. “Now we have a highly connected data science community, everyone works consistently with the same advanced tools and maximizes the reuse of our data in our feature store.”
Unlike many legacy companies, AT&T has also faced a lot of legacy tech debt, he added.
“We had great things, some really smart people doing really good things, but with a non-standard technology, there were results that weren’t shared, features that couldn’t be reused,” he explained.
Over the past two years, the company has modernized to a cloud-native elastic technology. The past year included an assessment of the state of AI at AT&T, he added, which found that AI efforts have delivered the company billions of dollars in value on an annual basis — everything from revenue improvement to cost savings and efficiency processing.
“We’ve taken the company’s competence to the next level by not only working with those groups that are already doing well, but also bringing other parts of the company to the fore so they can really come to the table and take advantage of the great ML and AI functionality we’ve created,” said Markus.
AI-driven decision making is important in telecom
Today, AI-driven automation and decision making has become critical to running an efficient business in the complex and cost-intensive operational world of telecom – everything from optimized network planning, customer service and field service to protecting customers and networks.
“The pace is constantly accelerating as technology becomes more proficient at solving complex problems at the scale of AT&T and the demands of the company and our customers grow,” he said.
While the use cases of the tables could be resolved, he added, the company is now focused on next-generation challenges that continue to build on the value already created with AI.
“Tackling the more complicated issues, both from an AI and business perspective, involves a steeper curve, such as developing AI-powered products and services and creating self-healing 5G networks,” he said.
Democratize the ability to create AI
In addition to the CDO’s feature store and centralized data platform, Markus explained that AT&T is working to democratize the ability to create AI.
“We have a standard code-driven AI creation process built for the data science community,” he said. “Now we’re working to make that low-code, no-code so we can really democratize the ability to create AI, not just for the data science community, but other subject matter experts across the company.”
If AT&T’s main goal is to embed AI into the “core structure” of how the company runs its business, Markus said the second goal is to extend the functionality of AI-as-a-service.
“We want to take that code-driven process and continue with what we call the citizen data scientist,” he said. “Those are the subject matter experts in the company who can create AI for their use cases, using responsible and ethical AI, and really drive more value for the business.”
AT&T uses AI to solve business problems
That aligns with what Markus said is one of his team’s core tenets: understanding the business problem and then getting the right data in place.
“We use technology to solve business problems,” he said. “We don’t do technology for the technology – so it all starts with understanding what the problem is, how that poses a challenge to the business, so it starts almost in a consultative way.”
In a recent blog postMarkus highlighted several of AT&T’s most powerful AI use cases. Among other things, they use predictive AI models to prevent network outages by powering an end-to-end incident management platform that scans more than 52 million different network records, devices and customer circuits, and more than 1.2 trillion daily network alerts.
Another AI-driven solution that uses sampling, predictive modeling and multivariate analysis blocks nuisance robocalls by filtering through billions of daily records looking for patterns and suspicious properties.
And an AI-based fraud management tool evaluates millions of daily transactions, inspects events in milliseconds against hundreds of rules — and includes an interface that allows frontline fraud team members to write, test, and implement rules themselves.
Standardize skills in data science teams
When it comes to building successful teams, Markus said the first thing he did when he arrived at AT&T was a standardized definition of what a data scientist is.
“Things fade over time and we just weren’t really consistent,” he said.
In addition, data scientists often collaborate with the business. “Now that we’re using common technology and data, like the AI feature store, we can democratize that,” he said. That means people who are subject matter experts in fraud or network or customer service have a connection to the data science community in that part of the company.
“I’d almost call it a federated way of organizing, in a very connected way,” he said. “So we don’t duplicate work, we don’t duplicate data, we work together to solve more problems.”
The last big push towards data modernization
Markus said he is thinking about his role in terms of wearing different hats. He wears three hats at AT&T, he explained, and he’s trying to get rid of them all.
One point is to ensure that data and AI are used to deliver meaningful and significant value to AT&T. The second is about making sure that data and AI are first-class assets of the company. And the third is modernizing the company’s data and AI ecosystem.
“2023 is the last big push in that direction,” he said. “We will have most of our technology in a new, modern environment with an updated set of tools.”
And then he said, “Hopefully we can pull that hat back.”
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