Incomplete data, weak governance frameworks, and the challenge of managing rising volumes of unstructured data are threatening to undermine the effectiveness of AI models in capital markets, financial leaders have been warned.
The 2025 Data Management Summit in New York City last week, brought together chief data officers, data scientists, and industry experts to examine how institutions can build the trusted data foundations needed to support AI and machine learning and rethink data strategy and infrastructure to meet the pace of innovation.
As data volumes continue to grow and technology accelerates, the annual event, hosted by A-Team Insight, heard that preparing data to be AI-ready is no longer optional but a business priority for financial institutions seeking to innovate responsibly and stay resilient.
Speakers focused on how firms can move beyond reactive checks and siloed systems to create explainable, observable, and resilient pipelines and highlighted the barriers that continue to slow progress and the risks that come with these emerging technologies without strong data foundations.
Sessions examined the role of observability tools, the use of AI to automate remediation at scale, and the potential to augment legacy datasets with predictive insights and enriched context.
“It is not enough to expand data volumes if quality and lineage are missing,” said Eugene Coakley, Senior Data Engineer at Datactics and one of the panel speakers.
“We heard how unstructured data remains a major challenge and how AI can support firms to automate remediation and surface new insights. What stood out is that strong governance is no longer an afterthought. It is being recognised as the only way to ensure AI models are reliable and transparent.”
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