The rapid adoption of artificial intelligence by financial institutions has required a heavy data management uplift as organisations have upgraded their systems to incorporate the new technology.
It has also provided greater opportunity to squeeze even more value from data by enabling its efficient deployment across enterprises.
Just how companies manage data for AI to get the most value out of it will be among of the subjects discussed and debated by experts at this year’s A-Team Group Data Management Summit London 2025.
For many, the costs of building large language models for generative AI (GenAI) have been huge, adding to the expense already incurred in implementing previous AI iterations, such as machine learning (ML).
For many the cost may be overwhelming: anecdotal evidence suggests that the initial rush to build GenAI into their tech stacks is slowing as the cost-benefits of incorporation are questioned.
Nevertheless, many have continued in their AI adoption strategies, betting that the benefits of efficient automation of workflows, the easier analysis of datasets and the faster retrieval of key data that AI can enable will pay dividends at a time of compressed fees and amid a volatile investment environment and uncertain regulatory outlook.Best Practices
At Data Management Summit London, a panel of experts will discuss how organisations can get that optimisation piece right.
The Best Practice Data Management to Sustain and Scale AI, GenAI and LLMs panel will bring together Ashly Joseph, Data Management Lead at J.P Morgan; Paul Barker, Head of Data and Analytics Governance at HSBC UK and Europe; Jean-Paul Otte, EMEA Data Strategy at ****Precisely; and Mats Stellwall, AI and ML field CTO at Snowflake. The discussion will be moderated by Element 22 partner Mark Davies.
“Over the past two years, organisations have moved from proof-of-concepts and ‘art of the possible’ to real implementations. This year, however, we will likely see many companies facing growing pressure to demonstrate tangible returns on their AI investments,” Snowflake’s Stellwall told Data Management Insight.
“At the same time there are increasing requirements for transparency, accountability and explainability that will add to the challenges for organisations. By aligning the data strategy with the AI strategy organisations will be more equipped to manage both parts.”
Data Demands
AI’s voracious appetite for data has already become one of the frontlines in the battle to get a return on investment in the technology. By optimising the management and governance of the huge volumes of data that are entering firms’ systems, not only can they reduce the total cost of their data, they can also maximise its utility and value.
The scaling of AI integration is among the toughest nuts to crack for data managers and the topic is likely to draw keen debate. Vendors have recognised this and some are offering services that enable the outsourcing of those processes so that institutions can take advantage of the processing and storage heft that specialists offer.
That, however, begs the question of how much control organisations should hand over to third parties as they build out their AI infrastructure.
To get to the essence of the challenge and provide insight into proven and potential solutions, the speakers will touch on topics that will include:
- The objectives of using AI, GenAI and LLMs
- How firms should manage the challenges of data quality, volume and availability needed to scale and sustain AI
- How firms should sustain data transparency, governance and controls as AI is scaled to ensure a proactive approach to responsible and transparent AI
- The practical requirements for scaling AI infrastructure and whether organisations should build or buy their AI models and tooling
- How firms should keep cloud costs and risks under control for AI
- How roles in data management will change and what the right resources, skills and talent to meet the demands of AI might be.
Defence to Offence
Making data a core part of an organisation’s operations is essential to mining the full value of that information, Lewis Reeder, Senior Director, Enterprise Insights at BNY will say in the Summit’s opening Practitioner Innovation Keynote address.
In conversation with Mridula Mutharaju, Data & Analytics Lead at NatWest, Reeder will press the case for data being regarded as a crucial value-creating element of all organisations.
From regulatory data to ESG data, Reeder will say that even if information has been absorbed into organisations’ systems for specific purposes, that data can be recycled and applied to other business cases across the enterprise.
In his keynote, which is entitled From Defence to Offence – Leveraging Data Management and AI for Profit and Revenue Generation, Reeder will also stress that organisations should prioritise data management based on business case needs. Further, he will touch on the challenges of aligning with the business and applying EDM capabilities and discuss how GenAI can be applied for business value creation?
- A-Team Group Data Management Summit London 2025 will be held on March 20. To see the full agenda, click here or register below.
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