Data Leaders Who's Who: JoAnn C. Stonier
JoAnn C. Stonier
Chief Data Officer, Mastercard
JoAnn C. Stonier serves as Chief Data Officer for Mastercard, leading the organization’s data innovation efforts while navigating current and future data risks. She oversees the curation, quality, governance and management of the company’s extensive data assets. JoAnn and her team design and operationalize Mastercard’s global data strategy, guiding enterprise deployment of cutting-edge data solutions, including advanced analytics and AI, and the development of enterprise data platforms. Her leadership is integral to Mastercard’s push to deepen the strategic value it can provide its merchant, banking and government customers and cardholders through its expanding data-driven products and capabilities.
During this interview JoAnn shares insights on diversity and bias in AI:
What influenced you to pursue a career in data?
While I didn’t necessarily set out to pursue a career in data – I think data has always been around me as a raw material of innovation. My career in data has evolved over time and has always provided me with an opportunity to learn. I first encountered data early in my career when I was a technology auditor, and then when I built financial technology systems. As my career grew and changed, data became more central to my roles as a privacy lawyer and privacy officer – and now of course as a data officer for Mastercard. In so many different jobs, data provided endless opportunities for learning and for applying my knowledge to new problems to create new solutions.
JoAnn also shared her thoughts on:
Do you have any tips for women who want to gain a foothold in the industry?
What do you love about working with data?
What advice do you have for young women who want to work in data/data science?
Looking back on your career, what is the one thing you would have changed in your working environment to break bias?
How can we better support diversity in an organisation?
How do we create a supportive culture that is inclusive and diverse?
How do we get to where we need to be, as a representative data analytics community?
What are the current opportunities in the conversations around data teams, talent, and diversity?
