Ronke Ekwensi is Global Chief Data Officer at Applied Materials, where she leads enterprise data strategy to advance data maturity and enable scalable analytics and AI across the organization. With more than 25 years of cross-industry experience, Ronke has built a career focused on driving transformation through data governance, strategy, and workforce enablement.
At Applied Materials, Ronke is responsible for strengthening the company’s data foundation through a comprehensive data product strategy designed to translate complex technology into measurable business value. She has led the implementation of enterprise data governance capabilities, including data quality and metadata management, and works closely with ERP transformation teams to ensure data readiness as the organization transitions to SAP S/4HANA.
Prior to this, Ronke served as Vice President of Data & AI Education at T-Mobile, where she led enterprise-wide initiatives to build an AI-ready workforce. She developed data and AI literacy programs, partnered with executives to embed AI into business processes, and helped scale responsible AI adoption. Ronke also served as Chief Data Officer at T-Mobile, where she led the modernization of data platforms, including the migration from Hadoop to Snowflake.
Earlier in her career, Ronke held executive leadership roles in data management and governance, including Vice President of Data Management & Governance at Prudential Financial.
Ronke has previously been recognized in the DataIQ 100 North America and brings a multidisciplinary perspective shaped by her academic background in law, policy, and business.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
“Effective data and AI leadership requires far more than technical expertise; it demands enterprise systems thinking. Data and AI leaders recognize that AI cannot scale on fragmented pipelines or poorly understood data, and they intentionally build foundations that align technology, process, and organizational design.
“These leaders must have a relentless business-first orientation. The most effective leaders anchor data strategy in real business outcomes, not tools or architectures. They start with high-value use cases, partner deeply with executives, and organize delivery around measurable impact. This mindset distinguishes enterprise leaders from technologists and positions data and AI as business capabilities rather than technical experiments.
“Effective data leaders excel at executive influence and coalition building and secure sustained sponsorship at the highest levels of the organization. Another defining trait of these leaders is a strong product mindset to move the organization away from ad hoc data delivery toward well-defined data products with clear owners, consumers, quality standards, and lifecycle management.
“The most effective leaders understand how to leverage transformation windows. These leaders ensure data is placed squarely in the critical path of transformation, using timing as strategic leverage rather than deferring foundational work. They understand the power of modern AI while remaining clear-eyed about its limitations, risks, and dependencies on data quality and semantics.
“As a data leader, I find that at its highest level, data leadership is translational: the ability to translate technology into business value, governance into advantage, AI potential into practical capability, and executive ambition into structured, executable roadmaps.”
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
“A data strategy lives in service of a business strategy; focus on solving business problems, not data problems.”