Ataccama > Case Studies > T-Mobile's Data-at-Scale Initiative: A Case Study on Enhanced Data Management and Security

T-Mobile's Data-at-Scale Initiative: A Case Study on Enhanced Data Management and Security

Technology Category

Analytics & Modeling - Machine Learning
Networks & Connectivity - 5G

Applicable Industries

Cement
Telecommunications

Applicable Functions

Quality Assurance

Use Cases

Inventory Management
Time Sensitive Networking

Services

System Integration
Testing & Certification

About The Customer

T-Mobile is a globally recognized S&P 100 brand and one of the largest wireless carriers in the US, supporting over 116 million subscribers. The company leads its market through superior customer experience and several high-profile mergers and acquisitions, including its 2020 merger with rival US carrier, Sprint. T-Mobile's successes have led to the organization absorbing and handling more customers and their data than ever before, within an increasingly complex infrastructure. T-Mobile employees are also its owners, via annual stock grants, which translates into a real responsibility that its teams feel to support the business and its customers.

The Challenge

T-Mobile, one of the largest wireless carriers in the US with over 116 million subscribers, was facing a significant challenge in managing and securing its vast data landscape. The company's growth, including its 2020 merger with Sprint, meant it was handling more customer data than ever before within an increasingly complex infrastructure. This complexity made it a target for cyber attacks, as evidenced by a data breach in 2021 that directly impacted its share price. This incident catalyzed T-Mobile's efforts to redefine its data management at scale. The company sought an enterprise-wide data transformation to secure its data, maintain regulatory compliance, save time and reduce costs, and accurately predict customer behavior and intention. The challenge was to scan an estimated 5,000 apps and 22,000 databases continuously, with 8 petabytes of data.

The Solution

T-Mobile turned to long-time partner Ataccama for expert advice and solutions to support its mission. The data governance team built a 'Data Scanning at Scale' initiative, which was delivered using Ataccama's unified platform for automated data quality, master data management, and metadata management: Ataccama ONE. This platform was designed to work seamlessly in complex enterprise data governance contexts, such as T-Mobile's. The solution involved scanning multiple systems simultaneously, creating new data labels to automatically classify new data sources, improving future scan success with accepted/rejected status, seamless system integration, and discovering unknown applications and data stores for routine scans. A 24-hour proof of concept project was run, with Ataccama's solution ultimately selected for its ability to scan large volumes of data quickly and efficiently, its total cost of ownership, integration flexibility, and future-proofing potential.

Operational Impact

The 'Data Scanning at Scale' initiative resulted in an automated, self-improving, closed-loop solution that onboards data sources, classifies data with automated rules and advanced machine learning, integrates with ticketing systems to create data remediation tasks, and provides reporting. Over 22,000 databases and 5,000 applications with over 8 PB of data were scanned. The data governance team now uses Ataccama ONE for continuous scanning systems, classifying data, and securing newly added sensitive assets. The solution also provides an always-on automated data scanning, classification, and protection for existing and new data sources, and a solution for Data Mesh metadata gathering and disciplined, comprehensive data management. This has put T-Mobile in a stronger position to protect itself against third-party attacks, secure its data, comply with industry standards, and turn its data into a competitive advantage to better serve its customers.

Quantitative Benefit

$350 million in cost-avoidance and consumer protection by eliminating the risk of PII leakage
$50 million in savings through data reuse and removing redundant systems and databases
$25 million in savings by reducing data preparation times for AI teams