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Harveer Singh, Chief Data Officer, Truist

Describe your career to date

I am an esteemed technology executive renowned for my expertise in driving data and digital transformations within the financial services industry. With a distinguished career spanning leadership roles at Western Union, Ernst & Young, Deloitte, and Accenture, I have consistently excelled in guiding organizations through technology challenges and bridging the gap between business and IT.  

My contributions have earned me accolades, including recognition as one of the top 50 American Asians in Business by the American Asian Business Council in 2022 and induction into the Forbes Technology Council.  

I have been a speaker at prominent conferences like Garner, MIT, AWS, and Snowflake, and I am an author with my book recognized as one of Amazon’s top 10 short reads for Business and Technology in 2022.  

As the Chief Data Office for Truist’s Digital, Retail & Small Business Banking, I am ushering the bank into next generation platforms and experiences. I am dedicated to advancing financial inclusion and empowering individuals striving for economic stability and my commitment to driving positive change is evident through his role in promoting access to financial services. 

My data accomplishments have garnered significant recognition in the press, with notable features in publications like Profile Magazine, Fintech Magazine, and CIO Review, among others. 

How are you developing the data literacy of your organization, including the skills of your data teams and of your business stakeholders? 

As the Chief Data Officer in the financial services industry, developing data literacy across our organization is paramount to harnessing the full potential of data-driven decision-making. We employ a multifaceted approach tailored to both our data teams and business stakeholders.  

For our data teams, we prioritize continuous skill development through specialized training programs focusing on advanced analytics, machine learning, and data visualization techniques relevant to financial services. These programs are designed to deepen their expertise and keep them abreast of emerging trends and technologies in the industry. Hands-on projects allow them to apply their knowledge in real-world scenarios, fostering practical experience and problem-solving skills.  

Simultaneously, we recognize the critical role of business stakeholders in leveraging data effectively. We provide tailored workshops and educational sessions aimed at enhancing their understanding of data interpretation, analytics, and the impact on financial decision-making. By promoting a data-literate culture, we empower stakeholders to make informed decisions based on data insights, ultimately driving business growth and mitigating risks.  

Additionally, fostering collaboration between data teams and business stakeholders is integral to our strategy. Cross-functional projects and knowledge-sharing initiatives encourage mutual learning and understanding, bridging the gap between technical expertise and business acumen. This collaboration ensures that data initiatives align with strategic business objectives, enhancing their relevance and impact. Continuous evaluation and measurement of our data literacy initiatives allow us to gauge their effectiveness and make necessary adjustments. Key performance indicators such as improved data-driven decision-making, increased adoption of analytics tools, and enhanced cross-functional collaboration serve as metrics of success.  

Our approach to developing data literacy in the financial services industry revolves around comprehensive training, hands-on experience, collaboration, and continuous evaluation. By investing in the skills of both our data teams and business stakeholders, we strive to cultivate a data-driven culture that drives innovation, fosters growth, and ensures competitiveness in the dynamic landscape of financial services. 

​​What role do you play in building and delivering conventional artificial intelligence solutions, including machine learning models? Are you also involved in your organization’s adoption of generative AI?  

As a Chief Data Officer, my role in building and delivering conventional artificial solutions, including machine learning models, involves several key responsibilities: 

Strategic planning: I oversee the strategic direction of our organization’s artificial intelligence (AI) initiatives, aligning them with business goals. This includes identifying opportunities where AI can drive value and improve efficiency. 

Data governance: Ensuring the quality, security, and proper usage of data is crucial. I establish data governance policies and procedures to maintain integrity throughout the AI model development process. 

Data acquisition and integration: I oversee the acquisition and integration of relevant data sources needed for building machine learning models. This might involve working with data engineers to collect and prepare data for analysis. 

Model development: While not always directly involved in the technical aspects, I collaborate closely with data scientists and engineers to guide the development of machine learning models. This includes defining success metrics and ensuring the models align with ethical and regulatory standards. 

Performance monitoring and optimization: After deployment, I oversee the monitoring of model performance, making sure they continue to meet business objectives. This involves implementing feedback loops for continuous improvement. 

Ethical considerations: I ensure that ethical considerations, such as fairness and transparency, are embedded in our AI solutions. This might involve assessing and mitigating biases in the data and models. 

Stakeholder communication: I act as a bridge between technical teams and executive leadership, translating technical details into business insights. I communicate the value of AI solutions and their impact on the organization.  

Overall, my role is to lead the strategic vision, ensure data integrity and governance, collaborate on model development, monitor performance, and communicate the value and impact of AI solutions within our organization. 

How are you preparing your organization for AI adoption and change management? 

As Chief Data Officer in the financial services and banking sector, I spearhead efforts to prepare our organization for AI adoption and change management through a multifaceted approach.  

Firstly, we prioritize education and training to ensure our teams possess the necessary skills and knowledge to leverage AI effectively. This includes comprehensive training programs on AI concepts, tools, and methodologies, supplemented by hands-on workshops and certifications.  

Additionally, we foster collaboration across departments and functions to facilitate cross-functional alignment and knowledge sharing. Creating forums, committees, and working groups enables stakeholders to exchange ideas, share best practices, and collaborate on AI initiatives. Robust data governance and compliance frameworks are foundational to our AI adoption strategy. We establish policies and procedures to ensure the quality, security, and ethical use of data in AI applications, aligning with regulatory requirements in the financial services industry.  

Change management strategies are vital for addressing potential resistance to AI adoption and communicating the benefits to stakeholders. We develop tailored change management plans that highlight the value of AI, provide support and resources, and address concerns proactively. Pilot projects and proof of concepts allow us to demonstrate the value of AI in real-world scenarios, identify challenges, and refine our approach before scaling up. Continuous monitoring and evaluation, supported by key performance indicators, enable us to track progress, gather feedback, and make necessary adjustments to our AI adoption strategy.  

Overall, our approach to preparing for AI adoption and change management prioritizes education, collaboration, data governance, change management strategies, pilot projects, and continuous monitoring and evaluation. By taking a strategic and comprehensive approach, we aim to successfully integrate AI into our operations and drive innovation in the financial services and banking sector. 

Harveer Singh
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