The future of data management is in play with complementary technologies promising to empower, streamline and reduce costs of financial institutions’ data processes.
To get the latest thinking on the products and services that are likely to shape the way data is managed in years to come, A-Team Group invited market leaders to offer insights into their thinking at Data Management Summit New York City.
Predictably, artificial intelligence figured heavily in speakers’ thoughts. Beyond the technology, however, the five guests articulated a series of compelling conceptual and practical observations on the opportunities and challenges that await the industry.
BNY Strategy
The ball was set in motion by Eric Hirschhorn, chief data officer at BNY, who explained how the US bank had built a foundational data governance strategy that placed great importance on actionable policies and solutions-focused platforms.At the heart of the project, BNY sought to ensure that its professionals would have access to accurate data because AI models play a large role in bank processes. AI has helped in the centralisation of BNY’s datasets, a strategy that was adopted to vouchsafe trust in its highly valuable information and to enable easier distribution to users.
In this way BNY has been able to source hundreds of investment and operational ideas from across the enterprise and used AI to boil those down to a few dozen that can be actioned to the benefit of the business.
For BNY, AI has brought data efficiencies, it has reduced risk through anomaly detection and other similar guardrail applications and it has been utilised to capitalise on the mine of insights contained in unstructured forms, Hirschhorn said.
All of this is overseen by a policy and governance framework that is burnished by a Data usage Review Board and the scrutiny of chief legal, privacy and ethics officers.
Claire GPT Assistance
While BNY has an established data management framework, many institutions are still tooling theirs and for most generative AI (GenAI) will feature prominently. In a delivery entitled “Transforming data management and data governance with GenAI and large language models (LLMs)” Peter Ku, chief industry strategist, banking, capital markets and financial services at Informatica explained the possibilities that the new addition to the AI canon can offer.
The US-based company’s LLM-driven Claire GPT service, he explained, has been designed to help build some of the most critical parts of a data management setup at scale, including pipelines and data quality rules.
Claire GPT’s capabilities, which also include data discovery and metadata interpretation, are also offering a solutions to the chronic shortage of data skills, reducing organisations’ reliance on costly consultation services, Ku said.
TurinTech AI Code Optimisation
Institutions’ digital capabilities are only as good as the codes on which they are built. But what if that coding itself is inefficient?
That is a question that TurinTech AI founder and chief executive Leslie Kanthan set out to solve with the UK company’s GenAI-driven code optimisation tool.
Kanthan’s address, entitled “Accelerating financial software runtime using GenAI-powered code optimisation tools”, set out how TurinTech AI is helping companies to address what he calls a growing technical debt within companies – inefficiencies born of legacy tech stacks and a shortage of resources.
This debt has created a situation in which organisations are using more energy and resources than necessary to run their systems because tight development deadlines has meant software has been written without close enough attention to performance, security and efficiency.
TurinTech AI deploys GenAI to scour huge swathes of coding and identify where improvements can be made. Its Artemis solution will then devise alternative phrases that can be tested and validated before being offered for implementation. In this way, Kanthan said, companies can improve software performance by 30 per cent.
Modernisation with Arcesium
AI is but part of a holistic approach to modern data management that institutions must adopt to maintain an edge. In his keynote, Arcesium head of institutional asset management Mahesh Narayan explained that legacy tech stacks were mitigating against this as companies sought new digital solutions to enable them to thrive in a changing investment landscape.
Support for new asset classes, faster and more efficient processing are driving asset managers to help reduce the total cost of ownership of their data, Narayan said.
Modern financial data platforms such as Arcesium’s cloud-native Aquata offer that, he said, by integrating data and decision-making tools so that customers can ingest, validate, standardise, analyse and distribute data across portfolios and investors.
Stonebranch Streamlining
The final keynote also dwelled on process automation, with Stonebranch vice president of solution management Nils Buer explaining how his company has developed a platform that can monitor and optimise companies’ full data pipelines.
As the journey that data takes through systems becomes more complex, the need to have granular visibility into the effectiveness of the tools that carry it along has become crucial, Buer explained.
Stonebranch’s platform provides a centralised view of customers’ pipelines by sitting over existing systems and identifying the root cause of issues before establishing protocols to provide ongoing remedial action. All the while, the platform provides an auditable stream of performance metrics, he said.
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