What Data Governance Means for Financial Services - with Andrew Sellers of Confluent
Feb 12, 2024
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Andrew Sellers, Head of Technology Strategy at Confluent, discusses the challenges faced by financial institutions in IT costs. He emphasizes the importance of data governance, AI advancements in finance, and the role of generative AI in data management. The podcast also delves into the risks of misinformation in AI technologies and the need for human oversight.
Ensuring trustworthy and complete data is crucial for financial decision-making.
Start AI initiatives small and realistic to manage expectations and deliver measurable ROI.
Deep dives
The challenge of guaranteeing the reliability of financial institutions' data
The biggest challenge facing financial institutions is ensuring that their data is trustworthy and complete enough to make sound decisions about risk. This challenge keeps financial professionals up at night, as making decisions based on accurate and reliable data is crucial in managing risk effectively. Financial institutions are constantly adopting new technologies and data architectures to improve their decision-making capabilities. However, in the pursuit of cutting-edge technology, it is important not to overlook the importance of reliable data governance. Investing in data governance and ensuring the provenance of the data allows organizations to make better decisions, increase accessibility, and improve the overall customer experience.
The importance of starting small and realistic in AI initiatives
Financial leaders should approach AI initiatives by starting small and realistic. Rather than making large investments expecting immediate results, it is recommended to identify use cases where AI technologies can be effectively applied. For example, using generative AI tools for tasks such as document recognition or summarization, which are already well-developed capabilities, can yield tangible benefits and improve workflow efficiency. It is also advisable to pair generative AI tools with traditional models to ensure transparency and accountability in decision-making. By starting small and gradually expanding AI initiatives, financial institutions can leverage the strengths of AI technologies while managing expectations and delivering measurable ROI.
Navigating the ROI conversation and limitations of generative AI tools
When discussing ROI and generative AI tools with financial leaders, it is essential to have a clear understanding of the technology's capabilities and limitations. Generative AI tools, such as large language models (LLMs), can significantly aid data governance and management practices, particularly in tasks like cataloging and metadata generation. However, it's important to note that these tools should be used in conjunction with human oversight and expertise to ensure accuracy and mitigate the risk of relying on potentially inaccurate or biased results. Financial institutions should prioritize having reliable data in place before exploring the full potential of generative AI tools. By focusing on realistic expectations and augmenting human operators, these institutions can harness the power of generative AI to enhance data governance effectively.
Today’s guest is Andrew Sellers, Head of Technology Strategy at Confluent. Previously, he served as Chief Technology Officer at QOMPLX, a high-growth startup in cyber-risk analytics, and as a Senior Cyberspace Operations Officer, CTO, and Assistant Professor of Computer Science in the United States Air Force. Andrew joins Emerj Senior Editor Matthew DeMello on today’s podcast to talk about the high costs and tight budgets of IT. Throughout his appearance, Andrew offers advice on balancing ROI goals with the typical pitfalls of false start pilot initiatives. To access Emerj’s frameworks for AI readiness, ROI, and strategy, visit Emerj Plus at emerj.com/p1.
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