Top 100 D&A Global Innovators: Chun Schiros, PhD

In this week's exclusive interview, Chun Schiros PhD talks about data integrity, scalability, and data and analytics talent

In this this interview, the first of our extended interviews with our Top 100 Global Innovators in Data and Analytics, Regions Bank Head of Enterprise Data Science Chun Schiros PhD discusses the challenges posed by data integrity, process scalability, and talent acquisition, while also highlighting the indispensable role of responsible artificial intelligence in today’s data-driven world.

Don't miss the opportunity to glean wisdom from one of the industry's finest as we kick off our series of extended interviews with data and analytics leaders. Stay tuned for more in-depth conversations and expert insights released weekly.

In a professional context, what achievements are you most proud of in the past year, and why?

Our team had a remarkable 2022 filled with pivotal breakthroughs. We focused on building the best team, executing with excellence, and promoting data-driven innovation. We built a dynamic group of data professionals and fostered a culture of learning and innovation through educational sessions and advanced training. This led to cross-functional collaboration, breaking down silos and empowering teams to utilize data effectively. We improved our analytics capabilities, delivering reliable results and driving growth and savings through ML/AI-powered insights, while ensuring proactive model risk management practices. Additionally, we automated routine data tasks to enhance efficiency. 

I was honored to be recognized as the Data Leader of the Year, underscoring the impact of our team not only within our organization but also in the broader data community. These achievements have filled me with pride and a deep sense of responsibility to continue collaborating, innovating, and executing effectively.

What do you think are some of the biggest challenges facing data and analytics leaders today? And how do you think they can be overcome?

Data and analytics is a rapidly evolving field, and there are shared challenges that emerge consistently across the industry. Those challenges fall into three categories: data, process, and people. Addressing these challenges requires a comprehensive data strategy. 

Data-related challenges involve ensuring data quality, integrity, accuracy, and security. Process-related challenges include maintaining the scalability of data operations and managing the increasing size and complexity of data and algorithms. People-related challenges highlight the need to attract and retain skilled talent, foster a culture shift toward data-driven decision-making, and promote continuous learning.

At the same time, the rapid development of artificial intelligence in recent months has become a powerful driving force behind innovations and growth. But the emerging area of responsible AI brings its own unique set of challenges that include ensuring transparency and accountability, as well as fostering public trust.

In your experience, what does it take to be a successful leader in the data and analytics space? What characteristics or skills should aspiring data leaders focus on cultivating?

Successful data leaders possess a blend of technical expertise, strategic thinking, business acumen, strong communication skills, and leadership abilities. These leaders are impact-oriented, focusing on driving tangible results and creating value through data-driven initiatives. They inspire and motivate their teams, providing guidance and fostering a collaborative and inclusive data culture. They encourage continuous learning and improvement.

I think cultivating these skills and characteristics along with a genuine passion for data and a curious mind will enable the data leaders of tomorrow to position themselves for success in a complex and rapidly evolving field.

What are you most passionate about when it comes to data and analytics? What do you think is too often overlooked or misunderstood?

I'm passionate about turning data into business value through advanced analytics. I enjoy the potential to uncover hidden patterns, insights, and correlations in data that might not be apparent through traditional methods. However, one area that could potentially be misunderstood is the complexity of the data analytics process. Each step requires different technical skills, analytical methodologies, and tools. This further highlights the necessity to attract and retain diverse and skilled talent. 

It's also important to recognize that AI and machine learning models are not magical solutions that can automatically provide accurate predictions or insights. Models are built based on data inputs, assumptions, and algorithms, which can introduce limitations and biases. Models require careful validation, testing, and ongoing monitoring to ensure their accuracy and relevance. Additionally, the interpretation of model outputs requires expertise and contextual understanding to avoid misinterpretations or overgeneralizations.

Data and analytics offer immense opportunities for innovation for organizations to create a competitive edge. So, as we continue with this path and overcome various challenges as emerge, I look forward to what the future will hold in this field.

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