As McDonald’s Chief Data Officer, Craig is helping to define and infuse data across the global enterprise. He is creating the roadmap to identify the team, technology, process and culture change required for enabling enterprise data transformation, establishing best-in-class data strategy and governance and evangelizing these changes throughout McDonald’s.
Most recently, Craig was the Director, Global Data Insights & Analytics within Ford Motor Company. In this role, Craig provided the executive office, product development, manufacturing, global supply chain, finance, human resources and business units with data insights to enable better decision making for the enterprise.
Craig has more than 25 years of experience working in corporate strategy and data analytics as well as manufacturing, construction, IT, supply chain and management consulting. He has a proven track record of driving value creation, growth and business performance. Prior to Ford, Craig was the first Senior Vice President, Data Analytics at McKesson Corporation, leading analytics for the Fortune 5 company worldwide.
Craig also served as the first Chief of Analytics for Caterpillar. Leadership at Caterpillar included roles in strategy, IT, 6 Sigma, and supply chain. Prior to that, Craig was part of Deloitte Consulting’s Strategy and Operations practice. Craig also served as a Lieutenant in the U.S. Navy Civil Engineer Corps.
Craig serves on professional councils focused on advanced analytics and digital transformation. He is an advocate and mentor for the advancement of STEM programs across the globe, including USFIRST Robotics. Craig has an MBA from the University of Illinois, a BS in Industrial Engineering from Purdue and is a licensed professional engineer. Craig and his family currently reside in Michigan.
In our first of two reverse panels, Chief Data Officers and analytics leaders from some of the world’s most respected brands answer startup executives’ questions about how they select, deliver, and build atop emerging technologies like AI and Big Data to build internal products and improve their business results.