Big Data Analytics at T-Mobile

A key application of big data analytics involves understanding consumer behaviors from sources such as click streams, sensors, text, and other forms of non-traditional data. In this workshop we will discuss the use of NoSQL analytics to extract value from text data captured during customer interactions for the purpose of increasing subscriber retention. We demonstrate how the Not-Only-SQL approach leveraging a combination of SQL and MapReduce maximizes efficiency and productivity of data scientists within T-Mobile through detailed examination of a specific case study in semi-structured text analytics. We also show the importance of advanced data visualization techniques when performing big data analytics. The overall architecture for optimizing the use of Hadoop alongside traditional and non-traditional platforms is presented along with our future direction in big data analytics.

Learn about combining SQL and MapReduce for semi-structured text analytics.
Learn about optimizing a big data analytics ecosystem with a unified data architecture.
Learn about matching skill sets and technologies to maximize productivity for data scientists within an enterprise analytics team.

Stephen Brobst is the CTO at Teradata. Previously, he founded three start-up companies (one acquired by IBM, one acquired by NCR, and one IPO). Stephen performed his Masters and PhD research at the MIT in the area of high performance parallel processing. He also completed an MBA with joint course and thesis work at the Harvard Business School and the MIT Sloan School of Management. Stephen was appointed to Barack Obama's Presidential Council of Advisors on Science and Technology (PCAST) where he focused on Big Data strategy across all federal agencies.

Jesse Lynch has spent the last 10+ years developing Information Management and BI/Analytics solutions in the Telecom and Financials industries. He is currently a Director of Business Solutions in IT at T-Mobile, working with areas such as Marketing, Channel Management, Sales and Care to meet ever increasing needs for data and analytics. Recent and ongoing efforts include developing additional capabilities in the area of Predictive Analytics with the implementation of SAS and ‘R’ capable systems, the introduction of Teradata Aster and Hadoop for data exploration, and continued roadmap planning for future Data Warehouse Ecosystem improvements.