Data Was Often Created as a Byproduct Unfit for Today’s Diverse Uses — Mastercard Fellow of Data and AI

(US & Canada) JoAnn Stonier, Fellow of Data and AI at Mastercard, speaks with Ramon Chen, Chief Product Officer at Acceldata, in a video interview about the origin of the role of a Mastercard Fellow, how organizations tackle the risk aspect of generative AI (GenAI), and the evolution of data quality and data management with the new AI discipline.

Mastercard Inc. is an American multinational payment card services corporation that offers a range of payment transaction processing and other related payment services.

Shedding light on the origins of her role as Data and AI Fellow, Stonier shares that the idea of fellowship was developed by Mastercard’s board of directors, CEO, and senior leadership.

Throughout her career, a significant part of the roles she played involved building external relationships. These included monitoring market trends, engaging with regulators and organizations to understand where the marketplace was going, and incorporating those into product development and effective risk management.

After the emergence of GenAI in 2022, much of 2023 went into refining the strategy in collaboration with the board, says Stonier. Consequently, it became clear her role needed to shift outward to anticipate future trends.

As a result, Stonier was appointed as a Data and AI Fellow, and the role allows her to dedicate her attention to activities outside of the firm and bring back information to the company. In this role, she connects with external organizations such as the World Economic Forum (WEF), the Organisation for Economic Co-operation and Development (OECD), the United Nations (UN), and various policy and advisory bodies. This helps in staying ahead of changes and guides the organization in adapting and leading in data and AI.

Delving further, Stonier says that in her position, she gets to be a part of conversations where organizations discuss balancing risks related to AI, GenAI, and innovation. Emphasizing the risk aspect, she says that everyone was focused on GenAI since its inception in 2022 but then quickly realized the risks attached to the new technology.

Now, while organizations are still drawn to innovation, they are focusing more on their internal processes with GenAI, says Stonier. Whether it is coding, customer service, legal, HR, or marketing, organizations are doing it to train their employees, get better at using tools, and understand vendors.

Organizations are investing in governance, data management, and data quality practices to get better internally, says Stonier. Additionally, products are also being extended in the marketplace to be more interactive with customers, adding chatbots, advancing interactive dashboards for customers, increasing customization, and more.

However, Stonier affirms that a real change in innovation is anticipated, and that would be a step change. She states that it can be seen in AI platform companies but will be more evident in the rest of the industry in the coming two years.

Having said that, Stonier believes that firms are now cognizant of the various types of risks, whether it is data, bias, modeling, or ensuring that the outcomes are actually applicable to the customers, regardless of the industry.

When asked how data quality and data management evolve with the new AI discipline, Stonier says that the data being created and used was not necessarily generated to be put to the current variety of uses.

She maintains that often, data was generated as a byproduct of something else. Therefore, while the quality of that information may have been fine for its primary purpose, it is questionable now, as the same data is being reused for financial purposes, health studies, communication, climate, or efficiency.

Given this scenario, one has the opportunity now to improve the quality of data for it to fit multiple purposes. Therefore, many organizations are paying deeper attention to the quality of information generated or data purchased to ensure it is fit for use.

Stonier further states that although no one would always put in bad information, information is like Swiss cheese that is missing things or has too much information on one aspect and not enough on another. This leads to biases over time, and therefore, it is critical to have quality input that reflects the complete needs of the users, which in turn reflects in the products and solutions.

From the data management perspective, she believes that AI can make data management easier. Also, AI is the reason why data management is necessary. To understand the relationship between different data elements, one has to understand their veracity, accuracy, completeness, and consistency while ensuring that the definitions are right throughout the ecosystem.

This requires more transparency, and AI can help with that, she adds. Many organizations have published data and AI principles, and transparency is one of them, and data observability is another. Furthermore, data observability helps with the transparency requirement, says Stonier.

Circling back, she feels excited about putting data skills into motion with AI and GenAI. As organizations leapfrog into the future of innovation, Stonier expects the data that is being used to apply to the problems solved and the solutions created.

CDO Magazine appreciates JoAnn Stonier for sharing her insights with our global community.